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Guaranteeing stable connectivity for emergency communications in rural and remote areas (e.g., to transfer high-quality video for remote surgery) has been challenging for any cellular network generation. With many initiatives proposed to expand the network coverage in 6 G, emergency communications are supposed to be enhanced significantly. For example, with line-of-sight propagation and high mobility advantages, aerial-assisted vehicular networks are expected to be key technologies in 6 G to enable broadband connectivity from the sky to emergency vehicles in rescue missions. However, deploying a group of low-altitude short = ABS, long=Aerial Base Stations (ABSs) to provide connectivity for emergency vehicles remains an open issue due to the challenge of maintaining the trade-off between connectivity quality guarantee and efficient flights. This work presents a hybrid Deep Reinforcement Learning-based scheme with Accumulative Training, namely EVRELAY, to address the problem. Based on a pre-trained signal map, the system can provide the best trajectories for deploying high-capability ABSs. Each DRL agent can dynamically adjust the ABS's movements to serve the maximum number of EVs based on the average signal power, remaining capacity (available data rate and energy), and collision avoidance with surrounding obstacles. The simulation results show that our method can maintain the highest overall connectivity coverage and data rate, 7% and 25% better than the existing methods while maintaining 10% lower energy consumption. The system is particularly efficient in large-scale deployment scenarios with many emergency vehicles departing simultaneously.

Cellular vehicle-to-everything (C-V2X) has been continuously evolving since Release 14 of the 3rd Generation Partnership Project (3GPP) for future autonomous vehicles. Apart from automotive safety, 5G NR further bring new capabilities to C-V2X for autonomous driving, such as real-time local update, and coordinated driving. These capabilities rely on the provision of low latency and high reliability from 5G NR. Among them, a basic demand is broadcasting or multicasting environment update messages, such as cooperative perception data, with high reliability and low latency from a Road Side Unit (RSU) or a base station (BS). In other words, broadcasting multiple types of automotive messages with high reliability and low latency is one of the key issues in 5G NR C-V2X. In this work, we consider how to select Modulation and Coding Scheme (MCS), RSU/BS, Forward Error Correction (FEC) code rate, to maximize the system utility, which is a function of message delivery reliability. We formulate the optimization problem as a nonlinear integer programming problem. Since the optimization problem is NP-hard, we propose an approximation algorithm, referred to as the Hyperbolic Successive Convex Approximation (HSCA) algorithm, which uses the successive convex approximation to find the optimal solution. In our simulations, we compare the performance of HSCA with those of three algorithms respectively, including the baseline algorithm, the heuristic algorithm, and the optimal solution. Our simulation results show that HSCA outperforms the baseline and the heuristic algorithms and is very competitive to the optimal solution.