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This correspondence considers the resource allocation problem in wirelessinterference channel (IC) under link outage constraints. Since the optimizationproblem is non-convex in nature, existing approaches to find the optimal powerallocation are computationally intensive and thus practically infeasible.Recently, deep reinforcement learning has shown promising outcome in solvingnon-convex optimization problems with reduced complexity. In thiscorrespondence, we utilize a deep Q-learning (DQL) approach which interactswith the wireless environment and learns the optimal power allocation of awireless IC while maximizing overall sum-rate of the system and maintainingreliability requirement of each link. We have used two separate deep Q-networksto remove the inherent instability in learning process. Simulation resultsdemonstrate that the proposed DQL approach outperforms existing geometricprogramming based solution.

Deep Q-Learning Based Resource Allocation in Interference Systems With Outage Constraint