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 $n$ *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.

中文翻译：

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在级联瑞利衰落信道上使用基于机器学习的功率分配改进 S-AF 和 S-DF 中继方案

我们研究了具有中继选择策略的双跳车辆间通信 (IVC) 系统的性能。我们假设一个广义的衰落信道模型，称为级联瑞利（也称为 $n$ *Rayleigh)，其中涉及到的乘积 $n$ 独立的瑞利随机变量。与更适合蜂窝网络的传统瑞利衰落假设相比，该信道模型提供了对 IVC 的真实描述。与现有工作不同，现有工作主要考虑双瑞利衰落信道（即， $n=2$ ); 我们的系统模型考虑了一般的级联顺序 $n$ ，为此我们推导出所考虑场景下停电概率的近似解析解。此外，在本研究中，我们提出了一种基于机器学习的功率分配方案，以提高 IVC 中的链路可靠性。分析和仿真结果表明，选择性解码转发 (S-DF) 和放大转发 (S-AF) 中继方案在高信噪比状态下具有相同的分集阶数。此外，我们的结果表明机器学习算法可以在选择最佳中继和传输功率分配方面发挥核心作用。