  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.
