An Overview of the Latest Research in Software Radio

An Overview of the Latest Research in Software Radio
Dr. Jeffrey H. Reed Bradley Dept. of Electrical and Computer Engineering Virginia Tech (540) Dr. Jeff Smith Mercury Computer

Acknowledgements Virginia Tech Current and Recent Sponsors and Affiliates in SDR and Smart Antennas Analog Devices ARO Booz-Allen Hamilton CIA DARPA DRS Technologies DRT General Dynamics Huawei Magnolia Broadband Mercury Computer MPRG Affiliates ONR NSF Motorola Qualcomm SBC Laboratories SAIC Samsung Tektronix Texas Instruments VA Tech Bradley Foundation VA Tech and Other Researchers* Dr. Annamalai Annamalai Dr. Brian Agee Dr. Charles Bostian Dr. Michael Buehrer Dr. Seungwon Choi * Dr. Luiz DaSilva, Dr. Carl Dietrich (assembled presentation) Dr. Steve Ellingston Dr. Robert Gilles Dr. Dong Ha Dr. James Hicks Dr. Allen MacKenzi Dr. Raqib Mostafa Dr. David Murotake * Dr. Jeff Reed,

Acknowledgements (the guys who really did the work)
Students Carlos Aguayo, James Hicks, Ramesh Chembil Palat, Jong-Han Kim, Youping Zhao, Jody Neel, Albrecht Fehske, Ramakant Komali, Rekha Menon,Vivek Srivastava, Kevin Lau, Samir Ginde, Tom Rondeau, Bin Le, David Maldonado, Philip Balister, Tom Tsou, Chris Anderson, Jina Kim, Lizdabel Morales, Michael Hoseman, kyouwoong Kim, Craig Neely, Christopher Vander Valk, Shereef Sayed And many others….

Overview of Presentation
Broad Overview of Research Needed and Key Developing Technologies Key Enabling Technologies Needing Further Development Some Developments Worth Noting Example Research Results from Virginia Tech Smart antennas realization Reconfigurable computing New open source SCA core framework developed and supported at Virginia Tech Power optimizing software radio framework Smart antenna API efforts Development of game theory to analyze cognitive radio networks Cooperative radios Quantifying networking performance of smart antennas implemented with software radio Concluding Thoughts about the Future

A Disclaimer and Request
There are many more research areas and excellent researchers doing SDR work not mentioned. Subsampled research presented here is indicative of what we tend to be more familiar with. (Presentation will be a “mile wide and an inch deep.” This presentation will be subsampled due to time, but feel free to discuss these issues with me during the conference Feel free to volunteer information about useful research not mentioned here.

Key Enabling Technologies Needed for SDR

Key Enabling Technologies 1/3
Antenna Technologies Multi-band/multimode antenna technology Antenna Switching: efficient switching techniques to optimize use of all platform antennas; freedom from one-radio/one-antenna paradigm Co-Site and Electromagnetic Interference Mitigation Radio Circuits and Power Amplifiers Develop Same Flexibility in Power Amplifiers Antenna-amplifier Arrays Flexible RF and predistortion Efficiency Software-base Communications Capabilities Portable Waveforms Integrations of FPGAs/DSPs/GPPs/CCM Rapid prototyping Non-waveform Specific Services Middleware Enhancements

Key Enabling Technologies 2/3
Network/Spectrum management Totally automated mobile network and spectrum management using adaptive, self-forming, self-healing approaches Shared Use Spectrum MANET Services Network Management Transceivers Advanced technology to combine transceiver channels into a multi-channel module Power and Cooling Power reduction/management technologies for hand-held and other small form factor sets Cooling technologies to permit high density electronic component use High capacity battery technology

Key Areas 3/3 Security/Information Assurance (IA)
Multi-Level Security Architectures Combination of INFOSEC and Key Management functions into a single element; advanced Cryptographic solutions to integrate these functions in a single chip IA Methodologies and applications and Internet Protocol Encryption (HAIPE) High-data-rate Encryption High Speed and Low Power Processors and Digital Components Faster Processing for High Speed Waveforms and Cryptography Custom Computing Machines Low Power FPGAs Superconducting Components Use of Common Components/Modules Across Platforms

Software Radio Software Research Topics
Overview of internal research topics to promote info sharing and discussion of common interests: Dynamic resource utilization Improved test Security Waveform building blocks Components for heterogeneous programming Research to increase levels of commerciality DIF

1. Dynamic Resource Utilization
Deployment and Configuration of Components update Incremental planning pattern for deployment on the fly Query/acknowledge to identify and quantify resources on shared nodes Existence proof of COTS SCA adaptation High availability persistent waveforms Partially reconfigurable heterogeneous components

Dynamic Resource Utilization for Increased Reprogrammability
Today’s Technology The Future… Cluster A Waveform/ Modem Waveform/ Prog. Modem Waveform/ Prog. Receiver Waveform/ Channel Reconfig Waveforms Within Fabric Up/down Conversion within Fabric Waveform Air prog. Static Config & Plan Dynamic Deployment, Plan & Reconfig

2. Improved SDR Validation
Secure test (see next page) Improved JTAP for behavioral portability System and complex waveform test Co-site/channel interference Scaleable to 35GHz wideband waveforms Scaleable to multiple waveforms …

3. Security-related research
Resolution of NSA/JTRS Security Supplement update and version community is working towards Completion of OMG/SBC Security spec suite and resolution with JTRS plans The form of future JTeL secure API and waveform test Novel “hacking” protection schemes Unification of MILS/programmable crypto and OTS fabric/backplane solutions “Softer”, more flexible crypto approaches

OMG/SBC Security Suite

4. “Softer” Waveform Specification and (Re)Generation
Characteristics can be realized in 7 OSI layers Layers of characteristic realized as SCA components Layer parts gathered from multiple waveforms & reused to compose a waveform protocol stack Layering of components transparent to the SCA CF Deal with only the Waveform channel but extendable to info proc and IO channels J. Smith, J. Kulp, M. Bicer, T. Demirbilek , "SDR – "Do You Care to Buy the Softest?", Mobile Communications and Military Transformation, March 2003, Washington, DC. Reuse layer parts gathered from multiple waveforms Classify/standardize parts of parts of waveforms (see Jean Belzile, Protocol translator between 3-d cells Reuse physical interfaces to multiple freqs From École de technologie supérieure, Jean Belzile Info proc channels IO channels Waveform channels

5. Components for Heterogeneous Processing
Treat FPGA/DSP as GPP-based SCA component Component portability implementation/existence proof – SCA 3.1 Resolution of above with extensions of SCA 3.0 Addressing portability in the face of SoC and highly integrated ASICs Exploitation of larger granule waveform HW Hierarchical waveform design (see #4) exploitation

6. Research to Increase Level of Commercialization (and reduce test)
Applicability of defense waveforms to defense requirements Incorporation/unification with OBSAI, CPRI and 3GPP, 3GPP2 framework, modeling, security, component and network standards Research to increase level of COTS Improved CORBA Zero-copy, high performance transport, streaming support, LwCCM, data-parallel, … COTS component model underlying SCA component model LwCCM, Compare, DANCE, Component Portability spec, … D&C common denominator between platforms, frameworks and tool providers

COTS Component Shared-Radio Infrastructure
HW Platform Domain specific framework Applications Current view of many defense non-radio applications HW Platform D&C layer Lighter-weight* Next Generation DS Infrastructure SCA CF SDR and non-SDR Applications (incl. SCA compliant ones) SCA-friendly architecture coexistence with multiple domains - multimission

7. Improved Modeling and Simulation
Requirements Waveform Specification Waveform Implementation Other Alternatives Formally Validatable Automated Process Portable behavioral and HW models/test EUML, unification of signal flow and UML CASE tools, … Composable waveform parts MDA approach for SDR specs HW abstraction Compare OMG/SDRF/JTRS unification

8. Expand Interface Portability into Digital IF Realm
Tuner Transmitter ADC Exciter DAC Receiver Digital Processing Tx DATA + Control (b) Control, Status (e) Control, Status (a) Synch Synthesizer Digital IF Interfaces Rx DATA + Status (d) (c) Standard interface and data fusion for high bandwidth streams Unification of Vita 49 and upcoming OMG submittal Anticipate WB Digital trend

Noteworthy Research Results (non-Virginia Tech)
RF and Antennas – Dumb and Smart Processor Technology Software Adaptive Networks Cognitive Radio

Reconfigurable Antennas
Antennas are usually fixed for specific bandwidth and carrier frequency  Needs Reconfigurable Antenna for flexibility Reconfigurable Antennas Multiple antenna-RF chain : Simple but Large Form Factor Single wideband antenna-RF chain : Fail to provide adequate performance due to its low-Q design Reconfigurable antenna-RF chain with MEMS 1. MEMS : Microelectriomechanical Systems

RF and Antennas – Dumb and Smart

MEMS Designs for RF Front Ends
E-tenna’s Reconfigurable Antenna Tunable antenna with narrow fixed bandwidth Patch antenna connected by RF switches Idealized MEMs RF Front-end for a Software Radio Use MEMS filter banks to create tunable RF filters J.H. Reed, Software Radio: A Modern Approach to Radio Design, Prentice-Hall 2002.

MEMS for Reconfigurable Antennas and RF - I
Advantages of MEMS Low phase noise Voltage Controlled Oscillators (VCO) by using MEMS-based high Q resonators Wideband varactors and phase shifters by using MEMS-based variable capacitors and switch-capacitor networks Tunable filters by employing MEMS-based variable reactive elements and switches 1. MEMS provides nearly lossless switching and high-Q filter. Reduces interference levels early in the RF chain.

Reconfigurable Antennas with MEMS - II
Reconfiguring Antenna with MEMS (A) (B) Application of MEMS switch for reconfigurable antenna f_ant = fc (B) f_ant = 2*fc Harris is doing some of this work.

Antenna Array - I Antenna Array Processing
Antenna array processing can achieve higher data rate and more capacity Allows diversity techniques, MIMO, Distributed MIMO and beamforming algorithms  Smart Antenna Software radio and Smart antennas complement each other well SDR provides the flexibility needed for effective smart antenna, and smart antennas provide the benefits that motivate the adoption of SDR

Antenna Array - II Switching waveforms in Adaptive beamforming
Switching waveforms in “SDR” adaptive beamforming requires significant dataflow changes  arise interconnection problem  Increase complexity  switched fabric CAN solve problems In the case of adaptive beam forming, SCA Technica Inc., observed that transition of the waveform from FDMA to TDMA to CDMA resulted in significant dataflow changes. Data flow and functional processing for smart antenna systems used with different waveforms, such as Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), and Code Division Multiple Access (CDMA), differ from one waveform type to another. Adaptive antenna systems used with software-defined radio (SDR) mobile terminals and base stations must be able to reconfigure not only their waveform processing, but also their functional processing data flow. Switched fabrics using crossbar switches are ideal for real-time hardware implementation of such systems. Data flow implementation of a subtractive co-site interference management systems is typically of complexity [i x j] for a system with i transmitters and j receivers. Use of a single 8-port crossbar switch (right) can reduce the “fan-out” to complexity [i + j], reducing both pin and wire counts [Source:SCA Technica Inc]

RF and Analog - I Superconducting components for SDR
Enable wide BW, high linearity, and high dynamic range Microelectronic device can operate up to 100GHz  Permit direct AD/DA conversion at RF level  Increase flexibility by putting whole system under SW control Possible RF level predistortion  Remove delays in Mod/Demod to use BB level predistortion  Fast enough to correct instantaneous fluctuation Superconducting circuits require operation at cryogenic temperatures, typically at 4 K (-269 °C)  Need Cryogenic Cooler (CryCooler)  Now Crycooler commercially available Much of this effort by Hypress, Inc. Source: Deepnarayan Gupta , “BENEFITS OF SUPERCONDUCTOR DIGITAL-RF TRANSCEIVER TECHNOLOGY TO FUTURE WIRELESS SYSTEMS”. HYPRES Inc, 2002 Elie K. Track, Jack Rosa, and Deep Gupta, HIGHLY COMPACT & EFFICIENT JTRS RADIOS USING SUPERCONDUCTOR MICROELECTRONICS – A QUANTUM LEAP IN PERFORMANCE: THE CRYOPACKAGE, HYPRES Inc, 2003

RF and Analog - II Amplifiers
SDR has to support multi-mode radio subsystem.  If the radio subsystem is common for various radio standards, then SDR will be more simplified. Polar modulator has almost same structure for FDMA, CDMA, and TDMA  Possible single transmitter for various modulations  Adequate for SDR Exploits AM-AM and AM-PM characteristics of PA Converts quadrature into polar signal using CORDIC algorithm (COrdinate Rotation DIgital Computer) Apply Separated predistortion for AM and PM  Go through Amplitude modulation and Phase modulation  They are recombined before PA Several different wireless standards of different bandwidths and modulations on a single amplifier while achieving acceptable performance. Source : Earl McCune, “SDR RADIO SUBSYSTEMS USING POLAR MODULATION” . Tropian Inc, 2002 Unlike traditional power amplifiers, the amplifier separates the transmitted signal into phase and amplitude components before recombining the signals. As a result of this approach, the author was able implementing several different wireless standards of different bandwidths and modulations on a single amplifier while achieving acceptable performance.

Simplified Polar Modulator Block Diagram
RF and Analog - II The Data is passed through a pulse shaping filter that generates the I and Q signals. The I and Q information is then converted to polar R and θ using a CRORDIC algorithm. The amplitude and phase values go through piecewise linear pre-distortion of the R and θ values to compensate for power amplifier non-linearity. The relative timing of the R and θ paths can also be individually adjusted. The amplitude path goes through a multiplying DAC with the multiplication value set for the power level. This DAC output then goes through a power driver to modulate an external Power Amplifier by modulation of the voltage on the final stage of the Power Amplifier. A second DAC is used to set the power level and modulate the earlier PA stages to minimize feed through from those stages. The phase path goes to a phase accurate frequency modulator using a VCO at the transmit frequency. The VCO frequency is stabilized with a mostly digital frequency servo type of loop with a parallel feed forward path. In this type of transmitter the final modulated signal only exists at the output of the PA Simplified Polar Modulator Block Diagram [Source : Wendell B. Sander, Stephan V. Schell, Brian L. Sander, “Polar Modulator for Multi-mode Cell Phones”. Tropian, Inc.]

RF and Analog - III (A) (B)
polar Mod. VCO RF buffer C-PA duplexer to RX I Q reference (A) (B) The polar transmitter operates separately on the phase modulation (PM) and envelope or amplitude modulation (AM) components of the signal. Without any envelope variation on the PM path, these RF circuits have no linearity requirements. This includes the power stage, which operates in compression at all times (C-PA). Envelope variations are imposed at the final output power (in the power stage) using gain variation techniques. It is essential that the relative timing between the PM and AM components be accurate to maintain signal quality. With the output stage operated in compression, noise figure effects do not set the wideband output noise floor. Rather, it is observed that VCO phase noise dominates far-off noise from the main signal. A) A polar transmitter block diagram for half-duplexsystems (e.g. GSM, TDMA) radio systems B) A polar transmitter design for full duplex radio systems, with extended PA dynamic range for CDMA operation [Source : Earl McCune , “SDR RADIO SUBSYSTEMS USING POLAR MODULATION”. Tropian Inc.]

Techniques for Improving RF Device - I
Zero-IF and Near-Zero-IF Quadrature Receivers Zero IF : Achieves low cost and small size solutions Near Zero IF : can avoid the DC offset and mixer self-mixing problems while achieve high integration level and low cost implementation They are suitable to multi-band/multi-mode communications devices and are favorable options for a practical implementation of Software Defined Radio. Problem of ZIF and NZIF Problem : Sensitive IQ imbalance due to many amplification and filtering stages in both I and Q IQ balancing technique is required  With the IQB technology, ZIF and NZIF quadrature receivers will no longer suffer from I-Q imbalance  become more favorable and feasible solutions to low-cost radio front-end for SDR 1. For ZIF and NZIF receivers, there are many amplification and filtering stages in both I and Q channels to meet sensitivity and interference performance, and, hence, the I-Q imbalance problem may become much more severe than any other radio architecture. Moreover, for an NZIF receiver, I-Q mismatch or imbalance will lead to poor adjacent channel interference or image rejection and cross-talks between the wanted signal and the adjacent channel signal/interference in the image band of the wanted signal

Techniques for Improving RF Device - II
Central Processing / Remote RF Developments in optical technology allow the conversion of RF signals to light and their transport via fiber optics with very low loss and distortion, and by the expansion of fiber networks in urban areas.  Called RF-on-Fiber (RoF) technology. Provides significant flexibility in upgrading functionality, and implementing various wireless standards and air interfaces  Consistent with goal of SDR approach The deployment of fiber-optics backhaul in most cities, combined with the technology of transporting analog RF on fiber, has the potential of providing an alternative to the current cellular network architecture, at higher flexibility and lower cost. Centralized processing, combined with low-cost remote RF units, open a new realm of design and implementation possibilities for multi-band/multi-mode applications, consistent with SDR goals.

Techniques for Improving RF Device - RoF
Typical RoF Link [Source: Emanuel Kahana, Mike Baker, Alek Tziortzis , “CENTRAL PROCESSING / REMOTE RF” FOR CELLULAR NETWORKS, USING OPTICAL MICROCELLS: CONCEPT AND PERFORMANCE”. Motorola]

Processor Technology

Objective Look at existing reconfigurable architectures for wireless communication (mobile and base station) Discuss advantages and disadvantages of each architecture Draw conclusions to aid RM design Architectures reviewed: PactXPP Quicksilver Morphosys Elixent Intel Montium (University of Twente) DRAW (Ohio Univeristy)

Different Types of Reconfigurable Designs
Reconfigurable logic: Variable logic and variable data routing Eg. combination of CLBs in FPGA Reconfigurable datapath: fixed logic and variable data routing Eg. MUXs and Registers Reconfigurable arithmetic: Limited choice of arithmetic operations and fixed data routing Eg. ADD, SUB, ACC, as in CPU design Reconfigurable control: variable control signals, limited choice of data routing Eg. Instruction decoder and datapath controller

Characteristics of reconfigurable architectures
Reconfigurable Fabric Datapath Reconfiguration Control Datapath Data Flow PactXPP Dynamic From MPU Configurable Buffer Quicksilver Decentralized MorphoSys From a MPU Intel N.A. Reconfigurable fixed Elixent Montium Stream DRAW Hierarchical Stallion Packet based

Guidelines for Reconfigurable Modem (RM) design (1/3)
Reconfiguration: Datapath based Static reconfiguration datapath does not change at run time RF works like an ASIC with hardwired interconnection RM design will require large amount of computational resources Less flexibility Dynamic reconfiguration datapath changes during run time More resource reuse Control and data routing become complex More operations in temporal domain results in higher flexibility Dynamic reconfiguration more suitable for RM mobile solution

Guidelines for RM design (2/3)
Processing Element structures Chip rate Ops ALU based PE designs not efficient with respect to speed, power, resource utilization and cost Viterbi / Turbo for data application Need special units for ACS and traceback operating at higher frequency Fine grain increases flexibility Efficient logic implementation Routing and control difficult More reconfiguration time Coarse grain: use ALUs routing and control easier lesser flexibility Requires more power RM design will need a combination of fine grain and coarse grain PEs

Guidelines for RM design (3/3)
Control: FSM node based decentralized Routing width proportional to complexity and flexibility Compiler design can become complex DSP / CPU based Generation of control signals easy Can execute some generic code on DSP Data transfer: Using buffer eases multiple clock requirements Can have multiple modules running at a higher synchronous clock rate with variable buffer depth Suitable for multiple data rate systems

Software

CRC SCA Implementation
CRC (Communications Research Centre) Technical scope SCA v 2.1 (2.2 under SDR Forum sponsorship) Java 60000 LOC, 300 pages of documentation Highly successful reference implementation 7000+ downloads (2003) hits on web site (2003) Check it out

Creating a Lightweight SCA
SCA is a widely accepted architectural framework that is rapidly evolving with each new version SCA is comprehensive and includes all the operating domains of SDRs  Problem where resources are limited in mobile devices Lightweight implementations of the SCA is required Since resources are limited in mobile devices, there has been a concerted effort to derive lightweight implementations of the SCA that maintain the critical functions of the modular object oriented framework, but with a reduced footprint.

Lightweight SCA Focuses on two primary aspects
Lightweight services Logging, Naming, Event Lightweight CCM (CORBA component model) Other approaches have been suggested Transform application metadata for COTS deployment (Mercury Computer)

Lightweight SCA Concerns
Unclear what the relative benefits of such approaches will be Reduced memory footprint Attempt to minimize HW aspect that is consistently less pressing Reduce computational load Relevant only for waveform transitions Affects boot-up and load times Is power consumption a better metric for determining “lightweight”?

Software Download - I Over-the-air-programming: SDR system can reconfigure itself by downloading applications and protocols Security of downloaded SW The security of downloading SW is key issue Standard download procedures: initiation mutual authentication  capability exchange  testing  non-repudiation exchange Download channel issues Download channel can increase interference level  Problem in interference limited system Common Channel : Broadcasting channel  Good for group of mobile  Fast power control is not possible Dedicated Channel: Good for fewer mobile  Fast power control is possible Status of SDR Forum efforts 1.The compatibility between the terminals and the different Radio Access Technologies (RAT) can be realized through terminal reconfiguration. This will involve the download of core software and applications that are not already on the terminal. Software download (SD) can range from simple parameters to actual executable program code 2.Non-repudation : For e-Commerce and other electronic transactions, including ATMs (cash machines), all parties to a transaction must be confident that the transaction is secure; that the parties are who they say they are (authentication), and that the transaction is verified as final. Systems must ensure that a party cannot subsequently repudiate (reject) a transaction. To protect and ensure digital trust, the parties to such systems may employ Digital Signatures, which will not only validate the sender, but will also 'time stamp' the transaction, so it cannot be claimed subsequently that the transaction was not authorized or not valid etc

Software Download - II [Source: General Dynamics Decision Systems]

Software Download - III
IW attack : Information Warfare Attack [Source: General Dynamics Decision Systems]

Adaptive Networks

Adaptive Networks - I The capability to alter its behavior is one of the attractive features of software radio  possible to adapt networks to changing conditions in a way that optimized performance Requires two steps for realization Development integration of link adaptive algorithms Development network adaptive algorithms DARPA xG project A new spectrum access behavioral regime consisting of technologies that sense, characterize, and utilize spectrum opportunities in an interference-limiting manner. A new regulatory control regime consisting of methods and technologies for controlling such opportunisticspectrum access behaviors in a highly flexible, traceable manner using machine understandable policies. xG project (neXt Generation project)  increase the ability of military systems to access spectrum, and to ensure the rapid deployment and operation of new generations of weapons systems, without the extensive, frequency by frequency, system by system, coordination now required for each country in which these systems will be operated.

xG Project Physical Layer detects MAC request related XG layer  MAC request is pending  XG layers exchanges spectrum utilization  Coordinate frequency assignments  Pended MAC request is exchanged. 1. In this implementation, the XG “MAC” layer uses the native Physical layer to coordinate with other XG systems. The Physical layer is the only “XG Aware” layer, in that it must recognize that certain of the MAC requests imply action by the XG layer. This approach has the advantage that it (hopefully) allows the use of XG with not only existing MAC designs, but should be capable of supporting legacy MAC code before the transition to XG-Optimized MAC and Physical layers is made. 2. In this framework, the Physical layer detects MAC requests that have implications for the XG layer. These generate requests to the XG layer that must be processed before the pending MAC layer request can be satisfied. The XG layers are utilizing the Physical layer to dialog and exchange spectrum utilization perceptions, and then to coordinate frequency assignments for the radios in the physical network.

Adaptive Networks - II Requires extra signaling to support adaptation as shown xG Modulation Identification can eliminate extra signaling Use multi-mode PLL : All known modulation types are simultaneously demodulated with the output symbol being determined by the demodulated signal with the lowest error metric. Use all possible Viterbi decoders simultaneously, and choose maximum likelihood output symbols based on the decoding result. Polyphase channelizer: To help identification of which band a signal is present and compares received signals to a known subset of signals with differing carriers and pulse shaping. After picking out the right format, the receiver adjusts its operation appropriately.

Adaptive Networks - III
European project OverDRiVE on Dynamic Spectrum Allocation (DSA) Their basic approach : network architecture consisting of functions, entities, components and interfaces objects with DSA requirements are supported by reconfigurable functions. Functions are implemented on entities, which may reflect actual devices or be virtual entities. FSA : Fixed Spectrum Allocation RAN: Radio Access Netwrok It investigates DSA methods that do not rely on having contiguous blocks of spectrum allocated to each of the RANs sharing the spectrum, thereby making the spectrum allocations more arbitrary, and more difficult to optimise and control. Furthermore, OverDRiVE investigates the possibilities for the dynamic allocation of spectrum to be adaptive simultaneously over both time and space

Cognitive Radios

Cognitive Radios Cognitive Radio is: Key benefit Major challenges
Aware of its environment, Aware of its capabilities, Aware of its operating context. Empowered to adapt its behavior in a way that improves its performance Key benefit Can cognitive radio enable a system designer to squeeze every Hz out the spectrum  Efficient Spectrum pooling Major challenges Figuring out how to provide the radio the etiquette Translating the observations into actions How to analyze cognitive radios

Approach to provide awareness - I
Complex Organic Distributed Architecture (CODA) CODA is based on cognitive psychology work performed as part of the CAST (Configurable radio with Advanced Software Technology) project. [Source: Tereska Karran ,”Adaptation in Software Radio using a Complex Organic Distributed Architecture (CODA)”. University of Westminster, London] The CODA intelligence cycle

Approach to provide awareness - II
Cognition Ontology : specifications of conceptualizations for providing awareness for cognitive radios Provides : A means of obtaining “meaningful” information by defining a language and algorithms for handling queries like, “how many multipaths does the channel have.”

Rich Cognitive Radio set of Research Topics
Resilience to network impairments Ad-hoc routing, extreme error conditions, link blockage Incorporation of knowledge based planning with interoperable knowledge representations and situation assessment Assessment and validation of situation-aware protocols Smart agent to build waveform building blocks on fly …

Research at Virginia Tech

Example Research Results from Virginia Tech
SCA core framework Open source effort Role of DSPs Power Management Integration of testing into the framework Rapid prototyping Smart antennas Overloaded array processing Networking performance Smart antenna API Experimental MIMO systems Cooperative radios Distributed MIMO Distributed Applications Reconfigurable computing Early work in steam processing Communications oriented processors Cognitive radio networks Game theory analysis of cognitive networks Learning Techniques Test Bed

SCA Core Framework

SCA Core Framework Open Source Effort Role of DSPs Power Management
Integration of Testing into the Framework Rapid Prototyping

Open-Source SCA Implementation::Embedded(OSSIE)
Traditional wireless education focuses on aspects such as circuit design, coding theory, and DSP Graduating engineer is likely to have a limited software background This is crucial for SDR design SCA offers powerful architecture for essentials in SDR design Open Source approach adopted OSSIE can be downloaded at: (over 3800 site visits, 800 downloads to date)

SCA and Education Two principal problems for use of SCA in universities Relatively complex specifications Simple sample code is crucial to help in understanding No simple-to-use CF is available in C++ Most EE’s software background is limited to C++ These problems are shared by entities other than universities OSSIE (Open Source SCA Implementation::Embedded) was developed to address these problems

OSSIE: Development Philosophy
Target user is entry-level Electrical Engineering Master’s student Limited time available to reach reasonable level of understanding Requires relatively simple code Limited knowledge of middleware CORBA can be overwhelming Research needs require easy access to different pieces of the implementation Must be inexpensive (preferably free)

OSSIE: Implementation Overview
Attempt to follow SCA 2.2 specifications All relevant classes to support a variety of waveforms are implemented First version written for Windows XP/2000 using Visual C++ 6.0 Second version for Windows XP/2000 using Visual C or .NET and Linux TAO (The ACE ORB) CORBA Use of ACE simplifies OS portability Xerces C++ XML parser Released under Apache Software License

OSSIE: Release Structure
Two libraries comprise CF release XML parsing library Configuration file-specific parsers CF classes Implementation to some extent of all classes except Aggregate Device Core application services and non-core applications Common pieces for non-core application selected I.e.: UUID provided by constructor or from configuration file

OSSIE: Shared Libraries
Important to minimize visible code developer needs to manage Shared libraries reduce the amount of visible code Simplify the amount of code the developer needs to directly interact with Different approaches can also reduce the amount of needed program memory Leverage use of dynamic library

OSSIE: Coding Structure and Shortcuts
ORB wrapper used to reduce the amount of visible code Minimize the exposure of the developer to the CORBA interface Can be overwhelming but not directly needed Common calls are simplified Lookup Bindobj getNamingContext Approach based on the use of single static ORB

OSSIE: Limits on Implementation
Implementation based on simplicity and readability Missing pieces considered important in commercial implementations Exception handling Aggregate Device (not necessary for target implementations) Missing exception handling can be an asset Forces developer to explore CF implementation XML debugging Implementation is likely to operate in controlled environment Exception handling can be added with relatively low effort

OSSIE: Development Path
OSSIE useful in basic R&D Inside and outside University environment VT committed to open-source C++ release philosophy Download available: Several planned improvements More complete framework Advanced waveforms Research-related contributions Power management SCA 3.0 Eventual goal is to achieve JTEL certification

OSSIE OSSIE: Vision Research Results Accelerated Research Education
Industry Academy Government Improved Architectures Standards Design Techniques Tools Component Libraries (e.g., MIMO, FEC) Future Research Directions Accelerated Research, Education Accelerated Research Contributed Code Code Accelerated Research Contributed Code OSSIE Research Results Virginia Tech MPRG SDR Research Accelerated Research Networking Cognitive Radio SDR Hardware UWB MIMO Propagation Education Graduate Undergraduate

OSSIE: Acknowledgements
Largely unpaid volunteer effort by a group of dedicated graduate and undergraduate students Sources of direct or indirect funding DCI Postdoctoral Research Fellowship Office of Naval Research Science Applications International Corporation (SAIC) Tektronix Texas Instruments MPRG Affiliates Program CRC’s reference implementation

Example OSSIE Waveform
Simultaneous demodulation of 16 DRM channels One PC dedicated to high speed data acquistion Three PC's dedicated to DRM decoding All processing in software Homemade HF RF front ends Complete radio made in less then six months Cycles bound in DRM decoding, not CF

Role of DSPs -- Project Goals
Study ways of using TI DSPs, to implement light, power-efficient SDR based on the SCA Demonstrate that a SDR implemented using a multi DSP platform can utilize the great power/cost/performance characteristics of DSPs Gain insight into the compatibility of TI DSPs with the SCA architecture Identify non-framework factors that might impact these tradeoffs

Project Description Develop a Wideband Code Division Multiple Access (WCDMA) waveform for a Multiprocessor C64x platform (fine details of WCDMA not implemented) When Mercury’s new platform becomes available, the same waveform will be ported to it and its performance will be evaluated

Power Management For SDR
SDR places challenges different from classic communications system Can support waveform swapping Needs to support wide set of devices Variety of needs and states Difficult to narrow to small, well-defined set of states Requires sophisticated power control structures Applications can be more predicable than PC Possible to determine “fast enough” speed Blind throttle for the waveform may not be enough

Power Management: State Support
Advanced Configuration and Power Interface (ACPI) is the current standard for PC power management ACPI supports mesh state machine Assumes basic device states can be throttled Linear transitions (throttle) are a subset of the mesh state machine

Power Management: Problems with Mesh SM
Assumes that all transitions are fundamentally “equal” Does not take into account QoS for power management issues related with state change Example: Voltage and frequency are fundamentally linked Increased voltage will allow a higher set of frequency settings to be supported Throttle transitions based on the assumption that lowest possible voltage is supported for the desired frequency If a change in voltage incurs a higher time delay in switching state than a change in frequency, could lead to unplanned additional latencies

Power Management: Rate-Change Support in Communications
Example (802.11b): Support alternate processing speeds for different sections of received frame Benefits Minimizes required computing power Provides ability to discard frame before high-speed processing is necessary

Power Management: Rate Change and SDR
Waveform takes place of “user” in SDR Latencies associated with change of state need to be taken into account State switching needs to be in order of microseconds Millisecond-level switches may be too slow for some waveforms Ideally, should cluster state changes into transition state Example: Crusoe TM5400 automatically controls voltage and frequency settings Slow ramp in voltage for up-frequency changes followed by fast frequency change Fast down frequency change followed by slow voltage change Changes performed automatically Possible for some equipment to leave change requests up to the application Voltage regulator can have a significant impact on the transition speeds in core operating voltage May be too slow (ms+) for some waveforms

Power Management: State Machine Description
Break down state machine into slow-change states and related fast-change states Provides application with ability to change states quickly during waveform operation Also supports sleep or standby operation

Power Management: Sample Operation
Fast operation Can cycle between 500 and 700 MHz 500 MHz may be more efficient at 1.5V May choose not to transition, since change to 600 or 700 MHz expected soon Can still transition to lower powers Support significantly lower power consumption levels Same concept can apply to other devices FPGAs, ASICs, CCMs, DSPs

Power Management: Common Interface
Design of common interface will have to wait until conceptual framework is finalized Will rely on ACPI to determine appropriate interfaces Will also rely heavily on SCA 3.0 interface specifications SCA 3.0 concentrates on non-CORBA interface descriptions Challenging task Generic nature of hardware makes static definition of interfaces unlikely Will most likely require a generic structure May be able to leverage AML

Application-Level Power Management
Algorithm development Field of research currently has large number of contributions Primarily concentrating on PC-based systems ACPI/OSPM Clear from Operating Environment Power Management (OEPM) that SDR will have some unique characteristics Optimization strategies will be based on the permutations possible by conceptual framework This research venue cannot proceed until conceptual framework is complete

Power Management Summary
Some concepts in power management are fairly mature PC power management Voltage and frequency scaling Policies and algorithms Current state-of-the-art does not cover all needs of SDR Unique issues related to nature of SDR Actively developing techniques to resolve these issues

Acknowledgement This work is funded by the DCI Postdoctoral Research Fellowship and the MPRG Affiliates Program

Dr. Jeffrey H. Reed Dr. P. Max Robert
Integrating Test Equipment Into the SCA to Design and Test Software-Radios Dr. Jeffrey H. Reed Dr. P. Max Robert Carlos Aguayo Mobile and Portable Radio Research Group (MPRG) Bradley Dept. of Electrical and Computer Engineering 432 Durham Hall, MS #350 Virginia Tech Blacksburg, VA 24061 (540) Work made possible by a gift from Tektronix

Test and Validation of SDR
SDR is a relatively new technology The term SDR was coined in 1992 The first user-led field evaluation for any JTRS production representative hardware was held on Sept. 17, (Cluster 2 Handheld EOA) The same hardware platform must support multiple bands and modes JR GVR (Ground vehicular, rotary wing, TACP) (Formerly Cluster 1) will provide capability to store up to 10 waveforms Each waveform has to be tested/validated

Integrating Test Equipment
Test equipment can be leveraged to provide an integrated solution for SDR test and validation by integrating it into the SCA Provides an embedded resource to analyze and verify the correct operation of SDR Logical Software Bus via CORBA ORB & CF OS Network Stack Bus Layer Hardware Bus Waveform Component Wrapper

Advantages of Integrating Test Equipment
Allows for a modular design strategy (seamless transition from simulation to deployment) Simulated components -> Test equipment implementation -> Final version Allows better isolation of HW and SW components to pinpoint bugs and error sources Facilitates production-line validation of SDR Provides a new dimension of built-in tests by expanding the capabilities of the TestableObject interface

MPRG Approach Create wrappers around Tektronix test equipment (Arbitrary Waveform Generator & Real-Time Spectrum Analyzer) to integrate them into OSSIE LoadableDevice interface was used for the adapters Successfully integrated AWG430 & RSA3308A into a sample waveform For more details refer to:

Future Test and Validation of SDR
There is still debate about correct implementation and validation approaches for SDR As more SDR implementations start emerging, the advantages of integrating test equipment into the SCA will be more evident. In future cognitive radio scenarios, where radios are allowed to ‘learn’, having integrated test equipment will be almost a necessity

Project Hierarchical Structure
WCDMA Components at MPRG Other TI Code OSSIE New Components TEXAS INSTRUMENTS WCDMA Waveform C64 C64 C64 C64 Compare Performance Are we going to do it this way? Anil should have much of this already componentized. If we use this approach how will we get PowerPC code? Mercury’s Platform

Waveform Development is not so Easy and Rapid Prototyping Tools are Needed
Multi-domain systems, analog-digital design Wide and Multiband systems (ADCs, VCOs, Power Amplifiers) Complex filters (configurable, tunable, efficient) Increasingly complex algorithms 30-40 military standards commercial standards Code has to be developed for efficient, hybrid platforms Simple, computationally intensive algorithms => FPGAs Mathematically-intensive portion of the system => DSPs

Traditional Development Approach
The traditional design approach uses separate tools for the DSP and FPGA IDE with JTAG support for the DSP VHDL tool suite for the FPGA. Implementation is typically performed by separate engineering teams Full evaluation of the system cannot be performed until a custom prototype is built late in the process

Importance of Development Tools
Development time can be reduced by reusing simulation code to generate working code In SDR, source code often outlives platform New FPGA Architecture every months Platform for initial design may not be the same for deployment The use of the right tools can improve waveform reusability and lead to rapid prototyping and faster time-to-market

Existing Tools There are not many tools to develop SDR at this time
Mathworks’ SimulinkTM is a platform for model-based design and multidomain simulation It is integrated with MATLABTM. The leading software tool for DSP algorithm development

Simulink Features 1/2 Interactive block diagram simulation tool
Graphic, intuitive design and simulation of the system Extensive and expandable libraries of predefined blocks Fills the gap between Waveform Requirements Specification and Platform-Specific Model

Simulink Features 2/2 Simulink
C code generation using Real-Time WorkshopTM Embedded target for TI C6000 VHDL code generation for FPGA using Xilinx System GeneratorTM for DSP Companies such as Lyrtech are using this set of tools to rapid prototype their designs MATLAB Simulink RTW TI Code Composer Studio TM Hardware Platform

There is Still Work to Do
The code and components generated by these tools are not complete from an SCA stand point If SCA compliance is desired, the actual code for deployment and configuration still has to be performed manually, Even when all components are ready and functional there is still work to do with the Domain Profile (XML configuration files) Developing the Domain Profile without the appropriate tools can be a painful experience and an important source of waveform errors There are no existing tools to automatically generate profiles for SDR

By Leveraging the Appropriate Tools, It is Possible To:
Create a uniform framework for rapid prototyping SDR and exploring new algorithms and concepts Perform a seamless design from simulation to hardware realization Rapidly prototype FPGA and DSP subsystems Minimize initial and recurring cost for design system Leverage commercial tools, but with proprietary advantage

SCA Rapid Prototyping Approach
Leverage Virginia Tech’s Open Source Software Communication Architecture (SCA) Leverage Mathworks SimulinkTM Create “wrappers” around DSP and FPGA code created with Real-Time Workshop and System Generator for DSP Design and implement tools for automating XML configuration files Create tools for benchmarking implementation

SCA Rapid Prototyping Simulink MATLAB SCA Software Radio RTW
System Behavior Modeling and simulation Simulink SCA Software Radio Code Generation RTW Hardware Platform Comp x Comp y SCA Compliance “Wrappers” SCA Waveform OSSIE Composer Studio TM TI Code Automatic XML Profile Generator

Differences from traditional Development Approach
Allows early testing of the design and faster iterations Does not require expertise in the target system Lets developers pinpoint sources of error even in complex hybrid systems Gives the flexibility to vary system parameters and verify the effects produced on the system in real-time

Advantages of an SCA Rapid Prototyping Approach
Seamless transition from simulation to SCA compliant implementation Expanded lifespan of source code Increased reusability and portability of components Easier test and debugging Powerful tool to validate new algorithms and technology concepts

Rapid Prototyping Provide a structure for the integration of rapid prototyping with current SCA architecture Allow integration of additional computing HW DSP, FPGA Reduce development cycle for new systems Increase code reuse Several aspects must be resolved Simulation to development path Development tools

SCA Rapid Prototyping Benefits
Uniform framework for rapid prototyping SCA systems Support a seamless design path from simulation to hardware realization Rapidly prototype FPGA and DSP subsystems Minimize initial and recurring cost for design system Leverage commercial tools to create custom solutions

Existing Rapid Prototyping Tools
There are not many tools to develop SDR at this time MathWorks Simulink® is a platform for model-based design and multidomain simulation Integrated with MATLAB, the leading software tool for DSP algorithm development

Existing Rapid Prototyping Environment
C code generation using Real-Time Workshop® Embedded target for TI C6000 HDL code generation for FPGA using Xilinx System Generator™ for DSP Companies such as Lyrtech are using this set of tools to rapid prototype their designs MATLAB Simulink RTW TI Code Composer Studio TM Hardware Platform

SCA Rapid Prototyping Approach
Leverage MathWorks MATLAB and Simulink® Support commercial tools through software wrappers Real-Time Workshop® for DSP Xilinx System Generator™ for FPGA Design and implement tools for automated generation of XML configuration files Develop debugging and benchmark tools

SCA Rapid Prototyping Simulink MATLAB SCA Software Radio RTW
System Behavior Modeling and simulation Simulink SCA Software Radio Code Generation RTW Hardware Platform Comp x Comp y SCA Compliance “Wrappers” SCA Waveform Composer Studio TM TI Code OSSIE Automatic XML Profile Generator

Smart Antennas

Smart Antennas Overloaded array processing Networking performance
Smart antenna API Experimental Systems

Overloaded Signal Environment
M-element Array Rx. front end Overloaded Array: more signals than elements. Conventional Array Processing breaks down. Can extract signals from the environment if can exploit known signal properties. OLAP hardest when all signals are cochannel, have little excess bandwidth (e.g. narrow-band) and are near-equal power.

Overloaded Array Scenario
Example: Airborne communication node is under consideration by commercial and military organizations. Communications in Disaster Relief Scenarios Military Communications Developed Spatially Reduced Search Joint Detection (SRSJD) Algorithm capable of OLAP in twice-overloaded environments Mobile Subscribers Co-channel Interfering t Airborne communication system employing an antenna array Cellular airborne base-station or a cellular repeater Commercial TV / Radio stations Intra-system CCI desired mobile user desired mobile user Desired LOS component External CCI Interfering base stations in the case of an airborne repeater (base station-repeater link) Overloaded array Interfering base stations An example of an overloaded array scenario is the airborne scenario. A high altitude aircraft provides surveillance of ground activities or provide emergency communication services in rural areas. Many equal power signals transmitted over low-delay, low-angle spread channels are visible by the aircraft’s receiver. The detection of the desired user and co-channel interferers becomes a Multi-user detection problem.

DDFSE-IR Architecture
Constrained Length Multi-Input/Multi-Output Whitened Matched Filter (MIMO-WMF) for desired user FB-DDFSE: an iterative reduced-state sequence estimator Output sequence estimate for desired user Multi-Channel FB-DDFSE mux. Explain in order: left to right (first) top to bottom (second). Strategy: Detect signal while rejecting interference by approximating interferers as cyclostationary Gaussian Noise. DDFSE-IR is then a reduced-complexity approximation to Forney’s Maximum Likelihood Sequence Estimation (MLSE). To achieve state reduction requires a synergistic design of the WMF and the reduced-state sequence estimator. Received array snap-shot augmented by over-sampling factor: can increase the effective degree of freedom of the array.

Dr. Annamalai Annamalai
The Impact of Transmit Smart Antennas at Mobile Handset on the System Level Performances Jong-Han Kim Dr. Jeffrey H. Reed Dr. Annamalai Annamalai

Motivation Related Work Upcoming Challenges Overviews Objective
Introduction Analysis Example Conclusions Progress Status Upcoming Challenges

Motivation Research topic Why transmit smart antenna at mobile handset
The impact of the transmit smart antenna at mobile handset on the system level performances Why transmit smart antenna at mobile handset Performance of multiple access communication system is limited by co-channel interference and channel fading, which can be effectively cancelled or reduced by various smart antenna algorithms Transmit smart antennas at mobile handset is an emerging technology by low power signal processing technology and small RF components

Motivation (cont’d) Evaluation of the system level performance
Smart antenna combining with other techniques can enhance the system performance more pronouncedly The link level performance might not be directly translated into the system level performances Ex) Performances of space time coding diversity at multi-user diversity network (such as HSDPA, HDR, etc.) is worse than those of single antenna in terms of system throughput System level performance is an important decision metric in employing the smart antennas at the communication system

Related Work Current work Objectives
Analysis of the impact of transmit diversity at handset on the reverse link DS/CDMA system capacity Objectives Develop a “unified” framework for reverse link CDMA capacity estimation Investigate the effect of fade distribution, multipath diversity, basestation receive diversity, soft-handoff (macro-diversity), for transmit diversity on intercell and intracell interference statistics

Introduction Contribution Capacity estimates
Develop a comprehensive analytical model for reverse link CDMA capacity estimates Transmit diversity at mobile station (open-loop and closed-loop diversity) Spatial diversity with non-identical fading statistics Multipath fading channel with arbitrary multipath profile Different user distributions in cells Maximum transmit power constraint Soft handoff Power control (fast and slow power control) Capacity estimates Outage probability metric based on intracell and intercell interference statistics

Analysis Example Reverse link DS/CDMA cellular systems

Capacity Estimates Outage probability vs. the number of users
Rayleigh pedestrian channel A Uniform user distribution Key observation Impact of power control (fast vs. slow power control) Impact of transmit diversity at mobile handset in conjunction with receive diversity at base station (M = # of transmit antennas at mobile handset X # of receive antennas at base station) Rayleigh Pedestrian A channel

Conclusions Efficacy of transmit diversity at mobile handset on the system capacity of DS/CDMA cellular system Capacity enhancement by other-cell interference reduction in terms of mean and variance Fast power control achieves greater capacity improvement than slow power control for similar environments The relative performance gains achieved by adding higher order diversity are greater in systems employing slow power control than those achieved using fast power control

Progress Status Developed a unified framework for reverse link DS/CDMA capacity estimation, which is extending the previous works by providing unified analysis framework and quantifying the impact of transmit diversity on reverse link DS/CDMA Developed a link simulator to evaluate the performances of transmit diversity schemes Developing a system simulator to validate the result of analysis framework

Upcoming Challenges Developing the analysis frameworks and modeling methodologies for Data networks (cellular-based or mobile ad hoc network) employing the smart antennas Heterogeneous networks (such as mixed voice and data users ) equipped with the smart antennas Investigating the cross-layer optimization techniques for the smart antenna system combining with other system performance improvement techniques Suggesting the further improvement solutions Adaptive utilization method of smart antenna algorithms Modified upper layer protocols

Factors to Consider in Creating a Smart Antenna API
Jeffrey H. Reed, Ramesh Chembil Palat, Raqibul Mostafa Mobile and Portable Radio Research Group Bradley Dept. of Electrical and Computer Engineering Virginia Tech Blacksburg, VA 24060 Seungwon Choi HY-SDR Research Center Hanyang University, Seoul, Korea Secondary antenna Primary antenna

What is a Smart Antenna Definition Mechanisms
Antenna array system aided by some “smart” algorithm to combine the signals, designed to adapt to different signal environments The antenna can automatically adjust to a dynamic signal environment Mechanisms The gain of the antenna for a given direction of arrival is adjustable Take advantage of different channels for different antennas Some antennas are “smarter” than others

Smart Antenna Benefits
Base Station JAMMER Multipath Uplink Mobile Downlink Intercell Interference Signal Fading Smart Handset Co-channel (jamming) and adjacent channel interference reduction Multiple access interference reduction for capacity improvement Robustness against multipath, fading, and noise to improve coverage and range Higher spectral efficiency Reduced power consumption for the handset Lower probability of interception and detection Enhance location estimates Min. infrastructure changes in transitioning from voice to data systems

Smart Antennas in Software Radios
Software radios and smart antennas complement each other Smart antennas provide the benefits that motivate the adoption of software radios Software radios are flexible enough to support smart antenna algorithms and their system overhead

Smart Antenna Operation
Smart antenna operation possible in either direction of signal flow: Receive smart antenna Transmit antenna array Both modes share the same categories: Beamforming Diversity Space Time Adaptive Processing (STAP) Multiple Input Multiple Output (MIMO)

Smart Antenna Principle
Three Interferers Moving Interferer Moving Target 4 element linear array. Constant Modulus Algorithm working in three environments. Note gain changes as a function of angle.

Smart Antenna Implementation: System Level View
Software and hardware boundaries need to be defined Appropriate interfaces required at the boundaries Lends itself to SDR implementation

Desirable Smart Antenna API Characteristics
The various SA algorithms must be applicable to SDR-based wireless communication systems such that SA API does not confine to the evolution of communication standards and system hardware. Interface between Smart Antenna Base Station (SABS) and SDR network must operate independently of hardware. SABS should be partitioned into small modules and each of modules should interface independent of various algorithms and communication standards. Functions and capability of each module must be known to the network controller. Thus, Beam-forming module in SABS should be manageable through SDR network. Network interface should be independent of system upgrade.

Example of SDR-based Smart Antenna System Open Architecture
Application Layer Middleware Layer Physical Layer

Hardware Partitioning
SDR-based Channel Card Structure DSP Demodulator Module Beam Former Beam Former Beam Former FPGA Beam Forming Parameter Beam Forming Parameter Beam Forming Parameter Demodulator Beamformer Interface Demodulator Beamformer Interface Demodulator Beamformer Interface DPRAM DPRAM DPRAM Demodulator Controller Demodulator Controller Demodulator Controller Micro Processor 6 ANT, I&Q (6 bits/signal) Channel Card Controller DPRAM SCME Searcher DPRAM Clock & Data Buffer Other Board Interface 4 ANT (6 bits/signal) Modulator DPRAM Tx Data Buffer Modulator Module

Smart Antenna API Logical Functionality
Commands Asynchronous protocols-to-device primitives for performing immediate, typically non-persistent actions. Variables Persistent antenna state or long-term measurement primitives. Response The synchronous device response to a protocol’s command or variable operation. Signals Asynchronous device-to-protocols primitives for reporting recent, typically non-persistent events. < Interface between Network and SABS through Network protocol >

Commands Signals Commands Requirements Qualifiers Description Response
CmdBeamformerReset Mandatory Beamformer Soft Reset Beamformer Soft Reset OK. Beamformer Soft Reset Failure. CmdBeamFormerExec BeamFormer Execution on/off BeamFormer Execution OK. BeamFormer Execution Failure. CmdCalibrationExec Calibration Execution on/off Calibration Execution OK. Calibration Execution Failure. CmdBeamFormerDMExec Optional Beamformer Diagnostic monitoring on/off monitoring OK. monitoring Failure. Signals Commands Requirements Qualifiers Description SignBeamformer Mandatory Beamformer Module loaded SigBeamformerError Interrupt Indicating the Beamformer Error

Smart Antenna API: Outstanding Questions
How general can it be in practice? What is the border between the smart antenna API and the antenna API? How can it be verified? Is it possible to use CORBA transport?

Cooperative Radio

Cooperative Radios Distributed MIMO Distributed Applications
Distributed computing Distributed location estimation Distribute spectrum monitoring and control Distributed security

Distributed Cooperative Diversity for High Data Rate UAV Links (ONR) BAA 04-001
Researchers: Ramesh Chembil Palat, Dr. A. Annamalai, Dr. Jeffrey H. Reed

Ground Wireless Cluster
Distributed, Collaborative Inter-Cluster Communication with UAV Assisted Relaying Ground Wireless Cluster UAV Cluster Collaborating Node Cluster Head Command Control Improves end-to-end communication reliability Can expect very large increase in effective throughput May have significant ramifications on network layer need to investigate multiple architectural solutions using D-MIMO

Distributed MIMO: Big Picture
MIMO technology offers tremendous improvements in a point-to-point link Well acknowledged fact Can we exploit MIMO advantages in a distributed set up? Limited literature on performance and implementation issues: Requires study of architecture selection and communication strategies using distributed MIMO Requires hardware implementation using distributed MIMO set up Ramifications on higher layers not well understood: Needs investigation of higher layer performance using D-MIMO for PHY layer

Architectural Issues (1/3)
GSC EGC Rx Beamforming Relays (DF or AF) Signal Processing Signal Processing No Diversity GSC (SC& MRC) EGC Rx Beamforming Tx Beamforming Synthetic Space Time Coding

Single source-destination node with multiple relay nodes Multiple choice for schemes to select from Rate/Reliability tradeoff evaluation of each scheme: ASER/ABER Implementation complexity E.g. array calibration for BMF Vs STBC Bandwidth and/or power efficiency

Example Scenario Simple DF/AF only scheme Bandwidth efficiency low as multiple bands required Implementation complexity and relay collaboration low Uplink-GSC; Downlink SSTC ASER performance similar Bandwidth efficiency high Implementation Complexity higher (relay collaboration required) Uplink GSC; Downlink TxBMF ASER performance better Higher implementation complexity (need feedback info about channel) Form a recommendation subset for architectural tradeoffs for operational scenarios !

What happens when more than one source/destination nodes are available to collaborate? (Next phase of research) Improved architectural flexibility Scale the problem to address link imbalance issues in multi-hop networks Cross layer ramifications Source Cluster Relay cluster UAVs Destination Cluster First Hop Second Hop

Simulation Setup for UAV Based Communication
15,000 10 Km 60 Km

Assumptions about UAV Characteristics
Wing span 3-4 ft and 9-10 ft Max speed 150Km/Hr (93 Miles/Hr) ([1],[3] other references) Max range Km for UAV Navigation [1],[3] Max and Min wind speed (22-8 knots) (40.74– Km/Hr) [4] At 10 kts max lateral displacement de = 1.7 ft in 3s so at 22 kts ~ 3 ft = 1m max Due to turbulence vertical max average displacement de = 3 ft Max Transmitted Power 1-2 W (1 W considered) [2]

Assumptions for simulation
Pathloss exponent (ground to air) at VHF: 2.3 (approx worst case) [5] Land to Sea Pathloss exponent at VHF: 3 (approx) [6] Uplink Transmit power PTx = 0-30 dBm UAV Transmit power 1/L*(PTx) ( L is the number of UAVs) Average noise power level at each receiver (both UL and DL): dBm Assumed perfect collaboration between UAVs All simulations and analysis based on BPSK modulation

Simulation Result Direct link fails under fading but relay
Effect of Reduced Path loss 6 dB 8 dB Direct link fails under fading but relay scheme works even with single relay SSTC Direct link experiences Rayleigh fading UAV Relay experiences Ricean fading with LOS component Beamforming Source Transmit Power in dBm At least 35x Range extension compared to direct land to sea link even without fading 4 UAV SSTC gives 8 dB gain and BMF gives 6 dB at BER of .001 over single relay

Effect of Doppler & Displacement Error
fc = 145 MHz BW = 10 KHz T = 100us Relative velocity due to wind 40 Km/Hr Displacement due to turbulence modeled as Gaussian RV with mean .3m and variance .01 The errors transformed to phase error 5dB 7dB Displacement error decreases performance but still better than Land to Sea direct link

Distributed MIMO Summary (1/2)
At least 35x range extension using UAVs Drastic power reduction for transmit nodes Better suited for LPI/LPD Beamforming performs better at low SNRs (1-2 dB) Good candidate for LPI scenarios Moving platform degrades BMF performance but still better than direct link Lower frequencies (VHF) better resistance Higher frequencies synchronization overhead increases

Distributed MIMO Summary (2/2)
Some other observations from simulation: Fading index of weaker link dominates performance E.g. distance from UAV to ship 50 Km (weaker link) hence changes in fading index in downlink can change ABER performance SSTC performs better than BMF at high SNRs > 25 dB Does not require feedback about channel compared to BMF AT lower SNRs DF schemes give better ABER performance than AF schemes Offers operational flexibility Can apply many permutations and combination of schemes Flexibility in terms of number of UAVs used Effective in dynamic military environment Switching communication architectures

CISCO URP Project Aegis Network-based Interference Characterization and Management for WLAN Jeffrey H. Reed, Professor Brian G. Agee, Adjunct Research Professor Youping Zhao, Ph.D. Candidate, Research Assistant Mobile and Portable Radio Group (MPRG) Bradley Department of Electrical and Computer Engineering Virginia Tech, Blacksburg, VA

Cooperative Radio Perspective for 802.11 WLAN Interference Management
What is cooperative radio? In cooperative wireless communication, we are concerned with a wireless network, where the wireless agents may increase their effective quality of service (measured at the physical layer by bit error rates, block error rates, or outage probability) via cooperation. How to cooperate among WLAN Access Points for interference management? Interference is to be detected, classified, located, canceled and/or mitigated based on the collected data from multiple WLAN Access Points. What are the possible applications of Cooperative radio for WLAN? Increased interference detection rate; better location accuracy; Improved QoS and security of WLAN owing to interference detection, cancellation or mitigation. Higher throughput or larger coverage of WLAN Well, many open issues and full of challenges…

Motivations & Visions of Project Aegis
WLAN interference management is an indispensable part of the future network Wireless connectivity in the enterprise and home will occur, ready or not! 95% of corporate laptops will ship with Wi-Fi embedded by 2005 (Meta Group). WLAN uses unlicensed (virtually unmanaged) radio medium (ISM band), therefore, it must contend with disparate numbers and varieties of interferers, including but not limited to, microwave oven, cordless phones, VoWiFi phones, Radar, Bluetooth devices, adjacent networks, and many emerging devices, such as ZigBee ( ) etc The costs of WLAN maintenance keep growing up rapidly. Current WLANs typically have limited interference characterization and management capability, which creates a strong need for developing sophisticated tools to characterize and manage WLAN interference, therefore optimize the WLAN operation in terms of throughput, coverage, QoS, etc. Visions: Develop algorithms and tools for interference detection, classification and geolocation for next generation WLAN management tools Apply macro-diversity, cognitive radio, cooperative radio techniques to intelligent WLAN interference management with the capabilities, such as: network-concentric spectrum analysis, interference sensing, classification, geolocation, automatic interference diagnosis, avoidance or mitigation Contribute to ongoing IEEE standardization efforts (e.g., TG k, TG n)

Main Tasks & Challenges of Project Aegis
Interference characterization, modeling and generation focus on non interference first (e.g., Microwave oven leakage, Bluetooth) Interference detection and classification Interference emitter geolocation techniques development May need to refine (or innovate) WLAN Network Architecture & Protocol to support the implementation of the Interference Management algorithms Main challenges: WLAN operates in unlicensed shared spectrum, where various (virtually unpredictable) interference exist with disparate features Complicated indoor radio propagation scenarios make the interferer location difficult Many practical issues to be considered, such as A/D speed, dynamic range, storage limit of AP, synchronization between APs

Example: Enterprise WLAN Networking Scenario (Hospital)
Burst b clients (CCK) Out-of-network STA’s (DSS) Wireless VoWiFi, cordless/PTT phones (DSS, FHSS) Bluetooth Microwave ovens (chirp) IEEE Zigbee devices (upcoming) High-rate g OFDM streaming data

DMP Bluetooth signal ON/OFF detection illustration
20 dB Up-edge stat a Down-edge stat 18 dB SOI Bluetooth “ON” detected i SOI Bluetooth “OFF” detected 16 dB SNOI Bluetooth “OFF” detected 14 dB 12 dB Threshold_OFF Detection statistics in dB 10 dB 8 dB 6 dB 4 dB 2 dB Threshold_ON 0 dB 50 µs 100 µs 150 µs 200 µs 250 µs ON Statistic Start Time

WLAN AP Beacon Exploitation Strategy
Calibration phase Detect and identify beacon periods for each AP Collect data during beacon preamble transmission period Transfer back to central site Use beacons as pilots to calibrate clock offsets between AP’s Data collection phase (may coincide with Calibration phase) Collect data at coordinated time and frequency (simultaneous with calibration phase if possible) Interference analysis phase Resample data collects onto common clock Remove beacons if needed (simultaneous calibration/analysis) Detect interferers under beacons Geolocate interferers

Implemented Single-Site Real Data Collection System

Processor Technology Dr. Peter Athanas Dr. Jeffrey H. Reed Dr
Processor Technology Dr. Peter Athanas Dr. Jeffrey H. Reed Dr. Srikathyayani Srikanteswara James Neel

Configurable Computing
Match the programmable hardware to the application. Speed Silicon efficiency Flexibility

The Stallion – Stream Processor
IFU MESH (computational) Integer Multipliers (allocable) Programmable Data Ports “Smart” Crossbar Network Stream I/O Allocable Resources

Wormhole RTR Stream Format
Program/Flow Header Configuration information Routing information Variable size Possibly removed as stream routs Data Application data stream Possibly chained Variable size

Stallion Overview 16-bit stream based CCM IFU 60 Functional Units
4 Multipliers Process: 0.25 m Clock: 50 MHz Area: 63.2 mm2 3.3 volts Power: 0.7 W Developed as part of Virginia Tech’s GloMo effort IFU Data Port Multiplier Crossbar J. Neel, S. Srikanteswara, J. Reed, P. Athanas, “A Comparative Study of the Suitability of a Custom Computing Machine and a VLIW DSP for use in 3G Applications,” SIPS 2004.

WCDMA Mapping Stallion more efficient
Stallion Implementation Stallion more efficient Stallion requires significant hand coding (development time) Stallion Despread Mapping C6201 Implementation

Objectives of Configurable Computing for Software Radio
Identify and Evaluate “Ideal” Custom Computing Machine (CCM) architecture for handsets targeting CDMA2000 and UMTS Method for Evaluating Disparate Chip Architectures Dynamic CCM Simulator Attributes of Optimal CCM for UMTS / CDMA2000 handsets Comparative Evaluation of Developed CCM, TI 6701 DSP, and ASIC High-Level Design of Compiler for Developed CCM _

Cognitive Radio

Cognitive radio networks
Game theory analysis of cognitive networks Genetic algorithm cognitive engine Test bed

Game Theory and Software / Cognitive Radio
Researchers: Jody Neel, Luiz DaSilva, Robert Gilles, Allen MacKenzie, Jeff Reed, Annamalai Annamalai, R. Michael Buehrer, Albrecht Fehske, Ramakant Komali, Rekha Menon,Vivek Srivastava, Kevin Lau, Samir Ginde, James Hicks Sponsors: Office of Naval Research, Motorola, NSF IREAN Program, MPRG Affiliates

Analyzing Distributed Dynamic Behavior
Dynamic benefits Improved spectrum utilization Improve QoS Many decisions may have to be localized Distributed behavior Adaptations of one radio can impact adaptations of others Interactive decisions Difficult to predict performance

Games Normal Form Game Model
A game is a model (mathematical representation) of an interactive decision process. Its purpose is to create a formal framework that captures the process’s relevant information in such a way that is suitable for analysis. Different situations indicate the use of different game models. Normal Form Game Model A set of 2 or more players, N A set of actions for each player, Ai A set of utility functions, {ui}, that describe the players’ preferences over the outcome space

How a Normal Form Game Works
Player 1 Player 2 Actions Actions Decision Rules Action Space Decision Rules u1 Outcome Space u2 -1 +1 1 WINS!

Cognitive Radio Network as a Game
Actions Actions Decision Rules Action Space Decision Rules Informed by Communications Theory u1 Outcome Space u2

Key Issues in Analysis Scalability As the number of devices increases,
Steady state characterization Steady state optimality Convergence Stability Scalability a1 a2 NE1 NE2 NE3 a3 a1 a2 NE1 NE2 NE3 a1 a2 NE1 NE2 NE3 a1 a2 NE1 NE2 NE3 1. Steady State Existence Is it possible to predict behavior in the system? How many different outcomes are possible? 2. Optimality Are these outcomes desirable? Do these outcomes maximize the system target parameters? 3. Convergence How do initial conditions impact the system steady state? How long does it take to reach the steady state? 4. Stability How does system variations impact the system? Do the steady states change? Is convergence affected? 5. Scalability As the number of devices increases, How is the system impacted? Do previously optimal steady states remain optimal? Scalability As the number of devices increases, How is the system impacted? Do previously optimal steady states remain optimal? Steady State Characterization Is it possible to predict behavior in the system? How many different outcomes are possible? Optimality Are these outcomes desirable? Do these outcomes maximize the system target parameters? Convergence How do initial conditions impact the system steady state? What processes will lead to steady state conditions? How long does it take to reach the steady state? Stability How does system variations impact the system? Do the steady states change? Is convergence affected?

Ad-hoc Power Control as a Game
1 Utility function Target SINR at node of interest Player Set N Set of decision making radios Individual nodes i, j  N Actions Pi – power levels available to node i May be continuous or discrete P – power space p – power tuple (vector) pi – power level chosen by player i Nodes of interest Each node has a node or set of nodes at which it measures performance {i} the set of nodes of interest of node i. 5 5 0 2 1 3 4 2 4 3

Potential game model Existence of a potential function V such that
Identification NE properties (assuming compact spaces) NE existence: All potential games have a NE NE identification: Maximizers of V are NE Convergence Better response algorithms converge. Stability Game is stable (Lyapunov) V is a Lyapunov function Design note: If V is designed so that its maximizers are coincident with your design objective function, then NE are also optimal. A potential game is defined by the existence of a potential function that captures the information related to unilateral deviations (changes in one player’s action). When every change in sign of the utility function is reflected in an identical change in sign of the potential function, the game is said to be an ordinal potential game. Similarly every change in value of the utility function is reflected in an identical change in value of the potential function, the game is said to be an exact potential game. While there are many techniques for identifying a potential game, the easiest to understand is the second order condition wherein for all pairs of players, i,j, and for all action vectors, if the second derivative of player i’s utility function with respect to player i’s action and player j’s action is equal to the second derivative of player j’s utility function with respect to player i’s action and player j’s action, then the game is an exact potential game. After we have shown that a game is a potential game, we can quickly gain some valuable insights into the game. First, we know the game has a steady state and we can identify those steady states by identifying the maximizers of the potential function. Second, we know that better response algorithms converge. Thus as long as the nodes behave in their own self-interest, we are assured of convergent behavior. Some typical better response algorithms include gradient algorithms and genetic algorithms. Third, we know that if noise is added to the system, the system remains stable as the potential function is a Lyapunov function for the game. Thus the potential function maximizers are also Lyapunov maximizers and thus stable. As an interesting design note, suppose you want to design a distributed algorithm so that a particular objective function is maximized. If you design the algorithm so that it is a potential game with an potential function whose maximizers are coincident with your design objective function, then the steady states for the system are also optimal

Power Control Application
Two cluster ad-hoc network 11 nodes DS-SS N = 63 Path loss exponent n = 4 Power levels [-120, 20 dBm] Step size 0.1 dBm Synchronous updating Target SINR  ~ 8.4 dB Objective Function Assume  is feasible ui(SINR) i

Simulation Results (ordinal potential) Noiseless Simulation
Noisy Simulation

Interference Avoidance by Waveform Adaptation
Multiple Access Channel is considered here Different types of users reside in a network Waveform used by users might reside in different dimensions (represented by signature sequence) Shape the waveform in a way such that interference in the network is minimized Receiver (Projection of rxed signal onto signal space) User1 N 1 UserK K User2 2 Signal at Receiver Transmitted Signal

Distributed Greedy IA Game
Each user chooses sequences to increase its SINR at receiver Utility function for each user is Game has potential function given by User updates iteratively increase V(S) ~ sum capacity

Simulation Results: Greedy IA Game
Utilities of users shown to converge Potential function also converges Sequences converge to Welch Bound Equality Sequences that maximize sum capacity Users choose waveform that gives minimum interference – best response

Game Theory Applied to Cognitive Radio: Future and Ongoing Work
Application Areas Power Control Waveform Adaptation Network Formation Topology Control Node Participation Work Areas Joint adaptations Study of impact of noise on other game models

Cognitive Engine based on Genetic Algorithms
Charles Bostian Tom Rondeau, Bin Le, David Maldonado Center for Wireless Communications 466 Whittemore Hall Virginia Tech Blacksburg, VA 24061 (540) Work sponsored by NSF

Why Cognitive Radio? SDR is an enabling radio platform
Provides adaptive waveforms Cognitive radio gives autonomous intelligence to the radio to exploit the benefits of SDR Spectrum is an available resource that needs better management

Spectrum-Wide Market Needs How cognitive radio helps you
Need for spectrum Opens availability to under-used spectrum Provides better management of current spectrum use Need for service Public safety / disaster response Military and public safety coordination Need for capacity Cellular services reaching maximum capacity, want to offer more and better services without the available resources

Approach At their most basic, Cognitive Radios are:
Aware: it can sense, perceive, and collect information about its environment Intelligent: it can process and learn about the environment and its own behavior Adaptive: it can use what it knows to alter the radio’s behavior to improve communication for itself and the surrounding radios We use biologically-inspired techniques that combine machine learning with genetic and evolutionary algorithms

Biological Adaptation
Intelligent adaptation is done using genetic algorithms (GAs) Radio is modeled as a biological system where traits are defined by a chromosome Each gene of the chromosome corresponds to one adjustable parameter of the radio The GA optimizes the chromosome to provide the user with a quality of service

Cognitive Radio Vision
Awareness Adaptation Intelligence

Intelligence is key Human-like Learning
Studying cognitive sciences to form better learning methods Environment Model Radio Learn Feedback Adapt Cognitive cycle for an intelligence radio

Building Knowledge Using childhood learning theories, the radios will learn from experience and from peers. As knowledge base increases, learning time and computational complexity decreases.

Distributed Learning and Intelligence
Radios can share knowledge Improves performance Reduces computational costs Relaxes individual radio responsibilities Sharing Knowledge Cooperation Autonomous System Resource Sharing

Details of the Cognitive Engine
For details contact

Some Experimental Results
Play well with others Reduce Spectrum Occupancy Maximize Data Rate

Development of a Cognitive Radio Test-bed using Tektronix Components
Lizdabel Morales Jeffrey H. Reed Virginia Tech Bradley Dept. of Electrical and Computer Engineering Mobile and Portable Radio Research Group

Current Spectrum Situation
Problem The amount of users of wireless technologies has grown tremendously during recent years. Current available spectrum is scarce and cannot provide for growth and innovation. Solution Considered FCC and other regulatory agencies have had the task of re-allocating the scarce spectrum. (Refarming) The primary technology being considered is Cognitive Radio.

What is a Cognitive Radio?
Is a Software Defined Radio (SDR) that is aware of its environment and its capabilities, it can alter its physical layer behavior, and is capable of following complex adaptation strategies, as defined by Mitola. In other words, the radio learns from previous experiences and can adapt to new situations not planned at the radio’s initial design time.

How can Cognitive Radio improve Spectrum Utilization?
Allocate the frequency usage in a network. Assist secondary markets with frequency use, implemented by mutual agreements. Negotiate frequency use between users. Provide automated frequency coordination. Enable unlicensed users when spectrum not in use. Overcome incompatibilities among existing communication services.

Proposed Research Development of a Cognitive Radio test-bed using Tektronix off-the-shelf components and MPRG’s open source SCA and test equipment software (“wrappers”). Initially cognition abilities will comprise of identification of particular frequency bands in use. Signal identification and other capabilities will be added as research progresses.

Cognitive Radio Test-bed

Test-bed’s Main Components
Arbitrary Waveform Generator AWG430 – used to create a multi-mode transmitter. Logic Analyzer – used for signal characterization (identifying bit patterns, protocols, etc.) Real Time Spectrum Analyzer (RSA3408A) – used to perform signal demodulation. PC with MPRG’s OSSIE (Open Source SCA Implementation Embedded) platform – used to implement the cognitive engine.

Research Plan (1/2) Provide a solution to test and validate cognitive radio with Tektronix COTS. Analyze radio etiquettes developed from our game theory research. Analyze the stability and convergence of cognitive algorithms developed from our research. Test the performance of genetic learning algorithms developed at VT’s CWT group. Develop new cognitive algorithms based on Hidden Markov Models.

Research Plan (2/2) Quantify the impact of fixed versus ad-hoc cognitive infrastructures. Quantify the impact of the Interference Temperature model proposed by the FCC. Investigate combinations of modulations that do not interfere with each other. Investigate how cognitive radios can be used to improve interoperability between systems. Develop and test applications for cognitive radio technologies such as ad-hoc video conference.

Future Goals Create a cognitive radio test-bed with 2 or more nodes.
Analyze the impact of cognitive radios in networks. Develop cognitive engines using various techniques (HMM, GT, GA, etc.)

Concluding Thoughts on Research Directions in SDR

Concluding Thoughts about the Future
Software Defined Radio is really a misnomer. Probably should be called Software Defined Networks Cognitive Radio is quickly gaining momentum as an important research area Probably should be called Cognitive Radio Networks Gut-feel: Probably lot of gain with simple approaches Cognition within existing standards is possible (SDR enabled) SCA isn’t perfect, but its getting better and researchers are getting smarter COTS will be coming more viable for easily making the radio DSP is the easy part --- Flexible analog and antennas are tough SDR will go commercial, but cost MUST be the driver New applications tilt cost savings to SDR Increasing development costs are favoring SDR

An Overview of the Latest Research in Software Radio

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