Multi-bit cooperative spectrum sensing strategy in closed form

Spectrum sensing is one of the most important tasks in cognitive radio system. In order to combat fading, cooperation among different sensing users is usually adopted. In our previous work [1], we quantified the performance gain of cooperative spectrum sensing by the notion of diversity. In addition, we have shown that even with local binary decisions, the cooperative spectrum sensing can achieve the maximum diversity by appropriately selecting local and fusion rules. However, there is a significant signal-to-noise ratio (SNR) loss compared with the soft information fusion scenario due to local binary quantization. Intuitively, increasing the number of bits of local quantization will improve the sensing performance. Most work in the literature on multi-bit cooperative sensing are mathematically intractable and can only be solved numerically with high complexity. In this paper, by jointly maximizing diversity and SNR gain, we provide a generalized multi-bit cooperative sensing strategy with the local and fusion decision rules in explicit closed form. Simulations show that even with small number of bits, our proposed cooperative sensing strategy can significantly improve the sensing performance.

[1]  H. Vincent Poor,et al.  Optimal Multiband Joint Detection for Spectrum Sensing in Cognitive Radio Networks , 2008, IEEE Transactions on Signal Processing.

[2]  Liuqing Yang,et al.  {Cooperative Spectrum Sensing with Ternary Local Decisions} , 2012, IEEE Communications Letters.

[3]  Shuguang Cui,et al.  Collaborative wideband sensing for cognitive radios , 2008, IEEE Signal Processing Magazine.

[4]  Danijela Cabric,et al.  Experimental study of spectrum sensing based on energy detection and network cooperation , 2006, TAPAS '06.

[5]  Jing Li,et al.  Tri-State Spectrum Sensing and Erasure-Injected Probabilistic Inference for Cognitive Radios , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.

[6]  Liuqing Yang,et al.  Cooperative Diversity of Spectrum Sensing for Cognitive Radio Systems , 2010, IEEE Transactions on Signal Processing.

[7]  Geoffrey Ye Li,et al.  Cooperative Spectrum Sensing in Cognitive Radio, Part II: Multiuser Networks , 2007, IEEE Transactions on Wireless Communications.

[8]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[9]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[10]  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.

[11]  Liuqing Yang,et al.  Detection-interference dilemma for spectrum sensing in cognitive radio systems , 2009, MILCOM 2009 - 2009 IEEE Military Communications Conference.

[12]  Hyundong Shin,et al.  Cognitive Network Interference , 2011, IEEE Journal on Selected Areas in Communications.

[13]  V. Tarokh,et al.  Cognitive Sensing Based on Side Information , 2008, 2008 IEEE Sarnoff Symposium.

[14]  Liuqing Yang,et al.  Large deviation solution for cooperative spectrum sensing with diversity analysis , 2013, 2013 IEEE Global Communications Conference (GLOBECOM).

[15]  Liuqing Yang,et al.  The optimal fusion rule for cooperative spectrum sensing from a diversity perspective , 2012, 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR).