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System Integration Cognitive Radio Yves LaCerte Rockwell Collins

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1 System Integration Cognitive Radio Yves LaCerte Rockwell Collins
(952) Cognitive Radio System Integration

2 System Integration Topics What is System Integration?
An Example - Cognitive Radio Integration Trends 814/2008 Yves LaCerte

3 What is System Integration?
INCOSE SE Handbook…. …establishment of system interfaces, internal and external… …emphasis on risk management and continuous verification… The process of putting a system together, with techniques to ensure all the parts work as a whole. 814/2008 Yves LaCerte

4 Integration is Hard Generally, the main contractor for the project is responsible for systems integration. The sub-contractors will usually be part of the integration team. Integration is one of the most costly and time-consuming activities in the systems engineering process. For large and complex systems, up to 40% of the development effort may be used in this activity, mostly in system testing. 814/2008 Yves LaCerte

5 Cognitive Radio A Non-traditional System Integration Challenge
Scientific American, February, “A Public Safety Cognitive Radio Node” “A Policy Proposal to Enable Cognitive Radio for Public Safety and Industry in the Land Mobile Radio Bands”, 814/2008 Yves LaCerte

6 Scenarios Urban agencies need to communicate with each other
New York City police and fire departments during 9/11 were not successful Federal, state and local level responders need to communicate Katrina response was less than successful 814/2008 Yves LaCerte

7 The Problem “Spectrum“ is regulated (e.g. FCC)
Assigned/licensed to users On a long term basis For large regions like whole countries 814/2008 Yves LaCerte

8 A Solution Cognitive Radio Senses and is aware of its environment
Dynamically adapts to utilize changing radio resources Maintains connectivity with its peers Does not interfere with licensed users and other CRs 814/2008 Yves LaCerte

9 814/2008 Yves LaCerte

10 Timeline 814/2008 Yves LaCerte

11 CR Architecture Cognitive Radio architecture:
Basic Non-Cognitive Radio Architecture: 814/2008 Yves LaCerte

12 Integration Challenge I
Multi-disciplines Major Domains Wireless communications Location-aware sensors Radio engineering Wide band antennas Machine learning Spectrum regulations Application service Etc. Policy domain Radio domain User domain 814/2008 Yves LaCerte

13 Machine Learning Orient Plan Decide
Some tasks cannot be defined well except by example. Discovers important relationships and correlations in large data sets. The working environment is not be completely known at design time. Orient Establish Priority Plan Normal Generate Alternatives (Program Generation) Evaluate Alternatives Register to Current Time Decide Alternate Resources Initiate Process(es) (Isochronism Is Key) Act Learn Save Global States Set Display Send a Message Observe Receive a Message Read Buttons Outside World New The Cognition Cycle Prior Pre-process Parse Immediate Urgent Infer on Context Hierarchy Mitola, “Cognitive Radio for Flexible Mobile Multimedia Communications”, IEEE Mobile Multimedia Conference, 1999, pp3-10 814/2008 Yves LaCerte

14 Cheaper RF Front-End Design
Advantages Flexibility A more flexible radio over classic radio systems. Software makes it easier to upgrade for better performance…. And upgrade for new performance…. Cheaper RF Front-End Design One problem with classic RF design is the complexity and the labor in developing a reliable design. With the design of a reliable Software Defined Radio (SDR), the quality and performance of the SDR can be enhanced by the digital hardware in order to reduce the complexity (and therefore the cost) of the RF front-end. Digital Signal Processor (DSP), Field Programmable Gate-Array (FPGA), General Purpose Processor (GPP) Smaller Parts Count With a less complicated RF front-end, the total parts needed is simplified. With digital components like the DSP and FPGA taking the place of many passive and active components, the list is smaller and cheaper. Mechanisms for flexible pooling of radio spectrum using a new class of protocols “formal radio etiquette” The radio learns. As we learn important etiquettes and plug them into an SDR, along with enough smarts to choose the proper etiquette for the proper circumstances. The degree of sophistication is practical where the radio architecture provides sufficient resources to support the protocols. 814/2008 Yves LaCerte

15 Disadvantages Software Reliability Security SWaP The radio learns.
Mechanisms for flexible pooling of radio spectrum using a new class of protocols “formal radio etiquette” The radio learns. As we learn important etiquettes and plug them into an SDR, along with enough smarts to choose the proper etiquette for the proper circumstances. The degree of sophistication is practical where the radio architecture provides sufficient resources to support the protocols. SWaP 814/2008 Yves LaCerte

16 Integration Challenge II
Unlike traditional interferers, cognitive radios adapt their operation in response to their perceived interference environment. When numerous cognitive radios are collocated, this interference environment may be constantly changing as the cognitive radios adapt to the other cognitive radios adaptations. Because of this recursive process, serious concerns are introduced: Under what conditions will the recursions settle down to a steady state? What is that steady-state? Will the resources be hoarded by a single radio/link or will they be equitably shared among the radios? Will the cognitive radios actually make use of available spectrum without impinging on other radios’ spectrum rights? How much bandwidth will be consumed with signaling overhead and how much bandwidth will actually be used for data transfer? 814/2008 Yves LaCerte

17 814/2008 Yves LaCerte

18 A Typical Integration Example
Collect Hardware Components Human Systems Integration Integrate Hardware Platform Test System Interfaces User Acceptance Test Configurations Integrate Software on Target Hardware Stress Factory Acceptance Collect Software Components Resolve Issues 814/2008 Yves LaCerte

19 Human Systems Integration
814/2008 Yves LaCerte

20 Integration Trends Integrated Modular Architecture
Each supplier generally has proprietary hardware (LRU) increasing cost of supply / repair chain and aircraft weight All software in a LRU/card must be developed to the same safety level even, if this is not strictly necessary, and is dedicated to that LRU If the hardware platform changes the whole product needs to re-verified by licensing authority 814/2008 Yves LaCerte

21 Integration Trends Integrated Modular Architecture
Uses spare computing capacity to run multiple independent applications in a central processing network – fewer equipment racks therefore less weight Application software is independent of an open architecture core executive – therefore it is platform and location independent Application software can be validated independently of the core executive and hardware Application software is location independent of the IO (Desirable but not always the case) 814/2008 Yves LaCerte

22 Integration Trends Micro and Nano Technologies
“The principles of physics, as far as I can see, do not speak against the possibility of maneuvering things atom by atom. It is not an attempt to violate any laws; it is something, in principle, that can be done; but in practice, it has not been done because we are too big”. Richard Feynman, “There’s Plenty of room at the bottom: An invitation to enter a new field of physics,” Engineering and Science, Feb. 1960, 814/2008 Yves LaCerte

23 Integration Trends Integration Micro and Nano Technologies
Systems engineering will become a key enabler for the successful commercialization of multi-functional, micro and nano technologies (MNT). Systems engineering delivers the methodologies, processes and tools to enable the efficient integration and exploitation of these disruptive technologies. Mechanical Fluidic BiPolar Parasitics Optical Thermal CMOS Digital Integration Inductance Behaviors Analog Materials MEMS Design Flow VLSI Design Flow 814/2008 Yves LaCerte

24 Integration Trends HW / SW Codesign
Increasing behavioral complexities… requires “design” optimization many functions, great variability, high flexibility heterogeneous target systems - processors, ASICs, FPGAs, systems-on-chip, … many design goals performance, cost, power consumption, reliability, ... 814/2008 Yves LaCerte

25 Integration Trends HW / SW Codesign
Systems engineering will become a key enabler for the successful commercialization of complex embedded software intensive systems. Systems engineering delivers the methodologies, processes and tools to enable the efficient integration and exploitation of these disruptive technologies. 814/2008 Yves LaCerte

26 Enterprise Integration
814/2008 Yves LaCerte

27 Enterprise Integration
814/2008 Yves LaCerte

28 Enterprise Integration
Business integration design and modeling of business processes Presentation integration integration of corporate knowledge and business processes Data integration how data is modeled and the meaning of the data Control integration messaging between applications Application integration different applications work together using mechanisms such as automatic event notification, flow control, and content routing 814/2008 Yves LaCerte

29 Integrating Two Systems
Exploration Why is the information needed? What information is needed? Information Management How will the information be used? How will the information be stored? What is the human role in the required activities? Constraints What are the constraints? Effectiveness How will the integrated system continue to be effective? Technology What technology will facilitate the required activities? 814/2008 Yves LaCerte

30 814/2008 Yves LaCerte

31 Integration is Hard High degree of uncertainty
Design for integrability Integration strategies Emergent properties 814/2008 Yves LaCerte

32 Uncertainties System components are not available on time
Duration of integration is longer than planned Cost of testing facilities is higher than planned 814/2008 Yves LaCerte

33 Sub-system Integration
Integration Planning Requirements Specification User Acceptance Test Plan User Acceptance Test System Specification Factory Acceptance Test Plan Factory Acceptance Test System Design System Integration Test Plan System Integration Test Detailed Design Sub-system Integration Test Plan Sub-system Integration Test Component Implementation Component Test 814/2008 Yves LaCerte

34 Design for Integrability
Integration tends to be more successful with low coupling between components Partitioning decisions are made early, often without integration in mind Hardware software co-design Merged integration approach 814/2008 Yves LaCerte

35 Integration Strategies
Big bang or Incremental Horizontal or Vertical Order of integration impacts efficiency First come first integrated? Foundational components with long lead time? 814/2008 Yves LaCerte

36 Integration Strategy Incremental integration
Scheduling and staging strategy Components are developed at different times or rates, and integrated as they are completed The alternative to incremental development is to develop the entire system with a "big bang" integration 814/2008 Yves LaCerte

37 Horizontal Integration
Integration Strategy Sub-system 2 Sub-system 1 Sub-system 2 Sub-system n Horizontal Integration Vertical Integration Component 1 Component 2 Component m 814/2008 Yves LaCerte

38 Integration Strategy Component 2 Component m Sub-system 2 Sub-system 1
Sub-system n Component 1 Component 1 Component 1 Component 2 Component m 814/2008 Yves LaCerte

39 Emergent Properties A new component is introduced and problems are found Is it due to the relationship between the new component and the existing system? Or does the new component cause the existing system to be used in a different way? Did problems with the system exist BEFORE the component was added? 814/2008 Yves LaCerte

40 The State of Our Knowledge
System “C” We know quite a lot about integrating components (over which we may have little or no control) to form systems. “SYSTEM D” Unplanned, unexpected, emergent behavior here… We know something about integrating individual systems (over which we may have little or no control) into systems of systems. System “B” We know very little about integrating an interoperable network of systems…the key distinction being that the network is unbounded (or very loosely bounded) and has no single controlling authority. System “A” 814/2008 Yves LaCerte

41 Integration challenges
Unbounded Systems E.g. Large-scale communication networks Incompletely and imprecisely defined Distributed administrative control No central authority Limited global effect Emergent algorithms Predict global effects based on local activities Integration challenges What guarantees can be provided that the results of integrating systems into larger systems, when the interfaces are not completely known, will be acceptable? This is often addressed as the interoperability problem. 814/2008 Yves LaCerte

42 Interoperability Integration: the act of forming, coordinating, or blending into a functioning or unified whole. Interoperation: The ability of two or more systems or components to exchange information and to use the information that has been exchanged. Interest in integration of current stand-alone systems to meet future system requirements. Driven by advances in communication technology recognition of common areas of functionality in related systems increased awareness of how enhanced information access can lead to improved capability 814/2008 Yves LaCerte

43 Interoperability Challenges
Communication Media Wireless (WiFi, WiMax, cellphones) Secure? Reliable? Messaging and Security Across different equipment from different vendors: – Cyber security –“security by obscurity” is no longer feasible – Network management – will the data get where it needs to go in a timely manner? – Protocol standards – agreement on standardized interfaces between systems Data Management Across different equipment, vendors, customers – Vast amounts of data, data discovery – how to manage this data? – Data mining, data consistency, data privacy – how to find and validate data? – Conversion of “data” into “information” – how to use effectively? – Data modeling standards – ability for “self-healing” Computer Applications Real-time analysis? – Automated controls – what and where should they be implemented? – System reliability, efficiency, service, safety, compliance? 814/2008 Yves LaCerte

44 Game theory Does the algorithm have a steady state?
What are those steady states? Is the steady state(s) desirable? What restrictions need to be placed on the decision update algorithm to ensure convergence? Is the steady state(s) stable? 814/2008 Yves LaCerte

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