Decentralized V2V Power Control Under Rain-Induced Channel Nonstationarity and Interference
Keywords:
Vehicle-to-Vehicle Communication, Power Control, Rain Attenuation, Channel Nonstationarity, Interference ManagementAbstract
The rapid deployment of Intelligent Transportation Systems heavily relies on reliable Vehicle-to-Vehicle communication networks. To support the immense data rate requirements of autonomous driving and advanced driver assistance systems, communication protocols are increasingly shifting toward millimeter-wave frequency bands. However, these high-frequency channels are uniquely susceptible to meteorological phenomena, particularly rain attenuation, which introduces severe channel nonstationarity and degrades communication reliability. Furthermore, in dense vehicular environments, uncoordinated simultaneous transmissions lead to crippling co-channel interference. This paper investigates the problem of decentralized power control in Vehicle-to-Vehicle networks operating under rain-induced channel nonstationarity and severe interference. We propose a fully decentralized framework that allows individual vehicles to autonomously adjust their transmission power based on local observations of channel states and estimated meteorological impacts. By characterizing the rain attenuation through established meteorological models and integrating it into a time-varying interference topology, we formulate a multi-objective optimization problem aimed at maximizing system sum-rate while satisfying stringent reliability and latency constraints. Due to the lack of centralized infrastructure in many critical vehicular scenarios, we leverage a multi-agent distributed learning approach to navigate the highly dynamic environment. Through comprehensive analytical modeling and rigorous simulation across various rainfall intensities and vehicular densities, this study demonstrates that the proposed decentralized power control mechanism significantly mitigates interference and maintains robust link connectivity even under heavy precipitation.
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