Challenger-Based Combinatorial Bandits for Subcarrier Selection in OFDM Systems
This work addresses the challenge of online, measurement-efficient subcarrier selection for AI-enabled communication systems, representing an incremental improvement in domain-specific optimization.
The paper tackles the problem of efficiently identifying the top-m user-scheduling sets in multi-user MIMO downlink by framing it as a combinatorial pure-exploration problem in stochastic linear bandits, introducing a gap-index framework with champion and challenger arms that significantly reduces runtime and computation compared to state-of-the-art methods while maintaining high identification accuracy.
This paper investigates the identification of the top-m user-scheduling sets in multi-user MIMO downlink, which is cast as a combinatorial pure-exploration problem in stochastic linear bandits. Because the action space grows exponentially, exhaustive search is infeasible. We therefore adopt a linear utility model to enable efficient exploration and reliable selection of promising user subsets. We introduce a gap-index framework that maintains a shortlist of current estimates of champion arms (top-m sets) and a rotating shortlist of challenger arms that pose the greatest threat to the champions. This design focuses on measurements that yield the most informative gap-index-based comparisons, resulting in significant reductions in runtime and computation compared to state-of-the-art linear bandit methods, with high identification accuracy. The method also exposes a tunable trade-off between speed and accuracy. Simulations on a realistic OFDM downlink show that shortlist-driven pure exploration makes online, measurement-efficient subcarrier selection practical for AI-enabled communication systems.