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We investigate the performance of a dual-hop inter-vehicular communications (IVC) system with relay selection strategy. We assume a generalized fading channel model, known as cascaded Rayleigh (also called <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>&#x002A;Rayleigh), which involves the product of <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> 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, <inline-formula> <tex-math notation="LaTeX">$n=2$ </tex-math></inline-formula>); our system model considers the general cascading order <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>, 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.

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