35 citations · 37 across the 6 of their papers we have counts for
6 papers
Distributed Detection and Bandwidth Allocation with Hybrid Quantized and Full-Precision Observations over Multiplicative Fading Channels
Linlin Mao, Zeping Sui, Michail Matthaiou +1
A hybrid detector that fuses both quantized and full-precision observations is proposed for weak signal detection under additive and multiplicative Gaussian noise. We first derive…
Compressive Near-Field Wideband Channel Estimation for THz Extremely Large-scale MIMO Systems
Jionghui Wang, Hongwei Wang, Jun Fang +2
We consider the channel acquisition problem for a wideband terahertz (THz) communication system, where an extremely large-scale array is deployed to mitigate severe path attenuatio…
Multi-bit Distributed Detection of Sparse Stochastic Signals over Error-Prone Reporting Channels
Linlin Mao, Shefeng Yan, Zeping Sui +1
We consider a distributed detection problem within a wireless sensor network (WSN), where a substantial number of sensors cooperate to detect the existence of sparse stochastic sig…
Communication Efficient ConFederated Learning: An Event-Triggered SAGA Approach
Bin Wang, Jun Fang, Hongbin Li +1
Federated learning (FL) is a machine learning paradigm that targets model training without gathering the local data dispersed over various data sources. Standard FL, which employs…
Fundamental Detection Probability vs. Achievable Rate Tradeoff in Integrated Sensing and Communication Systems
Jiancheng An, Hongbin Li, Derrick Wing Kwan Ng +1
Integrating sensing functionalities is envisioned as a distinguishing feature of next-generation mobile networks, which has given rise to the development of a novel enabling techno…
Confederated Learning: Federated Learning with Decentralized Edge Servers
Bin Wang, Jun Fang, Hongbin Li +2
Federated learning (FL) is an emerging machine learning paradigm that allows to accomplish model training without aggregating data at a central server. Most studies on FL consider…