Throughput-Aware Cooperative Reinforcement Learning for Adaptive Resource Allocation in Device-to-Device Communication

Abstract

Device-to-device (D2D) communication is an essential feature for the future cellular networks as it increases spectrum efficiency by reusing resources between cellular and D2D users. However, the performance of the overall system can degrade if there is no proper control over interferences produced by the D2D users. Efficient resource allocation among D2D User equipments (UE) in a cellular network is desirable since it helps to provide a suitable interference management system. In this paper, we propose a cooperative reinforcement learning algorithm for adaptive resource allocation, which contributes to improving system throughput. In order to avoid selfish devices, which try to increase the throughput independently, we consider cooperation between devices as promising approach to significantly improve the overall system throughput. We impose cooperation by sharing the value function/learned policies between devices and incorporating a neighboring factor. We incorporate the set of states with the appropriate number of system-defined variables, which increases the observation space and consequently improves the accuracy of the learning algorithm. Finally, we compare our work with existing distributed reinforcement learning and random allocation of resources. Simulation results show that the proposed resource allocation algorithm outperforms both existing methods while varying the number of D2D users and transmission power in terms of overall system throughput, as well as D2D throughput by proper Resource block (RB)-power level combination with fairness measure and improving the Quality of service (QoS) by efficient controlling of the interference level.

An efficient actor‐critic reinforcement learning for device‐to‐device communication underlaying sectored cellular network

“…The convergence property of the actor‐critic method is much better than critic‐only method . Critic‐only methods like Q‐learning and SARSA utilize a state‐action value function. Also, these methods do not have an explicit function for the estimation of the policy.…”

Cooperative Admission Control with Network Coding in 5G Underlying D2D-Satellite Communication

“…The result is increased performance in terms of data rate, robustness, delay, security, and energy consumption. The cooperative network coding then extended to the device to device (D2D) communication, which further increases the overall capacity and throughput of the network [2][3][4]. The utilization of D2D and cellular network in network coding decreases the packet recovery time, and meets the performance of cellular via network coding [5].…”

Reinforcement learning algorithm for 5G indoor device‐to‐device communications

“…The D2D communication is a gifted component for 5G because of its two innate advantages, ie, traffic off-loading and radio resource reusing 7 capabilities. 12 The other domains of reinforcement learning for D2D communication includes deep learning for data transmission, 13 adaptive power allocation, 14 and access control and management. 8 With 5G and D2D, the focus is on indoor communications, particularly its coverage and connectivity issues.…”