Bio-Inspired Optimization Algorithm in Optimum Relay Selection for Cooperative Transmission Networks

Abstract

Multipath propagation, which causes channel fading, is one of the significant flaws in wireless network signal transmission. Cooperative Communication (CC) is a technique for reducing the impact of channel fading by leveraging the diversity gain made available by collaborating with several nodes and/or relays. Because of the size and complexity limits of wireless mobile devices, nodes with more than one transmitting antenna are uncommon. The scenario entails investigating a network that includes a sender, a receiver, and a relay. Two common CC mechanisms for increasing transmission reliability are amplifying and forward and decoding and forward. Relay nodes gather, process, and retransmit data to improve the signal-to-noise ratio (SNR) at the receiving end. To improve the overall efficiency of the system, we propose bio-inspired systems in which two parties exchange data via distinct relay nodes. The Artificial Immune System Algorithm (AIS) and Bacterial Foraging Optimization (BFO) are used in this study to provide a strategy for identifying the most effective relay to employ in CC. The Relay Selection (RS) technique considers criteria such as outage likelihood, collision risk, Bit Error Rate (BER), and energy utilization during CC. To validate the effectiveness of both techniques, delay, Packet Delivery Ratio (PDR), Residual Energy (RE), and outage probability are employed.