In-band spectrum sensing in cognitive radio networks: energy detection or feature detection?
Proceedings of the 14th ACM international conference on Mobile computing and networking
Optimal multiband joint detection for spectrum sensing in cognitive radio networks
IEEE Transactions on Signal Processing
Spectrum Sensing Framework for Cognitive Radio Networks
Wireless Personal Communications: An International Journal
Approximating a Sum of Random Variables with a Lognormal
IEEE Transactions on Wireless Communications
Soft Combination and Detection for Cooperative Spectrum Sensing in Cognitive Radio Networks
IEEE Transactions on Wireless Communications - Part 2
IEEE Transactions on Wireless Communications - Part 1
Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks
IEEE Transactions on Wireless Communications
IEEE Transactions on Wireless Communications
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
Cyclostationary Signatures in Practical Cognitive Radio Applications
IEEE Journal on Selected Areas in Communications
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The objective of cooperative spectrum sensing is to collaboratively detect the presence of the primary user by the aid of multiple secondary users. It is known that the performance of such a framework substantially depends on the fading assumption. In this paper, we propose an advanced framework for linear cooperative spectrum sensing in cognitive radio networks over correlated log-normal shadow fading channels. Considering the realistic sensing and reporting channels which are not addressed in similar works, motivates us to propose a novel approximation for correlated log-normal sum based on moment generating function calculation and moment matching method. Furthermore, the linear cooperative spectrum sensing coefficients are computed based on the optimization of the deflection criterion. This results in a framework with reasonable complexity which is suitable for practical applications. Simulation results show the excellent agreement between the exact and approximated statistics and the superior performance compared with conventional equally gain combiner.