590 citations · 2.4k across the 96 of their papers we have counts for
6 papers · 1 filter
Is the Envelope Beneficial to Non-Orthogonal Multiple Access?
Ziyi Xie, Wenqiang Yi, Xuanli Wu +2
Non-orthogonal multiple access (NOMA) is capable of serving different numbers of users in the same time-frequency resource element, and this feature can be leveraged to carry addit…
Federated Learning and Meta Learning: Approaches, Applications, and Directions
Xiaonan Liu, Yansha Deng, Arumugam Nallanathan +1
Over the past few years, significant advancements have been made in the field of machine learning (ML) to address resource management, interference management, autonomy, and decisi…
Knowledge-aided Federated Learning for Energy-limited Wireless Networks
Zhixiong Chen, Wenqiang Yi, Yuanwei Liu +1
The conventional model aggregation-based federated learning (FL) approach requires all local models to have the same architecture, which fails to support practical scenarios with h…
Dynamic Task Software Caching-assisted Computation Offloading for Multi-Access Edge Computing
Zhixiong Chen, Wenqiang Yi, Atm S. Alam +1
In multi-access edge computing (MEC), most existing task software caching works focus on statically caching data at the network edge, which may hardly preserve high reusability due…
Efficient Wireless Federated Learning with Partial Model Aggregation
Zhixiong Chen, Wenqiang Yi, Arumugam Nallanathan +1
The data heterogeneity across devices and the limited communication resources, e.g., bandwidth and energy, are two of the main bottlenecks for wireless federated learning (FL). To…
A Reliable Reinforcement Learning for Resource Allocation in Uplink NOMA-URLLC Networks
Waleed Ahsan, Wenqiang Yi, Yuanwei Liu +1
In this paper, we propose a deep state-action-reward-state-action (SARSA) learning approach for optimising the uplink resource allocation in non-orthogonal multiple access (NOM…