Wideband Cyclostationary Spectrum Sensing and Modulation Classification

1. Ph.D. Defense 08/19/2013 Wideband Cyclostationary Spectrum Sensing and Modulation Classification Eric Rebeiz Advisor: Prof. DanijelaCabric UCLA Electrical Engineering Department

2. Wideband Cognitive Radio Concept & Vision • Cognitive Radios (CRs) opportunistically access the spectrum How can we achieve this goal? Future Promise of Wideband CR • Increase radio throughput • Support more users In order to achieve this vision, practical wideband spectrum sensing challenges should be considered

3. Wideband Spectrum Sensing & Classification Design • Goal #1: Interference mitigation through sensing and classification • Goal #2: Energy efficient processing Sensing Requirements • False alarm rate • Detection probability • Short sensing time • Minimum SNR Classification Requirements • Classification accuracy • Blind classification • Differentiate among M-QAM, M-PSK, GMSK, M-PAM

4. Wideband Sensing & Classification Challenges • Robust sensing and classification to impairments • Short sensing time • Energy efficiency • High computational complexity • Carrier & sampling offsets • Front-end nonlinearities

5. Research Contributions • Proposed a sensing and classification method that is robust to carrier and sampling clock offsets • TSP’13, Asilomar’12 • Proposed an energy efficient sensing and classification processor in blind scenarios • TCAS’13, Globecom’11, Milcom’11 • Analyzed the impact of receiver nonlinearities on the sensing performance and proposed algorithmic solutions • TSP (to be submitted), Crowncom’13

6. Outline • Cyclostationary feature detection overview • Spectrum sensing and classification under receiver impairments • Blind energy efficient sensing and classification • Impact of nonlinearities and their compensation • Summary of contributions • Cyclostationary feature detection overview • Spectrum sensing and classification under receiver impairments • Blind energy efficient sensing and classification • Impact of nonlinearities and their compensation • Summary of contributions

7. Cyclostationary Detection (CD) Overview • CD can perform spectrum sensing and modulation classification • CD estimates the Cyclic Auto-Correlation (CAC) function (C-CAC*and NC-CAC) or the Spectral Correlation Function (SCF) CAC at SCF

8. Cyclic Features of Different Modulation Types Modulation type determines the present cyclic features Features at functions of and

9. Outline • Cyclostationary feature detection overview • Spectrum sensing and classification under receiver impairments • Blind energy efficient sensing and classification • Impact of nonlinearities and compensation • Summary of contributions • E. Rebeiz, P. Urriza, D. Cabric, Optimizing Wideband Cyclostationary Spectrum Sensing under Receiver Impairments, in IEEE Transactions on Signal Processing, vol. 61, no. 15, pp. 3931-3943, Aug. 2013 • E. Rebeiz,P. Urriza, D. Cabric, Experimental Analysis of Cyclostationary Detectors Under Cyclic Frequency Offsets, in Proc. Asilomar Conference on Signals, Systems and Computers, Nov. 2012

10. Feature Detection Under Receiver Impairments What is the impact on sensing and classification?

11. Signification Degradation in Low SNR • Ideal feature computed by • Feature under cyclic offset computed at

12. Robust Feature Detection • Proposed Multi-Frame CAC where is the total sensing time • yields the conventional CAC • Tradeoff: -  N reduces effect of CFO -  M yields non-coherent integration • Resulting composite relationship of CAC to ideal one Term  with N Term with M

13. Tradeoff Between N and M • Single frame processing quickly degrades with CFO • Multi-frame processing spreads the energy across SCO and CFO How to optimize M and N? Cyclic Feature Cyclic Feature Multiple Frames (M = 10) Single Frame (M=1)

14. Optimization Design Strategy • CFO and SCO are non-deterministic circuit impairments • Design strategy is to optimize the average cyclic SNR • Conditional cyclic SNR defined as • Derived in closed form and given by where , are functions of the pulse shape filter, N and M

15. Performance Gains over Conventional Feature Detectors • CFO and SCO zero mean normally distributed, Under what ratios can we expect performance gains?

16. Best and Worst Case Performance Scenarios Most gains achieved when CFO is more severe than SCO

17. Contributions to Cyclic Feature Detectors • Analyzed performance loss due to carrier and sampling offsets • Proposed a new multi-frame statistic that achieves robust detection with optimum (N,M) pair • Significant improvement obtained when

18. Outline • Cyclostationary feature detection overview • Spectrum sensing and classification under receiver impairments • Blind energy efficient sensing and classification • Impact of nonlinearities and their compensation • Summary of contributions • E. Rebeiz, F. Yuan, P. Urriza, D. Markovic, D. Cabric, Energy-Efficient Processor for Blind Signal Classification in Cognitive Radio Networks, to appear in IEEE Transactions on Circuits and Systems I • E. Rebeiz, D. Cabric, Blind Modulation Classification Based on Spectral Correlation and Its Robustness to Timing Mismatch, in Proc. IEEE Military Communications Conference, Nov. 2011 • E. Rebeiz, D. Cabric, Low Complexity Feature-based Modulation Classifier and its Non-Asymptotic Analysis, in Proc. IEEE Global Communications Conference, Dec. 2011

19. Blind Sensing and Modulation Classification • What do we mean by blind? • Signals can appear anywhere in the wideband channel • Signals are not standard compliant • Spectrum sensing performed via a cyclic frequency search • Cyclostationary detection is not robust to CFO • Number of CAC computations becomes a burden Cyclic detection not energy efficient in blind scenarios

20. Proposed Hybrid Detection and Classification Processor • Most processing is in estimating signal parameters • What accuracy is required in the pre-processor?

21. Design Strategy to Minimize Consumed Energy • Discretization required for implementation purposes ( • Total # CAC computations: , • All blocks use CAC  minimizing energy = minimizing # samples

22. Tradeoffs Between Pre-Processor Resolutionand Classification Accuracy • Feature at is the weakest among all features • determined by signals in class 1 (M-QAM, M-PSK) For every SNR, there is a maximum CFO tolerable to meet Pc

23. Feasible Region for Classification • All triplets that meet form the feasible region • Features at the carrier frequency are stronger: looser requirement

24. Optimum Operating Point in Feasible Region Operating near the boundary of feasible region is most efficient

25. Blind Modulation Classification Contributions • The tradeoff between parameter estimation and the resulting classification accuracy was analyzed • An optimization strategy has been developed to minimize the total consumed energy while meeting the classification requirement

26. Outline • Cyclostationary feature detection overview • Spectrum sensing and classification under receiver impairments • Blind energy efficient sensing and classification • Impact of nonlinearities and their compensation • Summary of contributions • E. Rebeiz, A. Shahed, M. Valkama, and D. Cabric, Analysis of Spectrum Sensing under RF Non-Linearities and Compensation Algorithm, in preparation for submission to IEEE Transactions on Signal Processing • E. Rebeiz, A. Shahed, M. Valkama, D. Cabric, Suppressing RF Front-End Nonlinearities in Wideband Spectrum Sensing, in Proc. IEEE CROWNCOM 2013

27. Wideband Receiver Nonlinearities • Typical LNA IIP3 of -10 dBm, LNA linear gain of 35 dB • Mixer nonlinearity are also important, but have less of an impact • IIP2 dBm, IIP3 dBm Def: IIP3 = Input power at which linear term power equals power of 3rd order term

28. Received Signal with Intermodulation Terms • SOI @ , blockers @ , such that • Due to nonlinearity, intermodulation (IMD) term appears at How does the IMD term affect the sensing performance?

29. Degradation Depends on Blocker Modulation • SIR = -67 dB, SNR = 10 dB, N = 500 Samples Cyclostationary Detection Energy Detection • Severe degradation in sensing performance What are possible compensation methods?

30. Increasing Sensing Time is not Effective • Recall that under • How do we set the decision threshold?

31. Impact of Uncertainties on False Alarm • Setting the threshold requires knowledge of • Blocker and noise power • Blocker modulation Accurate estimation of parameters needed for threshold setting • CD is more robust to uncertainties than ED • Receiver IIP3

32. Intermodulation Term Compensation Actual IMD term , Estimated IMD term Compensation method: • Performance of compensation is modulation dependent

33. Modulation Dependent Compensation Algorithm • Compensation requires • Modulation type of blockers • Blocker strength • Receiver IIP3

34. Performance Gains due to Demodulation / Remodulation • When estimates are off, residual term degrades detection performance

35. Nonlinearity Contributions • Analyzed the degradation in sensing performance due to IMD term • Showed that impact of IMD is dependent on blockers’ modulation • Proposed a modulation-aware IMD compensationbased on demodulation / remodulation + sample-by-sample subtraction

36. Ph.D. Thesis Contributions • Analyzed the practical challenges in wideband spectrum sensing and modulation classification • Proposed energy efficient algorithmic solutions that are robust to the considered impairments • Future work: analyze additional wideband challenges such as • High sampling rates • Mixer nonlinearities, I/Q mismatch • Blockers suppression through beamforming • Modulation classification of overlapped signals

37. Thank you very much!Questions? Acknowledgments DARPA CLASIC Program My advisor & all faculty on my committee Lab mates, most importantly Paulo & Fang-Li

38. Blind Estimation of Receiver IIP3 • Objective function given by Resulting IIP3 offset is 0.15 dB

39. Proposed Architecture for Uncompensated Detectors • This architecture makes sure that the uncompensated detectors are operating at the right point on the ROC point

40. Published Articles • Journal articles - E. Rebeiz, A. Shahed, M. Valkama, D. Cabric, Spectrum Sensing under RF Non-Linearities: Theoretical Analysis and Compensation Algorithm, in preparation to submission to IEEE Transactions on Signal Processing - E. Rebeiz, P. Urriza, D. Cabric, Optimizing Wideband Cyclostationary Spectrum Sensing under Receiver Impairments, in IEEE Transactions on Signal Processing, 2013 - E. Rebeiz, F. Yuan, P. Urriza, D. Markovic, D. Cabric, Energy-Efficient Processor for Blind Signal Classification in Cognitive Radio Networks, in IEEE Transactions on Circuits and Systems I, 2013 - P. Urriza, E. Rebeiz, P. Pawełczak, D. Čabrić, Computationally Efficient Modulation Level Classification Based on Probability Distribution Distance Functions, IEEE Communications Letters, 2011 - P. Urriza, E. Rebeiz, D. Cabric, Multiple Antenna Cyclostationary Spectrum Sensing Based on the Cyclic Correlation Significance Test, in IEEE Journal on Selected Areas in Communications, 2013 - P. Sofotasios, E. Rebeiz, L. Zhang, T. Tsiftsis, S. Freear, D. Cabric, Energy Detection-Based Spectrum Sensing over Generalized and Extreme Fading Channels, in IEEE Trans. Vehicular Technology, 2013 - P. Urriza, E. Rebeiz, D. Cabric, Optimal Discriminant Functions Based On Sampled Distribution Distance for Modulation Classification, accepted for publication in IEEE Communications Letters, 2013 • Selected Conference articles - E. Rebeiz, A. Shahed, M. Valkama, and D. Cabric, Suppressing RF Front-End Nonlinearities in Wideband Spectrum Sensing, in Proc.IEEE CROWNCOM, 2013 - E. Rebeiz, P. Urriza, D. Cabric, Experimental Analysis of Cyclostationary Detectors Under Cyclic Frequency Offsets, in Proc. IEEE Asilomar Conference on Signals, Systems and Computers,2012 - E. Rebeiz, V. Jain and D. Cabric, Cyclostationary-Based Low Complexity Wideband Spectrum Sensing using Compressive Sampling, in Proc. IEEE ICC, 2012 - E. Rebeiz, D. Cabric, Blind Modulation Classification Based on Spectral Correlation and Its Robustness to Timing Mismatch, in Proc. IEEE MILCOM, 2011 - E. Rebeiz, D. Cabric, Low Complexity Feature-based Modulation Classifier and its Non-Asymptotic Analysis, in Proc. IEEE GLOBECOM, 2011

41. Theoretical Derivation of IMD Impact on Sensing Performance

42. Maintaining a Constant False Alarm Rate • Typical ACI scenarios only require estimation of blocker strength • Setting the detection threshold requires • Knowledge of modulation of the blockers • Blockers’ power and the noise power • Accurate knowledge of parameter

43. Design Strategy of Compensation Algorithm • We model the imperfections as • , for • Residual IMD term is given by • Objective is to achieve