A Meta Multi-Objective Reinforcement Learning Framework For Non-Orthogonal, Age-Optimized Information Dissemination in Vehicular ‎Networks

This paper studies how to send fresh and efficient information in vehicular networks. A roadside unit ‎‎(RSU) updates vehicles about different events. The goal is to reduce the delay in receiving information ‎while also saving power during transmission. The system uses an innovative way of transmitting data. ‎It sends multiple messages at the same time using superposition. Vehicles cancel unwanted signals with ‎a technique called successive interference cancellation (SIC). This method helps to improve the efficiency of communication. The problem is complex because two objectives must be optimized. One is to ‎minimize the delay in information updates, and the other is to minimize the power needed to send ‎updates. This is a multi-objective problem that is difficult to solve using traditional methods. To ‎address this challenge, the paper uses reinforcement learning (RL). A deep Q-network (DQN) decides the ‎best way to decode messages, while a deep deterministic policy gradient (DDPG) model determines ‎the optimal power allocation. Each learning model trains separately for different cases, which increases ‎computational time and effort. Instead of training models separately, the paper proposes a meta-‎learning approach. This helps estimate good solutions quickly without the need for retraining every ‎time. The meta-model adapts with small updates, saving significant time and computational resources. ‎Simulation results demonstrate that the proposed method outperforms older approaches. It reduces ‎training time while still achieving high efficiency. Moreover, it provides a better balance between ‎formation freshness and power consumption. These improvements make it highly suitable for real-time ‎data sharing in vehicular networks. This research has practical implications for enhancing road safety ‎and smart transportation systems. By optimizing data dissemination it contributes to the development ‎of more reliable and efficient vehicular communication networks‎.