Group-Based Multibit Cooperative Spectrum Sensing for Cognitive Radio Networks

In cooperative spectrum sensing (CSS), a multibit combination rule shows better sensing performance than one-bit hard combination rules at the sacrifice of the reporting overhead. To overcome the tradeoff between the sensing performance and the reporting overhead, we propose a novel group-based multibit CSS scheme with a limited reporting overhead. The proposed scheme adopts contention-based reporting to restrain the reporting overhead while achieving multiuser diversity with an increased number of secondary users (SUs). Moreover, SUs report one-bit sensing results to a fusion center instead of sending multibit quantization information, and the rest of the information is embedded in the time slot. The simulation results demonstrate that, as the number of SUs increases, the proposed scheme improves the sensing performance and the average throughput of SUs, whereas the conventional one-bit or multibit combination schemes show a tradeoff between the sensing performance and the throughput of SUs.

[1]  Erik G. Larsson,et al.  Spectrum Sensing for Cognitive Radio : State-of-the-Art and Recent Advances , 2012, IEEE Signal Processing Magazine.

[2]  Vijay K. Bhargava,et al.  Distributed Detection of Primary Signals in Fading Channels for Cognitive Radio Networks , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

[3]  Ying-Chang Liang,et al.  Optimization for Cooperative Sensing in Cognitive Radio Networks , 2007, 2007 IEEE Wireless Communications and Networking Conference.

[4]  Holger Karl,et al.  Quantization techniques for accurate soft message combining , 2012, 2012 IEEE Wireless Communications and Networking Conference (WCNC).

[5]  Philippe Ciblat,et al.  How Many Bits Should Be Reported In Quantized Cooperative Spectrum Sensing? , 2015, IEEE Wireless Communications Letters.

[6]  Sundeep Prabhakar Chepuri,et al.  Optimization of hard fusion based spectrum sensing for energy-constrained cognitive radio networks , 2013, Phys. Commun..

[7]  Feng Liu,et al.  A novel analytical scheme to compute the n-fold convolution of exponential-sum distribution functions , 2004, Appl. Math. Comput..

[8]  Zan Li,et al.  Efficient Soft Decision Fusion Rule in Cooperative Spectrum Sensing , 2013, IEEE Transactions on Signal Processing.

[9]  Geoffrey Ye Li,et al.  Soft Combination and Detection for Cooperative Spectrum Sensing in Cognitive Radio Networks , 2008, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[10]  Young-June Choi,et al.  Throughput analysis of cooperative spectrum sensing in Rayleigh-faded cognitive radio systems , 2012, IET Commun..

[11]  Hang Li,et al.  Throughput Analysis of Opportunistic Feedback for Downlink Multiuser Diversity with Capture Effect , 2012, IEEE Communications Letters.

[12]  A. Ghasemi,et al.  Collaborative spectrum sensing for opportunistic access in fading environments , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[13]  Liuqing Yang,et al.  Multi-bit cooperative spectrum sensing strategy in closed form , 2013, 2013 Asilomar Conference on Signals, Systems and Computers.

[14]  Qihui Wu,et al.  Kernel-Based Learning for Statistical Signal Processing in Cognitive Radio Networks: Theoretical Foundations, Example Applications, and Future Directions , 2013, IEEE Signal Processing Magazine.