wideband cyclostationary spectrum sensing and modulation classification n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Wideband Cyclostationary Spectrum Sensing and Modulation Classification PowerPoint Presentation
Download Presentation
Wideband Cyclostationary Spectrum Sensing and Modulation Classification

Loading in 2 Seconds...

play fullscreen
1 / 43

Wideband Cyclostationary Spectrum Sensing and Modulation Classification - PowerPoint PPT Presentation


  • 65 Views
  • Uploaded on

Ph.D. Defense 08/19/2013. Wideband Cyclostationary Spectrum Sensing and Modulation Classification. Eric Rebeiz Advisor: Prof. Danijela Cabric UCLA Electrical Engineering Department. Wideband Cognitive Radio Concept & Vision.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

Wideband Cyclostationary Spectrum Sensing and Modulation Classification


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
wideband cyclostationary spectrum sensing and modulation classification

Ph.D. Defense

08/19/2013

Wideband Cyclostationary Spectrum Sensing and Modulation Classification

Eric Rebeiz

Advisor: Prof. DanijelaCabric

UCLA Electrical Engineering Department

wideband cognitive radio concept vision
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

wideband spectrum sensing classification design
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
wideband sensing classification challenges
Wideband Sensing & Classification Challenges
  • Robust sensing and classification to impairments
  • Short sensing time
  • Energy efficiency
  • High computational complexity
  • Carrier & sampling offsets
  • Front-end nonlinearities
research contributions
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
outline
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
cyclostationary detection cd overview
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

cyclic features of different modulation types
Cyclic Features of Different Modulation Types

Modulation type determines the present cyclic features

Features at functions of and

outline1
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
feature detection under receiver impairments
Feature Detection Under Receiver Impairments

What is the impact on sensing and classification?

signification degradation in low snr
Signification Degradation in Low SNR
  • Ideal feature computed by
  • Feature under cyclic offset computed at
robust feature detection
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

tradeoff between n and m
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)

optimization design strategy
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

performance gains over conventional feature detectors
Performance Gains over Conventional Feature Detectors
  • CFO and SCO zero mean normally distributed,

Under what ratios can we expect performance gains?

best and worst case performance scenarios
Best and Worst Case Performance Scenarios

Most gains achieved when CFO is more severe than SCO

contributions to cyclic feature detectors
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
outline2
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
blind sensing and modulation classification
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

proposed hybrid detection and classification processor
Proposed Hybrid Detection and Classification Processor
  • Most processing is in estimating signal parameters
    • What accuracy is required in the pre-processor?
design strategy to minimize consumed energy
Design Strategy to Minimize Consumed Energy
  • Discretization required for implementation purposes (
    • Total # CAC computations: ,
  • All blocks use CAC  minimizing energy = minimizing # samples
tradeoffs between pre processor resolution and classification accuracy
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

feasible region for classification
Feasible Region for Classification
  • All triplets that meet form the feasible region
  • Features at the carrier frequency are stronger: looser requirement
optimum operating point in feasible region
Optimum Operating Point in Feasible Region

Operating near the boundary of feasible region is most efficient

blind modulation classification contributions
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
outline3
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
wideband receiver nonlinearities
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

received signal with intermodulation terms
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?

degradation depends on blocker modulation
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?

increasing sensing time is not effective
Increasing Sensing Time is not Effective
  • Recall that under
    • How do we set the decision threshold?
impact of uncertainties on false alarm
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
intermodulation term compensation
Intermodulation Term Compensation

Actual IMD term , Estimated IMD term

Compensation method:

  • Performance of compensation is modulation dependent
modulation dependent compensation algorithm
Modulation Dependent Compensation Algorithm
  • Compensation requires
    • Modulation type of blockers
    • Blocker strength
  • Receiver IIP3
performance gains due to demodulation remodulation
Performance Gains due to Demodulation / Remodulation
  • When estimates are off, residual term degrades detection performance
nonlinearity contributions
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
ph d thesis contributions
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
thank you very much questions

Thank you very much!Questions?

Acknowledgments

DARPA CLASIC Program

My advisor & all faculty on my committee

Lab mates, most importantly Paulo & Fang-Li

blind estimation of receiver iip3
Blind Estimation of Receiver IIP3
  • Objective function given by

Resulting IIP3 offset is 0.15 dB

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

maintaining a constant false alarm rate
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
design strategy of compensation algorithm
Design Strategy of Compensation Algorithm
  • We model the imperfections as
    • , for
  • Residual IMD term is given by
  • Objective is to achieve