1.6k citations · 6.8k across the 149 of their papers we have counts for
77 papers · 1 filter
Deep Unfolding-Based Channel Estimation for Wideband TeraHertz Near-Field Massive MIMO Systems
Jiabao Gao, Xiaoming Cheng, Geoffrey Ye Li
The combination of Terahertz (THz) and massive multiple-input multiple-output (MIMO) is promising to meet the increasing data rate demand of future wireless communication systems t…
QoE-based Semantic-Aware Resource Allocation for Multi-Task Networks
Lei Yan, Zhijin Qin, Chunfeng Li +3
By transmitting task-related information only, semantic communications yield significant performance gains over conventional communications. However, the lack of mature semantic th…
AMP-SBL Unfolding for Wideband MmWave Massive MIMO Channel Estimation
Jiabao Gao, Caijun Zhong, Geoffrey Ye Li
In wideband millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, channel estimation is challenging due to the hybrid analog-digital architecture, which c…
Graph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directions
Mengyuan Lee, Guanding Yu, Huaiyu Dai +1
As an efficient graph analytical tool, graph neural networks (GNNs) have special properties that are particularly fit for the characteristics and requirements of wireless communica…
Learn to Adapt to New Environment from Past Experience and Few Pilot
Ouya Wang, Jiabao Gao, Geoffrey Ye Li
In recent years, deep learning has been widely applied in communications and achieved remarkable performance improvement. Most of the existing works are based on data-driven deep l…
LEO Satellite-Enabled Grant-Free Random Access with MIMO-OTFS
Boxiao Shen, Yongpeng Wu, Wenjun Zhang +3
This paper investigates joint channel estimation and device activity detection in the LEO satellite-enabled grant-free random access systems with large differential delay and Doppl…
Hybrid Precoding for Mixture Use of Phase Shifters and Switches in mmWave Massive MIMO
Chenhao Qi, Qiang Liu, Xianghao Yu +1
A variable-phase-shifter (VPS) architecture with hybrid precoding for mixture use of phase shifters and switches, is proposed for millimeter wave massive multiple-input multiple-ou…
Deep Learning-based Channel Estimation for Wideband Hybrid MmWave Massive MIMO
Jiabao Gao, Caijun Zhong, Geoffrey Ye Li +2
Hybrid analog-digital (HAD) architecture is widely adopted in practical millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems to reduce hardware cost and e…
Hierarchical Codebook based Multiuser Beam Training for Millimeter Wave Massive MIMO
Chenhao Qi, Kangjian Chen, Octavia A. Dobre +1
In this paper, multiuser beam training based on hierarchical codebook for millimeter wave massive multi-input multi-output is investigated, where the base station (BS) simultaneous…
Online Deep Neural Network for Optimization in Wireless Communications
Jiabao Gao, Caijun Zhong, Geoffrey Ye Li +1
Recently, deep neural network (DNN) has been widely adopted in the design of intelligent communication systems thanks to its strong learning ability and low testing complexity. How…
Deep Learning based Channel Estimation for Massive MIMO with Hybrid Transceivers
Jiabao Gao, Caijun Zhong, Geoffrey Ye Li +1
Accurate and efficient estimation of the high dimensional channels is one of the critical challenges for practical applications of massive multiple-input multiple-output (MIMO). In…
Reinforcement Learning Based Power Control for Reliable Mission-Critical Wireless Transmission
Chongtao Guo, Zhengchao Li, Le Liang +1
In this paper, we investigate sequential power allocation over fast varying channels for mission-critical applications, aiming to minimize the expected sum power while guaranteeing…
Semantic Communications: Principles and Challenges
Zhijin Qin, Xiaoming Tao, Jianhua Lu +2
Semantic communication, regarded as the breakthrough beyond the Shannon paradigm, aims at the successful transmission of semantic information conveyed by the source rather than the…
Nine Challenges in Artificial Intelligence and Wireless Communications for 6G
Wen Tong, Geoffrey Ye Li
In recent years, techniques developed in artificial intelligence (AI), especially those in machine learning (ML), have been successfully applied in various areas, leading to a wide…
Asymmetrical Uplink and Downlink Transceivers in Massive MIMO Systems
Xi Yang, Shi Jin, Geoffrey Ye Li +1
Even if massive multiple-input multiple-output (MIMO) can theoretically bring huge benefits, it incurs substantial hardware complexity and expensive hardware costs. To address thes…
An Attention-Aided Deep Learning Framework for Massive MIMO Channel Estimation
Jiabao Gao, Mu Hu, Caijun Zhong +2
Channel estimation is one of the key issues in practical massive multiple-input multiple-output (MIMO) systems. Compared with conventional estimation algorithms, deep learning (DL)…
On Channel Reciprocity in Reconfigurable Intelligent Surface Assisted Wireless Network
Wankai Tang, Xiangyu Chen, Ming Zheng Chen +6
Channel reciprocity greatly facilitates downlink precoding in time-division duplexing (TDD) multiple-input multiple-output (MIMO) communications without the need for channel state…
Is NOMA Efficient in Multi-Antenna Networks? A Critical Look at Next Generation Multiple Access Techniques
Bruno Clerckx, Yijie Mao, Robert Schober +7
In this paper, we take a critical and fresh look at the downlink multi-antenna NOMA literature. Instead of contrasting NOMA with OMA, we contrast NOMA with two other baselines. The…
Deep Learning for Joint Channel Estimation and Feedback in Massive MIMO Systems
Jiajia Guo, Tong Chen, Shi Jin +3
The great potentials of massive Multiple-Input Multiple-Output (MIMO) in Frequency Division Duplex (FDD) mode can be fully exploited when the downlink Channel State Information (CS…
Model-Driven Deep Learning for Massive MU-MIMO with Finite-Alphabet Precoding
Hengtao He, Mengjiao Zhang, Shi Jin +2
Massive multiuser multiple-input multiple-output (MU-MIMO) has been the mainstream technology in fifth-generation wireless systems. To reduce high hardware costs and power consumpt…
Robust Precoding in Massive MIMO: A Deep Learning Approach
Junchao Shi, Wenjin Wang, Xinping Yi +2
In this paper, we consider massive multiple-input-multiple-output (MIMO) communication systems with a uniform planar array (UPA) at the base station (BS) and investigate the downli…
Federated Learning and Wireless Communications
Zhijin Qin, Geoffrey Ye Li, Hao Ye
Federated learning becomes increasingly attractive in the areas of wireless communications and machine learning due to its powerful functions and potential applications. In contras…
Spatially Correlated Massive MIMO Relay Systems with Low-Resolution ADCs
Peihao Dong, Hua Zhang, Qihui Wu +1
In this paper, we investigate the massive MIMO relay system, where the relay station (RS) forwards the signals from multiple remote users to the base station (BS). Large-scale ante…
Reinforcement Learning Based Cooperative Coded Caching under Dynamic Popularities in Ultra-Dense Networks
Shen Gao, Peihao Dong, Zhiwen Pan +1
For ultra-dense networks with wireless backhaul, caching strategy at small base stations (SBSs), usually with limited storage, is critical to meet massive high data rate requests.…