An antenna selection scheme with MRE and AWC for decision fusion in cognitive radio. Issue 10 (8th March 2017) - Jacob, Jaison; Jose, Babita R; Mathew, Jimson
Abstract: Spectrum sensing is an important task for the existence of cognitive radio. Accuracy of sensing is essential to eliminate interference to primary users. In a practical scenario, primary users may change its position and power frequently. A new scenario that may challenge cooperative spectrum sensing is considered here. An antenna selection scheme with multiple regions encoding for single node sensing and an adaptive weighted combining fusion rule for decision fusion are proposed to manage this scenario. The performance of the rule was analyzed in the new scenario under Rayleigh fading and compared with other fusion rules in the literature namely soft optimized linear combining (SOLC), soft equal weight linear combining, AND, OR, and voting. Probability of detection of the proposed approach was found better than SOLC (best among the rest) at low average SNR. Simulations showed that the probability of detection of proposed approach was nearly 10 % better than SOLC at probability of false alarm P f =0.01 and gradually became equal at P f =0.15, and it maintains it as P f goes up. Computational complexity of these approaches were measured as the time consumed in making a decision, and it was found that the proposed approach consumed only 20 % of the time than that of SOLC approach. Effect of number of neighbors on the performance of these approaches were also analyzed. Adaptive nature of this rule makes it suitable to perform well in any scenario under distributed sensing. Abstract : This article proposes an antenna selection scheme with multiple regions encoding for single‐node sensing and an adaptive‐weighted combining fusion rule for decision fusion in cognitive radio networks. This method is able to make right spectrum decisions in a scenario where the cognitive radio terminals are located around the boundary of a primary user. Higher probability of detection and lower computational time make this method superior to other optimised approaches used for decision fusion.