5 papers
Fused Constrained Policy Reuse Optimization for Wireless Resource Allocation
An Liu, Zheyuan Zhou, Kexuan Wang
Deep reinforcement learning (DRL) has been widely adopted for wireless resource allocation due to its model-free adaptability. However, online exploration is costly, as randomly in…
Joint User Priority and Power Scheduling for QoS-Aware WMMSE Precoding: A Constrained-Actor Attentive-Critic Approach
Kexuan Wang, An Liu
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…
A Lightweight RL-Driven Deep Unfolding Network for Robust WMMSE Precoding in Massive MU-MIMO-OFDM Systems
Kexuan Wang, An Liu
Weighted Minimum Mean Square Error (WMMSE) precoding is widely recognized for its near-optimal weighted sum rate performance. However, its practical deployment in massive multi-use…
A Hybrid Reinforcement Learning Framework for Hard Latency Constrained Resource Scheduling
Luyuan Zhang, An Liu, Kexuan Wang
In the forthcoming 6G era, extend reality (XR) has been regarded as an emerging application for ultra-reliable and low latency communications (URLLC) with new traffic characteristi…
Context-aware Constrained Reinforcement Learning Based Energy-Efficient Power Scheduling for Non-stationary XR Data Traffic
Kexuan Wang, An Liu
In XR downlink transmission, energy-efficient power scheduling (EEPS) is essential for conserving power resource while delivering large data packets within hard-latency constraints…