Improved S-AF and S-DF Relaying Schemes Using Machine Learning Based Power Allocation Over Cascaded Rayleigh Fading Channels

TL;DR

This paper analyzes dual-hop vehicular communication systems over cascaded Rayleigh channels, deriving outage probability and proposing machine learning-based power allocation to enhance reliability and relay selection.

Contribution

It introduces a generalized model for cascaded Rayleigh fading channels and develops a machine learning approach for optimal relay selection and power allocation.

Findings

Both S-DF and S-AF schemes achieve the same diversity order at high SNR.

Machine learning effectively improves relay selection and power distribution.

Analytical and simulation results validate the proposed methods.

Abstract

We investigate the performance of a dual-hop intervehicular communications (IVC) system with relay selection strategy. We assume a generalized fading channel model, known as cascaded Rayleigh (also called n*Rayleigh), which involves the product of n independent Rayleigh random variables. This channel model provides a realistic description of IVC, in contrast to the conventional Rayleigh fading assumption, which is more suitable for cellular networks. Unlike existing works, which mainly consider double-Rayleigh fading channels (i.e, n = 2); our system model considers the general cascading order n, for which we derive an approximate analytic solution for the outage probability under the considered scenario. Also, in this study we propose a machine learning-based power allocation scheme to improve the link reliability in IVC. The analytical and simulation results show that both selective decode-and-forward (S-DF) and amplify-and-forward (S-AF) relaying schemes have the same diversity order in the high signal-to-noise ratio regime. In addition, our results indicate that machine learning algorithms can play a central role in selecting the best relay and allocation of transmission power.