Power Control in Device-to-Device Communications using Deep Deterministic Policy Gradient Method

Device-to-device communications have been identified as a key contributor to addressing the requirements for video signal streaming in cognitive radio networks. Here, our primary objective is to optimize the power distribution for video transmitted signals in view of minimal interference. There are certain difficulties, such as distributing power with the least amount of interference. To deal with these issues, we proposed a Deep Deterministic Policy Gradient (DDPG) based approach to optimize the power control across Device-to-Device Networks. The proposed method seeks to reduce power utilization while enhancing the overall quality of received video signals. This helps us to achieve the best Signal to Interference Noise Ratio (SINR) without compromising the video quality of user devices. To substantiate our claim, The simulation results show that the proposed method outperforms the DQN-based method.