This paper considers the resource allocation problem in the sixth generation (6G) wireless interference channel (IC) under link outage constraints. Since the optimization problem is non-convex in nature, existing approaches to find the optimal power allocation are computationally intensive. When it comes to internet of things (IoT) and massive machine-type communications (mMTC) in the emerging 6G, the computational complexity becomes practically infeasible. Recently, deep reinforcement learning has shown promising outcome in solving non-convex optimization problems with reduced complexity. In this paper, we utilize a deep Q-learning (DQL) approach which interacts with the wireless environment and learns the optimal power allocation of a wireless IC while maximizing overall sum-rate of the system and maintaining reliability requirement of each link. We have used two separate deep Q-networks to remove the inherent instability in learning process. Simulation results demonstrate that the proposed DQL approach outperforms existing geometric programming based solutions.
Deep Q-Learning Based Resource Allocation in 6G Interference Systems With Outage Constraints
01.06.2023
2194020 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch