Multi-User mmWave Beam and Rate Adaptation via Combinatorial Satisficing Bandits

TL;DR

This paper introduces SAT-CTS, a novel learning policy for multi-user mmWave systems that efficiently adapts beams and rates to meet quality-of-service targets, with proven regret bounds and practical benefits.

Contribution

It presents the first finite-time regret bounds for combinatorial semi-bandits with satisficing objectives and proposes a lightweight, threshold-aware policy for beam and rate adaptation.

Findings

SAT-CTS reduces satisficing regret effectively.

It maintains competitive standard regret and fairness.

Experiments show improved throughput and fairness across users.

Abstract

We study downlink beam and rate adaptation in a multi-user mmWave MISO system where multiple base stations (BSs), each using analog beamforming from finite codebooks, serve multiple single-antenna user equipments (UEs) with a unique beam per UE and discrete data transmission rates. BSs learn about transmission success based on ACK/NACK feedback. To encode service goals, we introduce a satisficing throughput threshold $\tau_r$ and cast joint beam and rate adaptation as a combinatorial semi-bandit over beam-rate tuples. Within this framework, we propose SAT-CTS, a lightweight, threshold-aware policy that blends conservative confidence estimates with posterior sampling, steering learning toward meeting $\tau_r$ rather than merely maximizing. Our main theoretical contribution provides the first finite-time regret bounds for combinatorial semi-bandits with satisficing objective: when $\tau_r$ is realizable, we upper bound the cumulative satisficing regret to the target with a time-independent constant, and when $\tau_r$ is non-realizable, we show that SAT-CTS incurs only a finite expected transient outside committed CTS rounds, after which its regret is governed by the sum of the regret contributions of restarted CTS rounds, yielding an $O((\log T)^2)$ standard regret bound. On the practical side, we evaluate the performance via cumulative satisficing regret to $\tau_r$ alongside standard regret and fairness. Experiments with time-varying sparse multipath channels show that SAT-CTS consistently reduces satisficing regret and maintains competitive standard regret, while achieving favorable average throughput and fairness across users, indicating that feedback-efficient learning can equitably allocate beams and rates to meet QoS targets without channel state knowledge.