1.6k citations · 6.8k across the 149 of their papers we have counts for
22 papers · 1 filter
D2D-Enabled Mobile User Edge Caching: A Multi-Winner Auction Approach
Tiankui Zhang, Xinyuan Fang, Yuanwei Liu +2
In device-to-device (D2D)-enabled caching cellular networks, the user terminals (UTs) collaboratively store and share a large volume of popular contents from the base station (BS)…
20 Years of Evolution from Cognitive to Intelligent Communications
Zhijin Qin, Xiangwei Zhou, Lin Zhang +3
It has been 20 years since the concept of cognitive radio (CR) was proposed, which is an efficient approach to provide more access opportunities to connect massive wireless devices…
Deep Learning based Downlink Channel Prediction for FDD Massive MIMO System
Yuwen Yang, Feifei Gao, Geoffrey Ye Li +1
In a frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system, the acquisition of downlink channel state information (CSI) at base station (BS) is a…
First 20 Years of Green Radios
Shunqing Zhang, Shugong Xu, Geoffrey Ye Li +1
Green radios jointly consider the spectrum efficiency and the energy efficiency in wireless networks in order to provide sustainable development. In the past two decades, various g…
Deep Learning based Channel Estimation for Massive MIMO with Mixed-Resolution ADCs
Shen Gao, Peihao Dong, Zhiwen Pan +1
In this article, deep learning is applied to estimate the uplink channels for mixed analog-to-digital converters (ADCs) massive multiple-input multiple-output (MIMO) systems, where…
Learn to Compress CSI and Allocate Resources in Vehicular Networks
Liang Wang, Hao Ye, Le Liang +1
Resource allocation has a direct and profound impact on the performance of vehicle-to-everything (V2X) networks. In this paper, we develop a hybrid architecture consisting of centr…
Compression and Acceleration of Neural Networks for Communications
Jiajia Guo, Jinghe Wang, Chao-Kai Wen +2
Deep learning (DL) has achieved great success in signal processing and communications and has become a promising technology for future wireless communications. Existing works mainl…
Learn to Allocate Resources in Vehicular Networks
Liang Wang, Hao Ye, Le Liang +1
Resource allocation has a direct and profound impact on the performance of vehicle-to-everything (V2X) networks. Considering the dynamic nature of vehicular environments, it is app…
Model-Driven Deep Learning for MIMO Detection
Hengtao He, Chao-Kai Wen, Shi Jin +1
In this paper, we investigate the model-driven deep learning (DL) for MIMO detection. In particular, the MIMO detector is specially designed by unfolding an iterative algorithm and…
Deep Learning based Wireless Resource Allocation with Application to Vehicular Networks
Le Liang, Hao Ye, Guanding Yu +1
It has been a long-held belief that judicious resource allocation is critical to mitigating interference, improving network efficiency, and ultimately optimizing wireless communica…
Convolutional Neural Network based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis
Jiajia Guo, Chao-Kai Wen, Shi Jin +1
Massive multiple-input multiple-output (MIMO) is a promising technology to increase link capacity and energy efficiency. However, these benefits are based on available channel stat…
Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning
Le Liang, Hao Ye, Geoffrey Ye Li
This paper investigates the spectrum sharing problem in vehicular networks based on multi-agent reinforcement learning, where multiple vehicle-to-vehicle (V2V) links reuse the freq…
Graph Embedding based Wireless Link Scheduling with Few Training Samples
Mengyuan Lee, Guanding Yu, Geoffrey Ye Li
Link scheduling in device-to-device (D2D) networks is usually formulated as a non-convex combinatorial problem, which is generally NP-hard and difficult to get the optimal solution…
Performance Analysis of Clustered LoRa Networks
Zhijin Qin, Yuanwei Liu, Geoffrey Ye Li +1
In this paper, we investigate the uplink transmission performance of low-power wide-area (LPWA) networks with regards to coexisting radio modules. We adopt long range (LoRa) radio…
Deep Learning Based on Orthogonal Approximate Message Passing for CP-Free OFDM
Jing Zhang, Hengtao He, Chao-Kai Wen +2
Channel estimation and signal detection are very challenging for an orthogonal frequency division multiplexing (OFDM) system without cyclic prefix (CP). In this article, deep learn…
Deep CNN-Based Channel Estimation for mmWave Massive MIMO Systems
Peihao Dong, Hua Zhang, Geoffrey Ye Li +2
For millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, hybrid processing architecture is usually used to reduce the complexity and cost, which poses a…
Deep Learning based End-to-End Wireless Communication Systems with Conditional GAN as Unknown Channel
Hao Ye, Le Liang, Geoffrey Ye Li +1
In this article, we develop an end-to-end wireless communication system using deep neural networks (DNNs), in which DNNs are employed to perform several key functions, including en…
Beam Squint and Channel Estimation for Wideband mmWave Massive MIMO-OFDM Systems
Bolei Wang, Mengnan Jian, Feifei Gao +3
With the increasing scale of antenna arrays in wideband millimeter-wave (mmWave) communications, the physical propagation delays of electromagnetic waves traveling across the whole…
Artificial Intelligence-aided Receiver for A CP-Free OFDM System: Design, Simulation, and Experimental Test
Jing Zhang, Chao-Kai Wen, Shi Jin +1
Orthogonal frequency division multiplexing (OFDM), usually with sufficient cyclic prefix (CP), has been widely applied in various communication systems. The CP in OFDM consumes add…
Learning to Branch: Accelerating Resource Allocation in Wireless Networks
Mengyuan Lee, Guanding Yu, Geoffrey Ye Li
Resource allocation in wireless networks, such as device-to-device (D2D) communications, is usually formulated as mixed integer nonlinear programming (MINLP) problems, which are ge…
TCM-NOMA: Joint Multi-user Codeword Design and Detection in Trellis Coded Modulation based NOMA for Beyond 5G
Boya Di, Lingyang Song, Yonghui Li +1
In this paper, we propose a novel non-orthogonal multiple access (NOMA) scheme based on trellis-coded modulation (TCM). Different from those in the traditional code-domain NOMA, th…
Beam Training and Allocation for Multiuser Millimeter Wave Massive MIMO Systems
Xuyao Sun, Chenhao Qi, Geoffrey Ye Li
We investigate beam training and allocation for multiuser millimeter wave massive MIMO systems. An orthogonal pilot based beam training scheme is first developed to reduce the numb…