Optimization Analysis of Improved Energy Detection based Cooperative Spectrum Sensing Network in Nakagami-m and Weibull Fading Channels

Abstract

In this paper, performance analysis for optimization of cooperative spectrum sensing (CSS) network in Nakagami-m and Weibull fading environments and comparison between them are presented. First, we derive the novel analytic expressions for probabilities of missed detection and false alarm for CSS network in both fading channels, assuming improved energy detector (IED) and selection combining (SC) diversity at each cognitive radios (CRs). Next, we optimize the network parameters such as number of CRs, energy detector arbitrary parameter, and normalized detection threshold at CR to obtain the optimal performance of CSS network. The impacts of several network parameters like: multiple antennas at each CR, number of CRs in CSS network, Nakagami and Weibull fading parameters, and sensing channel SNR on the performance of CSS network are investigated. The performance comparison between conventional and improved energy detectors has also been highlighted in this paper.

Cluster‐based distributed cooperative spectrum sensing over Nakagami fading using diversity reception

“…However, the work in [17] is limited to single CR user in non-cooperative scenario. An improved ED-based optimised CSS is proposed [18] over Nakagami and Weibull fading channel in [18] reporting channel is considered to be ideal. In [19], distributed and centralised CSS are compared over different fading channels.…”

Spectrum Sensing in Cognitive Radio Networks: Threshold Optimization and Analysis

“…However, the spectrum sensing performance of the energy detector is severely affected by destructive channel effects such as shadowing and fading, and noise. To minimize the negative effects caused by noise uncertainty and communication channel, the cooperative spectrum sensing model is defined in the literature [35], [36]. In [35], the researchers proposed a fuzzy logic-based perception format for collaborative energy detection, based on the new reliability factors for local spectrum sensing.…”

“…When these processes are performed, the reliability factor is defined by using the SNR, detection differences and threshold, and the detection performance is measured. In [36], energy detector parameters are optimized for the best detection performance. Simulation studies have been carried out on fading channels about the optimal threshold, several cognitive radio users, and the number of antennas.…”

Spectrum Sensing in Cognitive Radio Networks: Threshold Value Optimization and Analysis

“…Little change in power of noise can result in large decline in the performance of energy detector due to SNR thresholds. In order to minimize the negative effects caused by noise uncertainty, cooperative spectrum sensing model is defined in the literature [26], [27]. In [26], the researchers proposed a fuzzy logic-based perception format for collaborative energy detection, based on the new reliability factors for local spectrum sensing.…”

“…When these processes are performed, the reliability factor is defined by using the SNR, detection differences and threshold values, and the detection performance is measured. In [27], energy detector parameters are optimized for the best detection performance. Simulation studies have been carried out on fading channels in relation to the optimal threshold, number of cognitive radio users and number of antennas.…”