116 citations · 515 across the 26 of their papers we have counts for
26 papers
Characterization and Mitigation of Polyphase-Code Artifacts in 5G NR ISAC
Xingkang Li, Shengheng Liu, Ziguo Zhong +4
Target sensing utilizing 5G NR reference signals has emerged as a prominent research direction in both academia and industry. However, non-ideal factors in practical deployments ex…
Bayesian Probability Fusion for Multi-AP Collaborative Sensing in Mobile Networks
Shengheng Liu, Xingkang Li, Yongming Huang +6
Integrated sensing and communication is widely acknowledged as a foundational technology for next-generation mobile networks. Compared with monostatic sensing, multi-access point (…
Enabling low-power massive MIMO with ternary ADCs for AIoT sensing
Shengheng Liu, Ningning Fu
The proliferation of networked devices and the surging demand for ubiquitous intelligence have given rise to the artificial intelligence of things (AIoT). However, the utilization…
Multi-grained spatial-temporal feature complementarity for accurate online cellular traffic prediction
Ningning Fu, Shengheng Liu, Weiliang Xie +1
Knowledge discovered from telecom data can facilitate proactive understanding of network dynamics and user behaviors, which in turn empowers service providers to optimize cellular…
Integrated user scheduling and beam steering in over-the-air federated learning for mobile IoT
Shengheng Liu, Ningning Fu, Zhonghao Zhang +2
The rising popularity of Internet of things (IoT) has spurred technological advancements in mobile internet and interconnected systems. While offering flexible connectivity and int…
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…
In-Situ Calibration of Antenna Arrays for Positioning With 5G Networks
Mengguan Pan, Shengheng Liu, Peng Liu +5
Owing to the ubiquity of cellular communication signals, positioning with the fifth generation (5G) signal has emerged as a promising solution in global navigation satellite system…
Fast Direct Localization for Millimeter Wave MIMO Systems via Deep ADMM Unfolding
Wenzhe Fan, Shengheng Liu, Chunguo Li +1
Massive arrays deployed in millimeter-wave systems enable high angular resolution performance, which in turn facilitates sub-meter localization services. Albeit suboptimal, up to n…
Link-level simulator for 5G localization
Xinghua Jia, Peng Liu, Wangdong Qi +5
Channel-state-information-based localization in 5G networks has been a promising way to obtain highly accurate positions compared to previous communication networks. However, there…
Toward 6G TK Extreme Connectivity: Architecture, Key Technologies and Experiments
Xiaohu You, Yongming Huang, Shengheng Liu +20
Sixth-generation (6G) networks are evolving towards new features and order-of-magnitude enhancement of systematic performance metrics compared to the current 5G. In particular, the…
Unsupervised Recurrent Federated Learning for Edge Popularity Prediction in Privacy-Preserving Mobile Edge Computing Networks
Chong Zheng, Shengheng Liu, Yongming Huang +2
Nowadays wireless communication is rapidly reshaping entire industry sectors. In particular, mobile edge computing (MEC) as an enabling technology for industrial Internet of things…
Learning-Aided Beam Prediction in mmWave MU-MIMO Systems for High-Speed Railway
Fan Meng, Shengheng Liu, Yongming Huang +1
The problem of beam alignment and tracking in high mobility scenarios such as high-speed railway (HSR) becomes extremely challenging, since large overhead cost and significant time…
Learnable Model-Driven Performance Prediction and Optimization for Imperfect MIMO System: Framework and Application
Fan Meng, Shengheng Liu, Yongming Huang +1
State-of-the-art schemes for performance analysis and optimization of multiple-input multiple-output systems generally experience degradation or even become invalid in dynamic comp…
Efficient Joint DOA and TOA Estimation for Indoor Positioning with 5G Picocell Base Stations
Mengguan Pan, Peng Liu, Shengheng Liu +5
The ubiquity, large bandwidth, and spatial diversity of the fifth generation (5G) cellular signal render it a promising candidate for accurate positioning in indoor environments wh…
Joint DoA-Range Estimation Using Space-Frequency Virtual Difference Coarray
Zihuan Mao, Shengheng Liu, Yimin D. Zhang +2
In this paper, we address the problem of joint direction-of-arrival (DoA) and range estimation using frequency diverse coprime array (FDCA). By incorporating the coprime array stru…
Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching
Shengheng Liu, Chong Zheng, Yongming Huang +1
Mobile edge computing (MEC) is a prominent computing paradigm which expands the application fields of wireless communication. Due to the limitation of the capacities of user equipm…
Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computation
Chunmei Xu, Shengheng Liu, Zhaohui Yang +2
Federated learning (FL) as a promising edge-learning framework can effectively address the latency and privacy issues by featuring distributed learning at the devices and model agg…
Hybrid Policy Learning for Energy-Latency Tradeoff in MEC-Assisted VR Video Service
Chong Zheng, Shengheng Liu, Yongming Huang +1
Virtual reality (VR) is promising to fundamentally transform a broad spectrum of industry sectors and the way humans interact with virtual content. However, despite unprecedented p…
Rank Minimization-based Toeplitz Reconstruction for DoA Estimation Using Coprime Array
Shengheng Liu, Zihuan Mao, Yimin D. Zhang +1
In this paper, we address the problem of direction finding using coprime array, which is one of the most preferred sparse array configurations. Motivated by the fact that non-unifo…