Improved S-AF and S-DF Relaying Schemes using Machine Learning based
Power Allocation over Cascaded Rayleigh Fading Channels
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.