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

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

3. 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)

4. 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.

5. 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]

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

7. 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

8. 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].

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

10. 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:

11. Results
ROC curves
(SNR=5dB)

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

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

14. 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.

15. Reference
S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE JSAC, vol. 23, no. 2, pp. 201–220, Feb
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
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
S. Atapattu, C. Tellambura, and H. Jiang, “Relay based cooperative spectrum sensing in cognitive radio networks,” in IEEE Global Telecommn. Conf. (GLOBECOM), Dec

16. Thank You !