Deep Reinforcement Learning Based Energy-Efficient Design for STAR-IRS Assisted V2V Users

Vehicle-to-Vehicle communication (V2V-C) is a cutting-edge technology in the field of 6G networks that improves spectrum utilization and energy efficiency (EE). Despite the potential benefits, there are some considerable difficulties with V2V-C, such as cross-channel interference, co-channel interference and the demand for huge connectivity. To address these challenges, researchers have turned to simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-IRSs) as auxiliary devices to improve wireless network performance. These surfaces allow users on opposite sides to be served at the same time by sending and reflecting signals. However, the existing solution has been limited to either continuous or discrete spaces, limiting optimisation parameters to either continuous or discrete nature. To address these limitations, the proposed scheme use a hybrid space to optimise the EE of the network for the downlink STAR-IRS aided communication system in the presence of vehicle-to-vehicle pairs (V2VPs), allowing one parameter to be continuous and the other to be discrete. In this research work, the proposed scheme uses the parameterized deep Q-network (P-DQN) framework for estimating the beamforming vector and phase shift for EE optimisation. The results from the simulation demonstrate the efficacy of the system proposed by maximising the spectrum usage and energy efficiency.