Deep reinforcement learning for resource constrained multiclass scheduling in wireless networks

Avranas, Apostolos; Ciblat, Philippe; Kountouris, Marios
IEEE Transactions on Machine Learning in Communications and Networking, 13 September 2023

The problem of multiclass scheduling in a dynamic wireless setting is considered here, where the available limited bandwidth resources are allocated to handle random service demand arrivals, belonging to different classes in terms of payload data request, delay tolerance, and importance/priority. In addition to heterogeneous traffic, another major challenge stems from random service rates due to time-varying wire-less communication channels. Existing scheduling and resource allocation approaches, ranging from simple greedy heuristics and constrained optimization to combinatorics, are tailored to specific network or application configuration and are usually suboptimal. On this account, we resort to deep reinforcement learning (DRL) and propose a distributional Deep Deterministic Policy Gradient (DDPG) algorithm combined with Deep Sets to tackle the aforementioned problem. Furthermore, we present a novel way to use a Dueling Network, which leads to further performance improvement. Our proposed algorithm is tested on both synthetic and real data, showing consistent gains against baseline methods from combinatorics and optimization, and state-of-the-art scheduling metrics. Our method can, for instance, achieve with 13% less power and bandwidth resources the same user satisfaction rate as a myopic algorithm using knapsack optimization.

Communication systems
Eurecom Ref:
© 2023 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
See also: