116 citations · 515 across the 26 of their papers we have counts for
6 papers · 1 filter
Model-driven deep neural network for enhanced direction finding with commodity 5G gNodeB
Shengheng Liu, Zihuan Mao, Xingkang Li +4
Pervasive and high-accuracy positioning has become increasingly important as a fundamental enabler for intelligent connected devices in mobile networks. Nevertheless, current wirel…
5G NR monostatic positioning with array impairments: Data-and-model-driven framework and experiment results
Shengheng Liu, Hao Wang, Mengguan Pan +3
In this article, we present an intelligent framework for 5G new radio (NR) indoor positioning under a monostatic configuration. The primary objective is to estimate both the angle…
Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB
Shengheng Liu, Xingkang Li, Zihuan Mao +2
High-accuracy positioning has become a fundamental enabler for intelligent connected devices. Nevertheless, the present wireless networks still rely on model-driven approaches to a…
Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning
Shengheng Liu, Tianqi Zhang, Ningning Fu +1
AI becomes increasingly vital for telecom industry, as the burgeoning complexity of upcoming mobile communication networks places immense pressure on network operators. While there…
Access Point Deployment for Localizing Accuracy and User Rate in Cell-Free Systems
Fanfei Xu, Shengheng Liu, Zihuan Mao +4
Evolving next-generation mobile networks is designed to provide ubiquitous coverage and networked sensing. With utility of multi-view sensing and multi-node joint transmission, cel…
Random Aggregate Beamforming for Over-the-Air Federated Learning in Large-Scale Networks
Chunmei Xu, Shengheng Liu, Yongming Huang +2
At present, there is a trend to deploy ubiquitous artificial intelligence (AI) applications at the edge of the network. As a promising framework that enables secure edge intelligen…