Energy-Efficient Resource Allocation for V2X Communications

The high mobility of automobiles causes channel estimation uncertainties in vehicle-to-vehicle (V2V) communications. Moreover, scarce spectrum resources further bottlenecked the quality of service (QoS) in vehicular communication networks. Non-orthogonal multiple access (NOMA) technique is introduced to solve these problems, which reuses resource blocks (RBs) to improve the network’s throughput and reduce latency with exploiting the power domain. In this paper, we devise a multi-agent reinforcement learning (MARL) based resource allocation method for roadside units (RSUs) in vehicular communication networks. A joint sub-band scheduling and transmit power allocation problem is investigated, aiming to find rational and reasonable solutions at each RSU, under QoS constraints and power limits, from a global perspective. Due to the complicated structure of this high-dimensional optimization, it is extremely challenging to accomplish in polynomial-time. The proposed method adopts MARL technique in RSUs to actualize collaboration and self-learning. RSUs act as agents, collectively interacting with the environment to maximize global energy efficiency, the ratio of the sum rate received at vehicles to the total power consumption of relevant RSUs, by trading-off between transmission rate and link interference. With distinctive observations and relevant feedback rewards, each agent learns to improve spectrum and power allocation by updating Q-networks using the gained experiences. The proposed method has much less complexity compared to the centralized implemented method, but still provides approximate performances. Simulation results demonstrate that the proposed method outperforms two existing baselines and a state-of-the-art power allocation scheme in terms of both average energy efficiency and probability of failure.