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.