Analysis of energy-efficient cooperative spectrum sensing with improved energy detectors and multiple antennas over Nakagami-q/n fading channels

Cognitive radio (CR) is an advanced technique used for the utilization of radio spectrum. A CR node uses the radio spectrum of a licensed user (called as primary user, PU) deviously. In this work, a cooperative sensing network (CSN) with improved energy detection (IED)-based CR nodes is proposed. Each CR node consists of multiple antennas and perform themselves selection diversity (SC) technique. Each CR node senses a PU through erroneous sensing channel and reports its sensing data (1-bit binary-decision) to the fusion center (FC) through erroneous reporting channels. At FC, existence of PU (presence or absence) is identified using k-out-of-N fusion rule. The novel expressions for detection probability over noise plus Nakagami-q/n fading channels are derived and the mathematical frameworks for analysis of throughput and energy-efficiency of the proposed network are developed. The simulation testbed is also developed and presented in this work to validate the analytically obtained performance characteristics. For several network parameters values, the investigation on comparison between IED and traditional energy detection method for various decision fusion rules is studied. Further, performance characteristics are analytically illustrated for throughput and energy-efficiency over noise plus Nakagami-q/n fading channels. The impact of diversity technique and effect of fading severity parameters on the throughput and energy-efficiency are also investigated. The effect of channel error on total error performance for both proposed and conventional networks is shown. Finally, optimal fusion rule and optimal network parameters values correspond to maximum throughput and maximum energy-efficiency are also identified.

Automated summary

This study introduces a cooperative sensing network with improved energy detection cognitive radio nodes. Mathematical expressions and simulation tests verify the network's performance characteristics in terms of throughput and energy efficiency, comparing different network parameters and decision fusion rules, as well as investigating the impact of diversity techniques and fading severity parameters on performance.