Cognitive Wireless Network Research at Virginia Tech

1. Cognitive Wireless Network Research at Virginia Tech Jeffrey H. Reed Director, Wireless@Virginia Tech Bradley Dept. of Electrical and Computer Engineering reedjh@vt.edu (540) 231 2972

2. Faculty Jeff Reed (Director, W@VT) Software Radio Claudio da Silva Spectrum Sensing Allen MacKenzie Cognitive Networks Tamal Bose (Assoc. Director, W@VT) Digital Signal Processing Luiz DaSilva Cognitive Networks Jung-Min Park Security Charles Bostian RF Design, Cognitive Engine Tom Hou Resource Management Yaling Yang Routing Mike Buehrer Cognitive MIMO Michael Hsiao Software Verification Madhav Marathe Modeling and Simulation • Research faculty: Carl Dietrich, Kyung Bae • Students: approximately 40 graduate students

3. Motivated by Cognitive Radio DoD (research driver) Regulators both domestic and international Refarm spectrum More efficient use Industry – easy deployment and maintenance and spectrum Standards groups White Space Coalition Public safety Where is the Cognitive Radio Headed?

4. Advanced Networking for QoS Power Consumption Reduction Collaborative Radio – Coverage and capacity extensions Femto cells and spectrum management Cognitive MIMO, e.g, learning the best spatial modes Cellular Radio Resource Management Maintenance and Fault Detection of Networks Multibanding, e.g., mixing licensed and unlicensed spectrum or protected and unprotected Public Safety Interoperatiliby Cognitive Routing and prioritization Emergency Rapid Deployment and Plug-and-Play optimization Enhanced security Anticipating user needs – intersystem handoff and network resource allocation Smart Antenna management Location dependent regulations Revolutionary Applications

5. Army Research Office BBN DARPA ETRI ICTAS Intel Sponsors of Cognitive Network Research • - Motorola • - National Institute of Justice • National Science Foundation • ONR • - Tektronix • - Texas Instruments

6. Outline • Cognitive Radio Concept and Applications • CR Development • Cognitive engine • Spectrum sensing • Cognitive networks • Game theory • Dynamic spectrum allocation • Dynamic spectrum sharing • Verification • Security • Radio environment maps • Cognitive routing • Cognitive MIMO • CR Deployment • - VT prototype and related technologies • - Corteks • Cognitive radio network test bed • $500 cognitive radio node and the DARPA WNaN network • MANIC Challenge

7. Cognitive Radio Concept Cognitive radios are flexible and intelligent radios that are capable of… … and can be realized as a cognitive engine (intelligent software package) controlling a software defined radio platform.

8. Cognitive Radio Concept Cognitive Radio – how the radio behaves Software Defined Radio – how the radio is constructed and controlled Fixed radios– set by operator Legacyradios– function is defined in hardware. Adaptive radios- adjust themselves to accommodate anticipated events. Software defined radios – function is defined by software. Cognitive radios- sense their environment and learn how to adapt.

9. Cognitive Networks • A single cognitive radio has limited utility. • Radios must work together to achieve goals, and requires fundamental changes to • Routing -- QoS provisioning • Spectrum sensing -- Collaboration • Intelligence is cheaper at the network level than the node level.

10. Cognitive Radio Applications

11. Cognitive EngineCharles Bostian

12. The concept of a Cognitive Engine Any SDR can go here

13. CWT2 Cognition Cycle with Two Loops Inner loop: Learning Outer loop: Recognition and Adaptation By Bin Le Environment Observation Scenario Synthesizing Case identified Waveform 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 Power Frequency Bandwidth FEC Modulation Pkt length WSGA Initialization Objectives Constraints Radio Practice Environment awareness and evolving knowledge lead to optimal radio reconfiguration

14. Cognitive Engine – Software Architecture observe Adapt Learn and reason United States Patent Published Application 20060009209 Cognitive Radio Engine Based on Genetic Algorithms in a Network

15. Another view of the software

16. Our patented distributed cognitive architecture allows a cognitive radio to incorporate modules from any source.

17. Signal Detection and Classification Using Spectral Correlation and HMM Jeff Reed

18. Signal Detection and Modulation Classification in CR CR require high sensitive signal detector Cyclostationary signal detector outperforms energy detector and matched filter detector Cyclic feature detection can be performed without knowing modulation type in low SNR Modulation type classification Provides appropriate demodulation method in CR receiver Enhance the spectrum awareness capability by collection higher layer information 18

19. Main signature remains BPSK with SNR=9dB BPSK with SNR=-9dB Spectral Correlation Example

20. Signal Detection with CDP Signal detector finds dominant peak in SCF SCF is 3-dimentional data  SCF amplitude, cycle frequency, and spectral frequency To reduce dominant cyclic feature search overhead in SCF, CDP is used Dominant cyclic features are extracted using crest factor of CDP Dominant cyclic features serve as feature vector in modulation type classification 20

21. Signal Detection with CDP Binary hypothesis testing Detection statistics Constant false alarm rate testing 21

22. Signal Detection and Feature Vector 10% CFAR  threshold Detected features Missed feature 22

23. BPSK Detection Performance CDP based detector is comparable to optimal single cycle detector up to SNR=-8dB with 50 observation block. 23

24. Modulation Type Classification Most modulation type has unique cyclic features This uniqueness can be exploited for modulation classification  unique feature vector The unique feature vector can be recognized using Hidden Markov Models (HMMs)  HMM based pattern matching algorithm is used for modulation classification HMMs has been used successfully for pattern matching in applications such as voice and hand writing recognition 24

25. Modulation Type classification Architecture 25

26. Modulation Type Classification Performance BPSK, FSK, MSK, and QPSK classification -6dB signal with varying noise environment Feature vector is generated using 10% FA • 90 % correct classification: • FSK and MSK at 50 obs. • BPSK and QPSK at 80 obs. 26

27. Signal Types BPSK, QPSK, AM, FSK, and MSK Spectral Correlation Time-domain averaging method Observation unit is 100 symbols IF Carrier frequency = 25% of sampling frequency Symbol rate = 10% of sampling frequency Assume received signal carrier frequency and bandwidth are known HMM Baum-Welch algorithm Quantize the spectral coherence and cycle frequency Simulation Parameters

28. HMM Recognition Diagram Wide range SNR (-9dB ~9dB) signals are coming and mixed down IF level Trained with specific signal type. For instance, HMM for AM with 9dB … Trained with specific signal type. For instance, HMM for QPSK with 9dB

29. Train HMM once using SNR=9dB Recognition is performed using input signals with SNR=-9dB In case of AM, BPSK with SNR= -9dB signal is false alarm of AM with SNR=9dB Overcome using 9dB and -9dB trainings for AM model Simulation Results (1/3) HMM cognition performance with various SCF observations and varying SNR

30. BPSK signal detection rate of various SNR and observation length(BPSK HMM is trained with 9dB) Decreasing SNR increases observation time to obtain a good detection rate Simulation Results (2/3) Detection Rate - 12dB 0% 50% 100% - 9dB - 6dB 0 5 10 15 20 25 30 35 40 Observation Length (One block is 100 symbols)

31. Simulation Results (3/3) • The required observation time to attain 90% detection rate is decreasing exponentially with input SNR Observation Length (One block is 100 symbols) 90% Detection Rate 0 5 1 0 15 20 -12 -10 -8 -6 -4 -2 0 2 4 6 8 9 (in dB) Input SNR

32. Signal detection using spectral correlation is possible in low SNR HMM classifies incoming signal with low probability of false alarm Identified required SNR and observation pair to attain specific detection rate Summary Results (1/2)

33. Baseline comparison (Detection rate) Even low SNR, -9dB, VT method outperforms than non-VT method <Note> * Multi-trained with -9dB and 9dB ** H. Ketterer, F. Jondral, and A. H. Costa, "Classification of modulation modes using time-frequency methods," 1999. Summary Results (2/2)

34. Distributed Spectrum SensingClaudio da Silva

35. Cognitive Radios: Spectrum Sensing Aiming at more efficient spectrum utilization, the FCC is currently revisiting allocation policies and moving toward the adoption of “spectrum sharing” strategies such as cognitive radio. • We shall not forget... • (Previous “attempts” to allow for spectral co-existence) • Digital overlay to the analog cellular network • Result: failed • UWB • Result: world wide adoption questionable

36. Distributed Spectrum Sensing • User 1 and 2 might not detect the primary user • Together, users 1, 2, and 3 have a much higher probability of detecting and classifying other systems • Distributed spectrum sensing: • Spectrum utilization is a spatial phenomenon • Take advantage of the radio signal variability • Tradeoff: number of sensors vs. radio complexity (is it better to have one expensive, highly complex receiver or several • inexpensive, less complex radios?)

37. Distributed Spectrum Sensing • Each radio obtains some relevant information on the spectrum, processes this information, and then shares a summary of its observations to other radios. • Node-processing – cyclic analysis, wavelets • Information reduction/compression – power/bandwidth sensing • information transmission • Data fusion – complexity

38. Cyclic-Feature Analysis

39. DistributedDetection • While single-node cyclic-feature algorithms are well-known, distributed algorithms based on such an approach have yet to be developed. • The challenge in this research is the design of algorithms that define how the cyclic-feature algorithm output must be thresholded, and how the outputs from multiple radios should be fused with a given complexity level. • “On the complexity of decentralized decision making... our results point to the inherent difficulty of decentralized decision making and suggest that optimality may be an elusive goal” [Tsitsiklis’85].

40. Specific Emitter IdentificationJeff Reed

41. Conventional network level security data (e.g. user ID, authentication key) can be vulnerable to hacker attack CR system depends heavily on spectrum measurement from other CRs False spectrum measurement information from malicious or malfunctioning CR devices breakdowns the operation of whole CR networks Conventional SEI exploits time-domain features available only short period Pulse rising & falling time and pulse rising & falling angle Need high SNR, good channel, and high speed ADC Requires new SEI technology which has some tolerance to the noise and varying channel conditions This leads to the investigation of cyclostationary features for SEI Motivation

42. Conventional SEI Methods SEI originated in military countermeasures to classify an enemy’s radar signal for threat evaluation Conventional SEI features Time-domain features Inter-pulse information : pulse repetition time Intra-pulse information : pulse rising & falling time and pulse rising & falling angle Frequency-domain features Power spectral density shape Symmetry of main and side-lobe Roll-off rate of pass-band Bandwidth Pass-band and stop-band shape Phase space feature Provides distinctive features of non-linear power amplifier N-dimensional phase space trajectory may show distinctive shape  it can be utilized as a feature Conventional features are easily obscured at low and fluctuation SNR 42

43. New Method • New method exploits the cyclostaionarity of transmitted signal • Never tried before. Most application of cyclic spectral analysis has been focused on modulation classification. • Unintentional harmonics buried in high noise can be identified easily due to nonlinear devices such as power amplifier. • Suitable for SEI of modern digital communication such as CDMA and OFDM systems 43

44. Application of SEI in CR A specific WLAN card can be used for authentication of known nodes in a network. If the specific WLAN signal is identified, then the spoofing signal can be filtered in CR spectrum sharing scenario. Radio signal profiling of malicious signal generator to improve spectrum sharing. The electromagnetic fingerprint can be an important factor for network forensic expert in the future. 44

45. Measurement Setup • 6 Different Wireless LAN Cards • Motorola • LinkSys • Belkin • D-Link • Netgear • IBM 45

46. Example of Cyclic Finger Print 46

47. HMM-based Classifier Architecture HMM training More than one HMMs per WLAN card are generated to obtain better identification 47

48. HMM-based Classifier Architecture HMM recognition 48

49. SEI Performance Confusion matrix of HMM identification for 200 trials SEI performance shows more than 90% correct classification except IBM and Netgear which have similar features 49

50. SEI Performance at SNR=0dB Confusion matrix of HMM identification for 0dB signal and 200 trials SEI performance shows more than 65% correct classification except IBM and Netgear which have similar features 50