335 citations · 342 across the 7 of their papers we have counts for
7 papers
Fairness-Guided DQN-Based TXOP Control for NR-U/Wi-Fi Coexistence
Po-Heng Chou, Yi-Fang Yu, Shou-Yu Chen +1
New Radio operating in unlicensed spectrum (NR-U) and Wi-Fi employ different contention parameters and channel-occupancy durations, which can produce severe coexistence imbalance.…
Algorithms for Interference Minimization in Future Wireless Network Decomposition
Péter L. Erdős, Tamás Róbert Mezei, Yiding Yu +3
We propose a simple and fast method for providing a high quality solution for the sum-interference minimization problem. As future networks are deployed in high density urban areas…
The optimal network throughputs when the model-aware node coexists with other nodes using different MAC protocols
Xiaowen Ye, Yiding Yu, Liqun Fu
In this document, we give the optimal network throughput when the DR-DLMA node (see our paper for definition) coexists with the nodes using other protocol (e.g., TDMA and/or ALOHA)…
Multi-Agent Deep Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks with Imperfect Channels
Yiding Yu, Soung Chang Liew, Taotao Wang
This paper investigates a futuristic spectrum sharing paradigm for heterogeneous wireless networks with imperfect channels. In the heterogeneous networks, multiple wireless network…
Non-Uniform Time-Step Deep Q-Network for Carrier-Sense Multiple Access in Heterogeneous Wireless Networks
Yiding Yu, Soung Chang Liew, Taotao Wang
This paper investigates a new class of carrier-sense multiple access (CSMA) protocols that employ deep reinforcement learning (DRL) techniques, referred to as carrier-sense deep-re…
Carrier-Sense Multiple Access for Heterogeneous Wireless Networks Using Deep Reinforcement Learning
Yiding Yu, Soung Chang Liew, Taotao Wang
This paper investigates a new class of carrier-sense multiple access (CSMA) protocols that employ deep reinforcement learning (DRL) techniques for heterogeneous wireless networking…
Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks
Yiding Yu, Taotao Wang, Soung Chang Liew
This paper investigates the use of deep reinforcement learning (DRL) in a MAC protocol for heterogeneous wireless networking referred to as Deep-reinforcement Learning Multiple Acc…