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.


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    Title :

    Deep Q-Learning Based Resource Allocation in 6G Interference Systems With Outage Constraints


    Contributors:


    Publication date :

    2023-06-01


    Size :

    2194020 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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