most citedA General Deep Reinforcement Learning Framework for Grant-Free NOMA Optimization in mURLLC

6 citations · 11 across the 4 of their papers we have counts for

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cs.IT2021★ 6 cited

A General Deep Reinforcement Learning Framework for Grant-Free NOMA Optimization in mURLLC

Yan Liu, Yansha Deng, Hui Zhou +2

Grant-free non-orthogonal multiple access (GF-NOMA) is a potential technique to support massive Ultra-Reliable and Low-Latency Communication (mURLLC) service. However, the dynamic…

cs.IT2020★ 2 cited

Deep Reinforcement Learning-Based Beam Tracking for Low-Latency Services in Vehicular Networks

Yan Liu, Zhiyuan Jiang, Shunqing Zhang +1

Ultra-Reliable and Low-Latency Communications (URLLC) services in vehicular networks on millimeter-wave bands present a significant challenge, considering the necessity of constant…