Performance of Energy Detection: A Complementary AUC Approach Saman Atapattu, Chintha Tellambura & Hai Jiang Electrical and Computer Engineering University of Alberta CANADA GLOBECOM 2010

Outline Introduction Research work Analysis Results Spectrum sensing Energy detection Research work Cooperative spectrum sensing Analysis Results

Spectrum Sensing Cognitive radio: environment awareness & spectrum intelligence [1]. Dynamic spectrum access Spectrum sensing Spectrum sensing: to identify the spectrum holes. Cooperative spectrum sensing: to mitigate multipath fading, shadowing/hidden terminal problem. busy Idle (spectrum hole)

Spectrum Sensing Primary user has two states, idle or busy. Noise Noise + signal Binary Hypothesis: Performance metrics: False alarm (Pf): efficiency Missed-detection (Pm): reliability Detection (Pd): 1-Pm Higher Pd (lower Pm) and lower Pf are preferred.

Spectrum Sensing Techniques Matched Filter Perfect knowledge Dedicated receiver structure Eigenvalue Detection Max-Min eigenvalues Computational complexity Difficulty of threshold selection Cyclostationary Detection Cyclostationary property High sampling rate Complex processing algorithm Energy Detection [2]

Energy Detection Energy of the received signal. Digital implementation: Test statistic: Noise (AWGN), Signal (deterministic/random), Channel. Compared with threshold.

Performance Measurements Average Pd: Pd vs. SNR ROC (receiver operating characteristic) curve: Pd vs. Pf AUC (area under ROC curve) [3]: probability that choosing correct decision is more likely than choosing incorrect decision. AUC vs. SNR

Research Work Complementary AUC (CAUC) System Model Area under the complementary ROC (Pm vs Pf) CAUC = 1-AUC, varies from 0.5 to 0 Good representation for diversity order System Model Data fusion strategy AF relaying Square-law combining (SLC) Rayleigh fading ROC analysis in [4].

Analysis AUC for instantaneous SNR in [3]. CAUC: Average CAUC: where

Results Average CAUC for relay based-cooperative spectrum sensing network. easy to extend for diversity techniques. Sensing Diversity Order: For high SNR Without direct path: With direct path: Nakagami-m fading: Diversity techniques:

Results ROC curves (SNR=5dB)

Results CAUC curves semi-log scale log-log scale (SNR=5dB)

Results CAUC curves Nakagami-m Diversity techniques (SNR=5dB)

Contribution Introduced Complementary Area under ROC Curve (CAUC) Derived CAUC for relay-based cooperative spectrum sensing network. Showed that Diversity order: Cooperative network: n or (n+1) Nakagami fading: m Diversity techniques: L Proposed methodology and results can be useful for other wireless research topics.

Reference S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE JSAC, vol. 23, no. 2, pp. 201–220, Feb. 2005. 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. 21–24, Jan. 2007. S. Atapattu, C. Tellambura, and H. Jiang, “Analysis of area under the ROC curve of energy detection,” IEEE Trans. Wireless Commun., vol. 9, no. 3, pp. 1216–1225, Mar. 2010. S. Atapattu, C. Tellambura, and H. Jiang, “Relay based cooperative spectrum sensing in cognitive radio networks,” in IEEE Global Telecommn. Conf. (GLOBECOM), Dec. 2009.

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