1.6k citations · 6.8k across the 149 of their papers we have counts for
22 papers · 1 filter
AI-Aided Online Adaptive OFDM Receiver: Design and Experimental Results
Peiwen Jiang, Tianqi Wang, Bin Han +5
Orthogonal frequency division multiplexing (OFDM) has been widely applied in current communication systems. The artificial intelligence (AI)-aided OFDM receivers are currently brou…
Ultra-dense LEO: Integrating Terrestrial-Satellite Networks into 5G and Beyond for Data Offloading
Boya Di, Hongliang Zhang, Lingyang Song +2
In this paper, we propose a terrestrial-satellite network (TSN) architecture to integrate the ultra-dense low earth orbit (LEO) networks and the terrestrial networks to achieve eff…
Low-Power Wide-Area Networks for Sustainable IoT
Zhijin Qin, Frank Y. Li, Geoffrey Ye Li +2
Low-power wide-area (LPWA) networks are attracting extensive attention because of their abilities to offer low-cost and massive connectivity to Internet of Things (IoT) devices dis…
ComNet: Combination of Deep Learning and Expert Knowledge in OFDM Receivers
Xuanxuan Gao, Shi Jin, Chao-Kai Wen +1
In this article, we propose a model-driven deep learning (DL) approach that combines DL with the expert knowledge to replace the existing orthogonal frequency-division multiplexing…
Frequency Synchronization for Uplink Massive MIMO with Adaptive MUI Suppression in Angle-domain
Yinghao Ge, Weile Zhang, Feifei Gao +1
In this paper, we develop a novel angle-domain adaptive filtering (ADAF)-based frequency synchronization method for the uplink of a massive multiple-input multiple-output (MIMO) mu…
Energy Efficiency of Distributed Antenna Systems with Wireless Power Transfer
Yuwen Huang, Yuan Liu, Geoffrey Ye Li
In this paper, we study energy-efficient resource allocation in distributed antenna system (DAS) with wireless power transfer, where time-division multiple access (TDMA) is adopted…
Performance Analysis of Multi-Cell Millimeter Wave Massive MIMO Networks with Low-Precision ADCs
Jindan Xu, Wei Xu, Hua Zhang +2
In this paper, we investigate a multi-cell millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) network with low-precision analog-to-digital converters (ADCs) at…
A Model-Driven Deep Learning Network for MIMO Detection
Hengtao He, Chao-Kai Wen, Shi Jin +1
In this paper, we propose a model-driven deep learning network for multiple-input multiple-output (MIMO) detection. The structure of the network is specially designed by unfolding…
UAV Communications Based on Non-Orthogonal Multiple Access
Yuanwei Liu, Zhijin Qin, Yunlong Cai +3
This article proposes a novel framework for unmaned aerial vehicle (UAV) networks with massive access capability supported by non-orthogonal multiple access (NOMA). In order to bet…
Model-Driven Deep Learning for Physical Layer Communications
Hengtao He, Shi Jin, Chao-Kai Wen +3
Intelligent communication is gradually considered as the mainstream direction in future wireless communications. As a major branch of machine learning, deep learning (DL) has been…
A Framework on Hybrid MIMO Transceiver Design based on Matrix-Monotonic Optimization
Chengwen Xing, Xin Zhao, Wei Xu +2
Hybrid transceiver can strike a balance between complexity and performance of multiple-input multiple-output (MIMO) systems. In this paper, we develop a unified framework on hybrid…
Resource Allocation for Cooperative D2D-Enabled Wireless Caching Networks
Jiaqi Liu, Shengjie Guo, Sa Xiao +5
In this paper, we study the resource allocation problem for a cooperative device-to-device (D2D)-enabled wireless caching network, where each user randomly caches popular contents…
Deep Learning-based CSI Feedback Approach for Time-varying Massive MIMO Channels
Tianqi Wang, Chao-Kai Wen, Shi Jin +1
Massive multiple-input multiple-output (MIMO) systems rely on channel state information (CSI) feedback to perform precoding and achieve performance gain in frequency division duple…
Channel Agnostic End-to-End Learning based Communication Systems with Conditional GAN
Hao Ye, Geoffrey Ye Li, Biing-Hwang Fred Juang +1
In this article, we use deep neural networks (DNNs) to develop a wireless end-to-end communication system, in which DNNs are employed for all signal-related functionalities, such a…
Deep Learning in Physical Layer Communications
Zhijin Qin, Hao Ye, Geoffrey Ye Li +1
Deep learning (DL) has shown the great potentials to break the bottleneck of communication systems. This article provides an overview on the recent advancements in DL-based physica…
Performance Analysis of Indoor THz Communications with One-Bit Precoding
Dan Li, Deli Qiao, Lei Zhang +1
In this paper, the performance of indoor Terahertz (THz) communication systems with one-bit digital-to-analog converters (DACs) is investigated. Array-of-subarrays architecture is…
Resource Allocation for Low-Latency Vehicular Communications with Packet Retransmission
Chongtao Guo, Le Liang, Geoffrey Ye Li
Vehicular communications have stringent latency requirements on safety-critical information transmission. However, lack of instantaneous channel state information due to high mobil…
Deep Reinforcement Learning based Resource Allocation for V2V Communications
Hao Ye, Geoffrey Ye Li, Biing-Hwang Fred Juang
In this paper, we develop a decentralized resource allocation mechanism for vehicle-to-vehicle (V2V) communications based on deep reinforcement learning, which can be applied to bo…
Toward Intelligent Vehicular Networks: A Machine Learning Framework
Le Liang, Hao Ye, Geoffrey Ye Li
As wireless networks evolve towards high mobility and providing better support for connected vehicles, a number of new challenges arise due to the resulting high dynamics in vehicu…
Deep Learning-based Channel Estimation for Beamspace mmWave Massive MIMO Systems
Hengtao He, Chao-Kai Wen, Shi Jin +1
Channel estimation is very challenging when the receiver is equipped with a limited number of radio-frequency (RF) chains in beamspace millimeter-wave (mmWave) massive multiple-inp…
Graph-Based Radio Resource Management for Vehicular Networks
Le Liang, Shijie Xie, Geoffrey Ye Li +2
This paper investigates the resource allocation problem in device-to-device (D2D)-based vehicular communications, based on slow fading statistics of channel state information (CSI)…
Sparse Representation for Wireless Communications: A Compressive Sensing Approach
Zhijin Qin, Jiancun Fan, Yuanwei Liu +2
Sparse representation can efficiently model signals in different applications to facilitate processing. In this article, we will discuss various applications of sparse representati…