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

arXiv: https://arxiv.org/abs/2506.16074 · HTML: https://mlsanity.com/paper/2506.16074/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 19 Jun 2025.

## Builds on (most cited first, 8 of 8)

- [Constrained Policy Optimization](https://mlsanity.com/paper/1705.10528/) (2017)
- [Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks](https://mlsanity.com/paper/1808.00490/) (2018)
- [A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation](https://mlsanity.com/paper/1912.04511/) (2019)
- [Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems](https://mlsanity.com/paper/2006.08099/) (2020)
- [CLARA: A Constrained Reinforcement Learning Based Resource Allocation Framework for Network Slicing](https://mlsanity.com/paper/2111.08397/) (2021)
- [Successive Convex Approximation Based Off-Policy Optimization for Constrained Reinforcement Learning](https://mlsanity.com/paper/2105.12545/) (2021)
- [Generalizable Resource Scaling of 5G Slices using Constrained Reinforcement Learning](https://mlsanity.com/paper/2306.09290/) (2023)
- [Rethinking WMMSE: Can Its Complexity Scale Linearly With the Number of BS Antennas?](https://mlsanity.com/paper/2205.06225/) (2022)

## Abstract (arXiv)

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.