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IISc-DRDO Workshop on Cognitive Radio Bangalore – March 14, 2009
Cognitive Radio - An Introduction R. David Koilpillai Department of Electrical Engineering Indian Institute of Technology Madras IISc-DRDO Workshop on Cognitive Radio Bangalore – March 14, 2009
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Evolution of Wireless …
LTE-Adv GSM GPRS WCDMA Rel. 5 (HSDPA) LTE 1xEV-DV UMB cdmaOne cdma2000 1xEV-DO IEEE d/e IEEE m MIMO- Wave2 Focus is on spectral efficiency – bits / sec / Hz
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Radio Functionality Evolution
Source: Prasad et al. IEEE Comm Magazine, April 2008
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Software Defined Radio (SDR)
J. Mitola, “The software radio architecture” IEEE Communications Magazine, May 1995
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Vanu SDR Architecture Commercial product Multistandard Flexibility
GSM / GPRS / EDGE Cdma / EV-DO Flexibility Scaleability Cost-effectiveness Ref:
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Vanu SDR Architecture Ref:
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SDR Summary Many technical challenges have been solved
SDR – now commercially viable and attractive Drivers for SDR Advances in processors, DSPs, FPGAs, … High speed, high-resolution A/D, … Multi-standard support, MIMO capability, … Efficient software tools and structures SDR: A flexible platform New technology development Technology migration Focus on basestations and not user equipment Numerous national and international initiatives Multiple SDR test beds Open-source material available SDR Forum – an active group The next step in SDR Migration towards Cognitive Radio …
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SDR Cognitive Radio
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Cognitive Radio (CR) Motivation for CR
Increasing demand for radio spectrum Broadband wireless demand is rapidly growing Current approach to spectrum allocation Fixed allocation to licensed users Existing scenario Under-utilization of spectrum Spatial and temporal “spectral holes” exist Innovative approach to improve spectrum utilization Cognitive Radio Initiated by FCC – regarding secondary usage of spectrum
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Utilization of Spectrum
Frequency range 30 MHz – 2.9 GHz Based on report by M.A. McHenry Max. utilization ~ 25% TV channels Average usage ~ 5.2 % New York City average ~ 13.1% Significant # white spaces Even in cellular bands Ref: M.A.McHenry, “NSF Spectrum Occupancy Measurements Project Summary,” August 2005 Ghasemi and Sousa, IEEE Communications Magazine, April 2008
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CR Approach Main steps in CR approach
Identify spectral bands not used by Primary User Signal sensing (to detect Primary User’s signal) Estimation of “Interference Temperature” Localised around user Spectral hole A spectral band assigned to primary user Currently unused at geographical location Should be done reliably Should be able to detect “low” level Primary User signals Utilize spectrum as “Secondary User” Increasing utilisation of radio spectrum Without causing interference to Primary User Primary user always has priority
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Today’s CR Scenario CR: Opportunistic Unlicensed Access
To temporarily unused frequency bands (across the entire licensed radio spectrum) A means to increase efficiency of spectrum usage Stringent safeguards required On-going licensed operations should not be compromised Spectrum sensing based access Unlicensed user transmits if licensed band is sensed to be free Main functionality of Cognitive Radios Ability to identify unused frequency bands Sensing must be reliable and autonomous Conclusion A perceived spectrum scarcity - due to inefficient, fixed spectrum allocation Consider radically different paradigm Secondary (unlicensed) users Opportunistic use of unused primary (licensed) band(s)
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IEEE 802.22 Project started by IEEE in Nov 2004
Charter: To develop a CR-based WRAN PHY and MAC specifications Transmission in unused TV and guard bands (54 MHz – 862 MHz) Very favourable propagation characteristics Channel BW 6 MHz (may be 7 MHz / 8 MHz in some countries) Spectrum sensing for identifying white spaces Distributed sensing FCC maintained server – info about unused channels (by geographical location Localised sensing CPE’s perform periodic measurements and send measurements to BTS BTS makes decision to use the current channel or any other alternatives Application scenarios Wireless broadband in rural / remote areas Performance comparable to today’s DSL technology Unlicensed devices lower cost and increased affordability Attractive for Wireless Internet Service Providers (WISP) TV migration : moving from broadcast to cable and satellite Broadcast TV channels available
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Comparison of Networks
WRAN Aspects Large coverage footprint Up to 100 Km Larger cells than cellular Leverage two factors Higher EIRP Attractive propgn characteristics Ideal for rural /remote services Broadband wireless access Unlicensed devices Ref: Cordeiro et al., “IEEE : The First Worldwide Wireless Standard based on Cognitive Radio,” IEEE, 2005
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IEEE 802.22 Specifications Target specifications Air interface
Spectral efficiency – 0.5 b/s/Hz – 5 b/s/Hz Average: 3 b/s/Hz 18 Mbps in 6 MHz Assuming 12 simultaneous users – 1.5 Mbps (DL) and 384 Kbps (UL) Range: 33 Km (extend to 100 Km) CPE Tx power 4W CPE Air interface Requirements – Flexibility and quick adaptibility Link adaptation based on SINR Adapt modulation and Coding option Frequency agility OFDM(A) based UL and DL Transmit Power Control : 30 dB withsteps of 1 dB Channel Bonding – Utilizing more than one TV channel System can use larger BW to support higher throughput
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IEEE MAC Medium Access Control (MAC) Design tailored for Cognitive Radio Technology Key aspect – adaptability based on dynamic changes in environment Spectrum sensing measurements Two structures Frame and Superframe Superframe will have Superframe Control Header (SCH) and preamble SCH sent by BS in every channel that is “available” Two types of spectrum measurements In-band measurements – in channel currently being used Out-of-band measurements – Other channels Two types of sensing Fast sensing - < 1 msec per channel Performed by CPE and BS - For quick information gathering Fine sensing – up to 25 msec per channel Verification / validation of measurements Deal with large propagation delay (roundtrip delay up to 300 microsec) MAC deals with a number of issues not addressed in traditional systems
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Cognitive Radio = Sense + Learn + Adapt + Use
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Spectrum Sensing
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Methods of Spectrum Sensing
Energy Detector Correlation-based detector Cyclostationarity-based detector Hybrid Detector Performance of spectrum sensing Sensing Criteria (Regulatory aspects) Sensing Period Detection Sensitivity
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Spectrum Sensing Optimum receiver Alternative – Energy Detector
If structure of primary signal known Optimum (in AWGN): Matched Filter (MF) followed by Threshold Can be implemented for a few specific primary signals (selected bands) Not practical for large # of primary users Need for coherent detector for each transmitted signal Alternative – Energy Detector Measures energy of signal in primary band Compare with properly set threshold Declare presence of “white spaces” primary user absent Requires longer sensing time to achieve desired level of performanc e Low computational complexity Ease of implementation ED - An attractive candidate for Cognitive Radio Drawbacks of ED Cannot discriminate between sources of input energy (signal vs. noise) Uncertainty of noise floor will degrade performance Especially at low SNR ED can be effectively combined with more robust detectors – “Hybrid Detectors”
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Spectral Sensing Binary hypothesis testing problem
Decision statistic (Energy detector) When signal absent, Δ is Central Chi-Square Variable with N degrees of freedom When signal present, non-Central Chi-Square Variable
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Energy Detector Decision statistic If N large, invoke CLT
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Spectral Sensing Performance (1)
Performance of Energy Detector is validated against analytical performance In AWGN, ED achieves good performance at very low SNRs ~ -8 dB Achieves low probability of false alarm Evaluated for frequency selective fading channels also
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Spectral Sensing Performance (2)
AWGN, Effect of sensing Period Performance in fading Robustness of energy detector enhanced if longer sensing period is used Performance in fading is poorer than in AWGN (as expected) Noise uncertainty causes major degradation in performance Energy detector not suited as a stand-alone detector
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Spectrum Sensing Summary
Many methods available Properties utilised: Energy, Correlation, Cyclostationarity Computational complexity and estimation time are important factors Searching over a vast frequency range Focus on robustness (at low SNR) and reliability Minimize probability of missed detection To avoid interference to primary user Uncertainties regarding measurement Noise and interference environment Strong motivation for Hybrid Detectors Sensing Criteria (Regulatory aspects) Sensing Period Detection Sensitivity
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Regulatory Constraints
Satisfactory protection of primary user from harmful interference Essential for realization of opportunistic spectrum access Regulatory constraints Sensing Periodicity (Tp) Period with which UL user must check for presence of primary user Detection Sensitivity Signal level at which the UL user must detect primary user reliably Sensing Period (Tp) Max. time (delay) UL user unaware of reappearance of primary user Max. duration of harmful interference Determines QoS degradation of primary user Delay of primary user in accessing channel Depends on type of primary user service – delay sensitivity Must be set by regulator for each licensed band
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Detection Sensitivity
Ref: Ghasemi et al., IEEE Communications Mag, April 2008 Threshold to be satisfied even if PU Rx is at edge of coverage Provided SU maintains distance D SU (CR) must be able to detect PU at distance (R+D) Detection Sensitivity
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Uncertainties in Sensing
Channel Uncertainty Due to fading / shadowing of PU signal Noise Uncertainty Aggregate Interference Uncertainty PU may experience harmful interference If multiple CR networks active Requires more sensitive detectors Detect PU at distance Alternative – system level coordination among CR devices Cooperative sensing Ref: Ghasemi et al., IEEE Communications Mag, April 2008
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Cooperative Sensing Sensing of primary user difficult with multipath fading and shadowing Significant fluctuation of signal level (worst case is very severe) Need to maintain sensing performance CR requires higher detection sensitivity (lower ) Requirement becomes very stringent To alleviate the problem … Cooperative Sensing Independent measurements at different locations / CRs Exchange of sensing information among CR nodes Diversity gain achieved (with respect to fading and shadowing) Improved probability of detecting PU Without increasing sensitivity of each individual SU Rx Introduces additional communications overhead Requires functionality of “Band Manager” (Fusion Centre) Collects information, makes decisions and shares information with all CR nodes Shadowing is correlated over short distances Cooperation to be done over larger distances (few nodes) Different from conventional view of Mesh / Ad Hoc networks (many nodes in close proximity)
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Cooperative Sensing Decision making options Hard Decision
Hard decision based Soft decision based Hard Decision Each SU makes indep decision Reg presence of PU One-bit decision Band Manager gathers information Shares decision with all CR nodes Rule: If one of the SUs senses PU signal Primary User present ROC – Receiver Operating Characteristic to evaluate performance Observation HD based decision making – not beneficial if SU SNRs are vastly different
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Multicarrier Techniques in CR
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Multicarrier Techniques
Multicarrier techniques widely used in Cognitive Radio (PHY) OFDM, Filterbank-based multicarrier, Multi-resolution filter banks Spectrum sensing – determine spectral holes Spectrum usage – communication Transmit data w/o interfering with Primary user In non-overlapping parts of spectrum Multicarrier techniques – efficient and effective To maximize efficiency Sidelobes (frequency response) of the subcarriers must be minimized CR transmission can be TDD or FDD TDD has inherent advantages for CR Tx and Rx in in same band knowledge of channel Implicit sensing of channel during Rx period (Tx OFF) WRAN standard focus on TDD OFDM based Frequency Code Time
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Multicarrier Techniques
OFDM Widely studied and well-understood (based on IFFT / FFT) Used for spectral sensing Underlying filter is the Rectangular window Poor side-lobe suppression Significant interference between sub-carriers Not suitable for spectral sensing / transmission (non-contiguous bands) Acceptable for contiguous bands Approaches to consider Muti-Taper Method (MTM) for spectral estimation Filterbank Multi-Carrier Filterbank-based approaches can overcome spectral leakage problems Less used than OFDM
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OFDM Carriers in Available Spectrum
Frequency T I M E Spectral Adaptation Waveforms Ref: B. Fette, “SDR Technology Implementation for the Cognitive Radio,” General Dynamics
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Performance of FFT Frequency response of “FFT filter”
Raised cosine filtering before FFT Reduces side-lobes Improved freq selectivity At expense of lower time selectivity Frequency response of “FFT filter” Filtering at Rx end also possible Similar tradeoff as at Tx Ref: Boroujeny et al., IEEE Communications Mag, April 2008
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Multicarrier Techniques
Multitaper Method (MTM) Advanced, non-parametric spectral estimation method A set of filters (Slepian 1978, Bell Labs) Discrete Prolate Spheroidal Sequences Optimal trade-off between time selectivity and frequency selectivity Combine the output of a family of filters Near-optimal performance in spectral sensing (Haykin, 2005) Example: A set of 5 DPSS based filters and their responses Filterbank Method Similar performance to MTM Can be used for sensing and for transmission Lower computational complexity than MTM A rich area for further investigation for CR
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Performance of Filterbank
Ref: Boroujeny et al., IEEE Communications Mag, April 2008 MTM – five filters of length 2048 Three filters with attenuation more than -60 dB Filterbank Multicarrier – Length 6x256=1536, 256-channel filterbank Achieves comparable performance to MTM
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UWB-based CR
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UWB Overview Cognitive network – an interconnection set of CR devices
Aware of radio channel characteristics Interference temperature, spectrum availability, policies, … Devices sharing of information to facilitate CR functions Suitable wireless technology facilitate collaboration between CR nodes Ultra Wideband (UWB) Bandwidth (BW) > 500 MHz or Fractional BW FCC permits unlicensed use of UWB (2002) Proposed methods for UWB OFDM-based UWB (UWB) – (OFDM-UWB) Impulse radio based UWB (IR-UWB)
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UWB Overview UWB – an underlay system
Co-exist with other licensed (primary) / UL users In same temporal, spatial, and spectral domain Signal embedded in noise floor secure transmission UWB has multidimensional flexibility Pulse shape, bandwidth (BW), data rate, power UWB has inherent potential to meet CR requirements IR-UWB – multiple attractive features High multipath resolution Ranging and positioning UWB – unlicensed operation in GHz Tx power limit < -42 dBm/MHz Ensures that UWB does not affect licensed operations
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UWB-based CN An interesting possibility … Information exchange in CN
UWB as a complement to other CR technologies For sharing information via UWB Locating other users Information exchange in CN CR nodes must have common understanding of spectrum to be used Sharing of sensing information Possible options Common control channel for CR nodes to share information A centralized controller that gathers info and decides spectrum availability Allocates distinct bands to each CR user Alternative: Low-power UWB signaling to share information Leverage all the advantages of UWB Low-throughput needed Low-complexity (OOK, with non-coherent detection) Issue: range of UWB
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Cognitive Networks Network of nodes with CR functionality
Cognitive networks is attractive for Dynamic Spectrum Access Sharing via UWB is attractive Point-to-point model Centralised model Draw from research results in UWB-based sensor networks Source: Arslan et al., Cognitive Wireless Communication Networks, Springer
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Security in Distributed Sensing
Reliable spectrum sensing is key in CR networks Shadowing and multipath fading challenges in sensing Shadowing leads to “hidden node” problem Sensing challenges alleviated by “Cooperative Sensing” Using multiple distributed CR nodes Two major security issues Incumbent emulation Caused by a malicious secondary Gains priority over channel by emulating PU characteristics Falsification of spectrum sensing data False data to mislead band manager Both are important issues that need to be addressed Potential countermeasures Authentication of the data and the sender Robust data fusion methods
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Information Theoretic Aspects - Capacity of CR Channel
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Information Theoretic Aspects in CR
Current CR scenario Device X1 transmits only when channel is free Device X2 transmits after X1 Or uses different freq band X2 need not wait until X1 is done Ref: Devroye et al., “Limits on Communications in a Cognitive Radio Channel,” IEEE Communications Mag, June, 2006 Is simultaneous transmission more efficient than time sharing? What are the achievable rates at which two users (CR capable) could transmit What are the achievable rates if two users do not have CR capability?
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Information Theoretic Aspects in CR
Ref: Devroye et al., “Limits on Communications in a Cognitive Radio Channel,” IEEE Communications Mag, June, 2006 Cognitive Radio Scenario Simplified model : Two transmitters (X1 and X2) and two receivers, (Y1 and Y2) Goal: Define and evaluate channel capacity for CR channel Two links: (X1 Y1 ) and (X2 Y2 ) Evaluate max. rate at which information sent over both links Capacity will be a two-dimensional graph (R1 , R2 ) Capacity regions – max. set of all reliable rates that can be simultaneously achieved Obtain inner (achievable region) bounds and outer bounds Usually based on random coding (w/o explicitly constructing codes
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Information Theoretic Aspects in CR
Two links: (X1 Y1 ) and (X2 Y2 ) X2 is a CR device (X1 X2 ) exists X2 knows message of X1 Genie aided X1does not know message of X2 An asymmetric problem An idealized situation Will provide an upper bound on rates achievable in practice An open problem Achievable region – combination of Han-Kobyashi interference region Dirty paper coding Relaying Ref: Devroye et al., “Limits on Communications in a Cognitive Radio Channel,” IEEE Communications Mag, June, 2006
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Capacity Computing capacity regions uses three techniques
Han-Kobyashi interference region Dirty paper coding Relaying Two links: (X1 Y1 ) and (X2 Y2 ) and X2 knows message of X1 Two possible actions of X2 Selfish Approach Try to mitigate own interference Dirty Paper coding Achieves region where R2 > R1 Selfless Approach X2 acts a relay for X1 X2 does not transmit own information Region where R1 is higher than R2 Region 1 – Time sharing by X1 and X2 Region 2 – Interference region – both do not know other’s information Region 3 – Cognitive region Region 4 – MIMO region – Both X1 , X2 and Y1 , Y2 cooperate This is the region that gives maximum capacity
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CR – A Practical Implementation
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CorDECT Rural WLL Deployment
CorDECT Network Cor - DECT CPE Fixed Wireless Link Up to 240 Kbps per village 15 Km range (up to 25 km with repeater) PSTN SS7/ R2MF Village B V5.2 This picture illustrates a typical rural deployment The Access center is located at the district headquarters This houses the CorDECT CO which interonnects this network to the PSTN and Internet Mostly the Basestation is colocated with the Access Center, However, depending on the deployment density, the basestations can be remotely located using E1 or xDSL interfaces. The CorDECT CPE works as a fixed wireless terminal providing both voice and data connectivity to remote villages The typical range is 15km, however it can be enhanced using cost effective repeater technology from Midas CorDECT CorDECT Internet Base Station xDSL/E1 CO Cor - DECT CPE Access Center Village A corDECT is deployed in > 15 countries
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GSM - CR Combination GSMLite
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CR Techniques for GSM band
Goal: Adaptive freq selection for GSM BTS Interference avoidance using CR Prototype (under field trial): Description: Support GSM Lite developed by Midas Usage: rural areas, in-building, femtocells Based on ADI Blackfin DSP Challenges Weak signal detection and monitoring Listening to other GSM BTS Hardware and Software Implementation Approaches for detecting GSM signal Cross Correlation Detector – training sequence Cyclostationarity-based Sensitive to frequency error Hybrid Detector (developed) Combines different schemes Implementation – “intelligent hopping” TeNeT group has developed Wireless Local Loop (WLL) product based on the DECT standard. This is a product called “corDECT” which is manufactured and sold by MIDAS, a company incubated under the auspices of TeNeT. It has clearly been demonstrated that corDECT is a cost-effective solution for wireless access. It is now being deployed in more than fifteen countries outside India, indicating that telecom products that are world-class and at the same time, are also cost-effective can be successfully developed in India. TeNeT faculty play an active role in offering continuing education courses in Wireless to students and faculty from other engineering colleges and also to engineers working in industry. Performance of Hybrid scheme
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Summary A technical overview of Cognitive Radio
CR - A paradigm shift in wireless communications Potential of significant increase in spectrum availability Opportunistic access Spectrum sensing Understanding the various challenges Technical and regulatory issues Robust and computationally efficient approaches are needed Cooperative sensing is attractive Information theoretic aspects – Capacity region for CR IEEE standard A practical application – CR-based GSM basestation Overall, CR is an exciting field
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My best wishes to all participants of IISc-DRDO Seminar on Cognitive Radio Thank You !
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David Koilpillai Profile
Education B.Tech, IIT Madras, MS, PhD Caltech, USA Work Experience IIT Madras (2002 – present) Professor, TeNeT Group, EE Department CEWiT – Chief Scientist (Jan 2007 – July 2007 Co-Chair, IIT Hyderabad Task Force (June 2008 – present) Ericsson Inc, USA ( ) Director, Advanced Technologies, Research and Patents (R&D team of 75 engineers, annual budget US $20 Million) Professional Areas of expertise: Cellular, wireless systems, DSP 32 Issued US patents Publications: 11 Journal, 45 Conference Research Interests: DSP applications in Wireless Ericsson Inventor of Year Award 1999 Fellow, Indian National Academy of Engineering
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