Demonstration of Real-time Spectrum Sensing for Cognitive Radio (Zhe Chen, Nan Guo, and Robert C. Qiu) Presenter: Zhe Chen Wireless Networking Systems Laboratory Department of Electrical and Computer Engineering Center for Manufacturing Research Tennessee Technological University Cookeville, Tennessee 38505 zchen42@tntech.edu http://iweb.tntech.edu/rqiu 1
Outline Background and Introduction Proposed Algorithm for Spectrum Sensing Implementation of the Proposed Algorithm on Hardware Platform Performance Evaluation Demonstration of Real-time Spectrum Sensing Conclusion 2
Cognitive Radio Cognitive Radio a technique of utilizing unused spectrums to communicate efficiently for secondary (unlicensed) users without interfering primary (licensed) users. Spectrum sensing a cornerstone function of cognitive radio, for detecting unused spectrum without interfering primary users, which is usually performed periodically. Unused Spectrum Segments Time SU2 SU1 Spectrum Segment Holes PU2 Used Spectrum Segments PU1 Frequency Primary user (PU) Secondary user (SU) 3
Challenges for Spectrum Sensing Algorithm A good spectrum sensing algorithm should offer high probability of detection (P D ) at low probability of false alarm (P FA ) for a wide range of signal-to-noise ratio (SNR). From a practical perspective, a good spectrum sensing algorithm has to be implementation friendly, including acceptable computational complexity. 4
Popular Off-the-Shelf Hardware Platforms for Cognitive Radio Small form factor (SFF) software defined radio (SDR) development platform (DP) The next generation universal software radio peripheral (USRP2) For more information about the two platforms, please refer to: "Towards A Real-time Cognitive Radio Network Testbed: Architecture, Hardware Platform, and Application to Smart Grid," 5 th IEEE Workshop on Networking Technologies for Software-Defined Radio and White Space, June, 2010. 5
Major Contributions of This Paper The platform employed in our research is real-time oriented compared to some of those reported. An FFT-based spectrum sensing algorithm (FAR), which is more implementation-friendly, is proposed in this paper. The decision threshold of FAR is insensitive to noise level. The relationships between the length of FFT, the length of averaging and the SNR are experimentally investigated. Both P D and P FA of the spectrum sensing algorithm are measured on hardware platform. A real-time spectrum sensing is demonstrated with controllable primary users (PUs). 6
The Proposed FAR Algorithm Input: baseband discrete-time signal Output: a series of vectors of two-class decisions that represent the availabilities of the channels in each time slot 7
Probability Discussions (1/2) The ratio is independent of noise level! The threshold of FAR algorithm 3.5 x 10-3 3 2.5 Sine signal + AWGN (SNR = -20 db) AWGN potential threshold 2 1.5 1 The pure-noise PDF does not change with noise level and SNR! 0.5 0 0 2 4 6 8 10 12 14 16 Ratio of PSD peak to PSD average 8
Length Discussions (2/2) Length of FFT and length of averaging 10 5 10 4 Length of FFT (when length of averaging = 1) Length of averaging (when length of FFT = 128) 8 x 10 4 Number of multiplications for FFT Number of required samples Relationship between N and T 10 3 6 4 10 2 2 10 1 10 0-25 -20-15 -10-5 0 SNR (db) 0 100 50 Length of averaging (T) 0 0 500 2000 1500 1000 Length of FFT (N) 2500 Both of the required lengths increase approximately exponentially as SNR goes down. Both the local minima of the required number of complex multiplications and the local minima of the required number of samples are achieved at (N, T)=(512,9), (1024,4), (1536,2) and (2048,2). 9
Implementation of the FAR algorithm on Hardware Platform Small form factor software defined radio development platform (SFF SDR DP) FAR algorithm (N = 128, T = 16) Signal analyzer SFF SDR DP Arbitrary waveform generator Family radio service (FRS) Digital phosphor oscilloscope 10
Performance Evaluation (1/2) Setup for performance evaluation Recorded FRS signals 11
Probability Performance Evaluation (2/2) Results 1 0.9 0.8 0.7 0.6-121 dbm/khz -124 dbm/khz -126 dbm/khz 1 0.9 0.8 0.7 0.6 P D 0.5 0.5 P FA P D 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 0 0.2 0.4 0.6 0.8 1 P FA The implemented FAR algorithm works well when the received maximum PSD is -121 dbm/khz or above. 0-128 -126-124 -122-120 -118-116 -114-112 Power (dbm/khz) With P FA close to zero, the FAR algorithm on the SFF SDR DP can achieve a high P D when the maximum PSD is -121 dbm/khz or above, noting that typical PSD of the received FRS signal is higher than our detection limit. 12
Real-time Demonstration (1/2) Layoff for real-time demonstration Setup for real-time demonstration 13
Sensed Channel State Amplitude of Rx Signal(V) Real-time Demonstration (2/2) Received signals and sensed channel states 0.05 0-0.05 0 5 10 15 20 25 30 35 40 Time (s) 1 0.8 0.6 0.4 0.2 0 0 5 10 15 20 25 30 35 40 Time (s) The sensed channel states match the recorded channel waveforms very well. 14
Conclusion FAR algorithm for spectrum sensing has been proposed. FAR algorithm is designed to compromise between the performance and implementation complexity. FAR algorithm has a constant threshold feature which is greatly in favor of blind sensing. Selection for major parameters of FAR algorithm has been discussed. Spectrum sensing receiver with FAR algorithm has been implemented on the SFF SDR DP and tested using real FRS signals. Performance evaluation shows that FAR algorithm is indeed effective. Real-time spectrum sensing with controllable PUs has been demonstrated. 15
Thank you! 16