Performance Analysis of Energy Detector in Relay Based Cognitive Radio Networks Saman Atapattu Chintha Tellambura Hai Jiang

Outline Introduction System model Detection analysis Upper bound ROC curves Conclusions

Radio Spectrum Primary user / license holder Occupancy of spectrum (below 1 GHz) is around 6~10%. Spectrum holes Spectrum under utilization Heavy Use Sparse Use Heavy Use Medium Use Less than 6-10% Occupancy

Cognitive Radio “A radio that can change its transmitter parameters based on the environment in which it operates”. Cognitive radio  Secondary network  Unlicensed users Spectrum Sensing…?

Spectrum Sensing Multipath fading & shadowing. Hidden terminal problem. PU should not be effected by secondary activities.  Reliability Decision based on the received signal H o = Primary user is absent (idle) H 0 : Y [n] = W [n] H 1 = Primary user is in operation (busy) H 1 : Y [n]= h X [n] + W [n]

Cooperative Spectrum Sensing (CCS) Mitigate multipath fading & shadowing by spatial diversity. Avoid hidden terminal problem. Shadowing Shadowed node Cooperative nodes Improve reliability and detection capability.

Sensing Techniques Matched filter: SU has a prior knowledge of the PU, coherent detection. Cyclostationary detection: PU exhibits strong cyclostationary properties. Covariance detection: the statistical covariance matrices of the signal and noise. Energy detection: the received signal strength.

Sensing Techniques Matched filter: SU has a prior knowledge of the PU, coherent detection. Cyclostationary detection: PU exhibits strong cyclostationary properties. Covariance detection: the statistical covariance matrices of the signal and noise. Energy detection: the received signal strength. Non-coherent Low complexity

Relay-based CCS Data fusion AF relaying in cooperative communications Relay Fixed gain (blind/semi blind) Variable gain Combining MRC/ SLC Filtering Energy detector Multipath fading Rayleigh/ Nakagami-m R i to CC (i=1, …, n) channel Orthogonal (TDMA) Relay links Relay links + Direct link System Model

Energy Detector Output is compared to the predefined threshold. Non-coherent, optimal, low signal processing. Binary hypothesis

Performance Metrics Test statistic False alarm probability: Detection probability:

Detection Analysis Detection: Average detection probability:

Detection Analysis Detection: Average detection probability: Contour integration: Residue theorem Moment generating function (MGF)

MGF Variable gain Fixed gain

Upper Bound for P d Case 1: Multiple-relay Case 2: Multiple-relay + Direct link SNR: MGF: Upper bound:  Case 1

n = 1 ROC curves for different number of cognitive relays (n) u=2, average SNR = 5 dB and fixed gain C=1.7

n = 1 ROC curves for different number of cognitive relays (n) u=2, average SNR = 5 dB and fixed gain C=1.7

n = 1, 2 ROC curves for different number of cognitive relays (n) u=2, average SNR = 5 dB and fixed gain C=1.7

n = 1, 2 ROC curves for different number of cognitive relays (n) u=2, average SNR = 5 dB and fixed gain C=1.7

n = 1, 2, 3, 4, 5 ROC curves for different number of cognitive relays (n) u=2, average SNR = 5 dB and fixed gain C=1.7

n = 1, 2, 3, 4, 5 ROC curves for different number of cognitive relays (n) u=2, average SNR = 5 dB and fixed gain C=1.7

Direct link SNR = -5 dB ROC curves for relay links + direct link u=2, average SNR = 5 dB and fixed gain C=1.7

Direct link SNR = -5, -3 dB ROC curves for relay links + direct link u=2, average SNR = 5 dB and fixed gain C=1.7

Direct link SNR = -5, -3, 0 dB ROC curves for relay links + direct link u=2, average SNR = 5 dB and fixed gain C=1.7

Direct link SNR = -5, -3, 0, 3 dB ROC curves for relay links + direct link u=2, average SNR = 5 dB and fixed gain C=1.7

Direct link SNR = -5, -3, 0, 3 dB n =1 n =3 ROC curves for relay links + direct link u=2, average SNR = 5 dB and fixed gain C=1.7

Conclusions The MGF of received SNR of the primary user’s signal is utilized to analyze the average detection probability. Tighter upper bound is derived. Sensing capability is increased with spatial diversity. Direct link has major impact of the detection capability. Analysis can be extended to multihop relaying.

References [1] S. Haykin, “Cognitive radio: Brain-empowered wireless communications,” IEEE J. Select. Areas Commun., vol. 23, no. 2, pp. 201–220, Feb [2] H. Jiang, L. Lai, R. Fan, and H. V. Poor, “Optimal selection of channel sensing order in cognitive radio,” IEEE Trans. Wireless Commun., vol. 8, no. 1, pp. 297– 307, Jan [3] J. N. Laneman, D. N. C. Tse, and G. W. Wornell, “Cooperative diversity in wireless networks: Efficient protocols and outage behavior,” IEEE Trans. Inform. Theory, vol. 50, no. 12, pp. 3062–3080, Dec [4] G. Ganesan and Y. Li, “Cooperative spectrum sensing in cognitive radio, part I: Two user networks,” IEEE Trans. Wireless Commun., vol. 6, no. 6, pp. 2204– 2213, June [5] F. F. Digham, M.-S. Alouini, and M. K. Simon, “On the energy detection of unknown signals over fading channels,” IEEE Trans. Commun., vol. 55, no. 1, pp , Jan [6] C. Tellambura, A. Annamalai, and V. K. Bhargava, “Closed form and infinite series solutions for the MGF of a dual-diversity selection combiner output in bivariate Nakagami fading,” IEEE Trans. Commun., vol. 51, no. 4, pp. 539–542, Apr

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