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Recognition and Adaptation in PHY Layer for Cognitive Radio

This presentation discusses the key issues in the physical layer of cognitive radio and proposes a system model for cognitive radio that includes hardware support, sensing technology, and machine reasoning. It explores the concepts of knobs and meters for radio operation and highlights the importance of spectrum utilization and waveform agility. The presentation also addresses challenges in the physical layer, such as sensing reliability and software approach for flexible waveform implementation.

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Recognition and Adaptation in PHY Layer for Cognitive Radio

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  1. Bin Le • Bin Le is a PhD candidate in Center for Wireless Telecommunications (CWT) at Virginia Tech, advised by Dr. Charles W. Bostian. Since 2003, Bin joined CWT and conducted the research of direct-conversion receiver (DCR) and started the research in cognitive radio in 2004. His research includes radio domain cognition, software defined radio and evolutionary computation. He has diverse publications mainly covering RF engineering, SDR design, signal processing and artificial intelligence. He is also the author of a book chapter of cognitive network support.

  2. W @ ireless T V irginia ech Recognition and Adaptation in PHY layer Bin Le binle@vt.edu Center for Wireless Telecommunications 466 Whittemore Hall Blacksburg, VA 24061 Virginia Tech 04/10/2006

  3. Presentation Outline • Radio domain cognition • Cognition cycle • Recognition and adaptation • Key issues in PHY layer • Spectrum utilization • Waveform agility • Hardware support

  4. System Model of Cognitive Radio • Knobs • Encoding of radio operation space • Adaptation through hardware AI • Meters • Sensing technology • language for environment awareness • Monitor through performance API Hardware Independent Cognitive Engine Machine Reasoning Radio Parameters “Knobs and Meters” Sense/Reconfigure Radio TX Radio RX Adaptation Detect/Sense Physical Media Air interface Behavioral diagram Functional diagram

  5. Cognition Cycle with Two Loops Environment Observation Scenario Synthesizing Case identified Link condition User/policy Radio hardware Knowledge Base Case report Reasoning Case-based Decision Making Apply experience Success memorized Bad trail overwritten Strategy instruction Radio Performance Estimation Link Configure Optimization WSGA Initialization Objectives Constraints

  6. Radio Recognition and Adaptation Wireless environment Performance API Transparent Radio-domain Adaptation Decision-making Learning Core Radio-domain specific General AI Radio-domain Recognition Radio platform Transparent Radio API

  7. PHY Layer in the CR system PHY layer Recognition PHY layer Adaptation

  8. Key Issues at PHY Layer • Cognitive spectrum utilization • Sensing reliability (hidden node problem, passive nodes 802.22) • Spectrum sharing and performance • Waveform agility • Waveform recognition • Software approach for flexible waveform implementation • Self-optimization of software defined functional architecture • Radio resource management • Hardware performance limitation and trend (ADC, DSP power, etc)

  9. Spectrum Utilization • Example of TV bands: Just need better ways of using spectrum • Global spectrum coordination • Better licensing procedure • Better regulations • Improve efficiency of fixed multiplexing • Allow flexible sharing schemes • Better interaction b/t policy and technology • Cognitive spectrum utilization • Smart and flexible transceiver: both hardware and algorithm • Multi-dimensional radio environment knowledge base to enable and support “cognitive” spectral behavior • Proliferate application and services diversity for “prosperous” bandwidth needs

  10. Spectrum Awareness and Sharing • The idea of cognitive spectrum utilization couples sensing and adaptation together • Adaptation relies on sensing fidelity • Sensing designed by adaptation needs • Joint design for optimal resource cost • Dynamic spectrum management • Open and dynamic coordination • Overlay/underlay to improve spectrum efficiency • Cognitive spectrum utilization

  11. Dynamic spectrum allocation (DSA) • OFDM PHY layer using 802.11a/g specs • Ad-hoc node configuration • CSMA/CA vs. DSA using cognitive algorithms • Simplified power control for minimal SNIR

  12. Spectrum Underlay • DSSS-UWB for spectrum underlay • PN-sequence for multi-user scheme • UWB spectrum is close or below the noise floor of pre-existing narrowband system • External coordination between two service layers is not as critical as in overlay system (using dynamic spectrum allocation scheme) • Spectrum is notched to protect narrow-band channels without much pulse-distortion due to its wide bandwidth

  13. UWB-Broadcast Underlay with Notching BC NW setup Spectrum probe Notch and calculate Gp Compute overall introduced UWB power in the spectrum - Probed at one BC Rx node Calculate SNIR for each network nodes Keep inserting UWB nodes to some cluster size

  14. UWB-Broadcast Underlay: Outage vs. Notching UWB-BC hybrid network in the underlay zone • UWB txPSD_dB = -130dBW/Hz, notchBW = 20MHz, UWB cluster size = 100, broadcast #channels = 10, notch Loss=30dB 50 iterations Spectrum at one BC Rx node after UWB underlay NW is established • Both system Pout is reduced when #notches/#channels increases from 0/10 to 10/10

  15. Simulation Test Conditions Functions Weights Minimize spectral occupancyMaximize throughputInterference avoidance BER 255 100 200 BW 255 10 255 Spectral Efficiency 100 200 200 Power 225 10 200 Data Rate 100 255 100 Interference 0 0 255 Cognitive Spectrum Utilization: MOGA Chromosome field: Power frequency Pulse Shape Symbol Rate Modulation

  16. General Waveform Agility Requirements • Standard-independent waveform parameters • Waveform feature projection and recognition • Adaptive feature extraction • Signal characteristics and channel condition • Hybrid classification approach • Temporal, spectral, and vector space • Open, self-reconfigurable waveform knowledge base

  17. Waveform Modulation Features • Temporal statistical features • Statistical moments with different orders • Simple and fast • Spectral statistical features • Power spectral density (PSD) and cyclostationarity features • Huge computation • Vector-space features • Constellation and motion statistics • No additional cost

  18. Signal Classification Testbed SNR=10dB SNR=50dB OCON Initialization OCON Training Training Algorithm Configure OCON_mod1 Generate modulation waveform Waveform Feature Extraction Feature Set MAXNET … … … OCON_modN Waveform Load configure Feature Configure Waveform Feature Database Management FeatureSet Output configure OCON Configuration Database Management Feature Space Configure

  19. Flexible Waveform Implementation • Based on SDR platform • Flexibility for cross-layer optimization • NSF NetS project • Interoperability • NIJ PSCR project • CWT recent NIJ-Anritsu waveform testbed • CWT Gnu-radio testbed

  20. Gnu Radio Based SDR Testbed Neural Network Classifier Digital receiver Power spectral density Channel BW estimate IF carrier estimate Symbol rate estimation Cyclostationarity Symbol detection Digital IF Signal Input Cyclic features Artificial Neural Network Modulation Classifier USB 2.0 Digital modulation features Temporal statistics Vector Analysis Symbol constellation Analog modulation Analog demodulator Modulation classifier Symbol Rate Modulation Classifier clock Cognitive Radio Receiver Signal Input Multi-rate Decimator Blind Equalization Baseband Channel Filter Symbol Detection demod Analog LPF ADC Gnu-USRP SDR platform Frequency Error Quadrature Frequency Synthesizer Carrier Recovery Symbol Timing Phase estimation Vector Analysis IF carrier Frequency Direct Digital Synthesizer DAC

  21. PHY-Related Hardware Issues • RF limitation – JTRS challenge • frequency and dynamic range • Flexibility in frequency and bandwidth • Analog-to-digital interface – the neck of SDR • ADC largely sets the digital block boundary • Trend in speed, resolution and power • Software processing capability (speed and flexibility: logic vs. ALU) • Power handling and power efficiency • Substrate limitation: Si vs. GaAs • PA power efficiency improvement • Design trade space and trend projection are most wanted!

  22. fs vs. ENOB • Performance limitation depends on sampling frequency. • The contribution of each noise source is different at different sampling rates. • A slope close to 1bit/2.3dBsps is shown for sigma-delta ADCs due to noise shaping techniques; in contrast, a slope much closer to thermal-noise boundary (1bit/6dBsps) is shown for SAR ADC group where no noise shaping loop is used.

  23. Power Grouped by Structure • Proof over 20 years of power consumption dependency on the ADC structure!

  24. P curve fitting

  25. Acknowledgement • This work is supported by Award No. 2005-IJ-CX-K017 awarded by the National Institute of Justice, Office of Justice Programs, US Department of Justice. The opinions, findings, and conclusions or recommendations expressed in this publication/program/exhibition are those of the author(s) and do not necessarily reflect the views of the Department of Justice. • This material is based upon work supported by the National Science Foundation under Grant No. CNS-0519959. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).

  26. W @ ireless T V irginia ech Thank You! binle@vt.edu

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