Joint User Priority and Power Scheduling for QoS-Aware WMMSE Precoding: A Constrained-Actor Attentive-Critic Approach

TL;DR

This paper introduces a novel reinforcement learning algorithm, CAAC, that dynamically optimizes user priorities and power allocation in WMMSE precoding for 6G networks, improving energy efficiency and QoS adherence.

Contribution

The paper presents a new constrained reinforcement learning approach, CAAC, integrating CSSCA and attention-based Q-networks for adaptive, efficient WMMSE precoding in 6G networks.

Findings

CAAC outperforms baseline methods in energy efficiency.

CAAC achieves higher QoS satisfaction levels.

The approach demonstrates improved learning efficiency and adaptability.

Abstract

6G wireless networks are expected to support diverse quality-of-service (QoS) demands while maintaining high energy efficiency. Weighted Minimum Mean Square Error (WMMSE) precoding with fixed user priorities and transmit power is widely recognized for enhancing overall system performance but lacks flexibility to adapt to user-specific QoS requirements and time-varying channel conditions. To address this, we propose a novel constrained reinforcement learning (CRL) algorithm, Constrained-Actor Attentive-Critic (CAAC), which uses a policy network to dynamically allocate user priorities and power for WMMSE precoding. Specifically, CAAC integrates a Constrained Stochastic Successive Convex Approximation (CSSCA) method to optimize the policy, enabling more effective handling of energy efficiency goals and satisfaction of stochastic non-convex QoS constraints compared to traditional and existing CRL methods. Moreover, CAAC employs lightweight attention-enhanced Q-networks to evaluate policy updates without prior environment model knowledge. The network architecture not only enhances representational capacity but also boosts learning efficiency. Simulation results show that CAAC outperforms baselines in both energy efficiency and QoS satisfaction.