Joint Deep Reinforcement Learning and Unsupervised Learning for Channel Selection and Power Control in D2D Networks

Abstract

Device-to-device (D2D) technology enables direct communication between devices, which can effectively solve the problem of insufficient spectrum resources in 5G communication technology. Since the channels are shared among multiple D2D user pairs, it may lead to serious interference between D2D user pairs. In order to reduce interference, effectively increase network capacity, and improve wireless spectrum utilization, this paper proposed a distributed resource allocation algorithm with the joint of a deep Q network (DQN) and an unsupervised learning network. Firstly, a DQN algorithm was constructed to solve the channel allocation in the dynamic and unknown environment in a distributed manner. Then, a deep power control neural network with the unsupervised learning strategy was constructed to output an optimized channel power control scheme to maximize the spectrum transmit sum-rate through the corresponding constraint processing. As opposed to traditional centralized approaches that require the collection of instantaneous global network information, the algorithm proposed in this paper used each transmitter as a learning agent to make channel selection and power control through a small amount of state information collected locally. The simulation results showed that the proposed algorithm was more effective in increasing the convergence speed and maximizing the transmit sum-rate than other traditional centralized and distributed algorithms.

Joint DDPG and Unsupervised Learning for Channel Allocation and Power Control in Centralized Wireless Cellular Networks

“…It is well known that the deep reinforcement learning has excellent abilities in environment interaction and dynamic perception. In the dynamic wireless network environments, the agent can learn through interaction with the environments [11], [12]. Currently, the deep Q network (DQN), as a research hotspot in deep reinforcement learning, has been widely used for the resource allocation in wireless communications.…”

D2D Communication Network Interference Coordination Scheme Based on Improved Stackelberg

“…According to the statistics, it is estimated that, in 2040, the number of intelligent terminal connections will increase by more than 30 times compared with 2022, and the average monthly traffic will increase by more than 130 times [5]. Finally, in the 6G era, there will be an Internet market of "hundreds the whole wireless communication system and reduces the computing pressure of the base station to a certain extent, while improving the spectrum resource utilization and throughput of the system [11].…”