67 citations · 112 across the 12 of their papers we have counts for
12 papers
Generative Diffusion Models for High Dimensional Channel Estimation
Xingyu Zhou, Le Liang, Jing Zhang +3
Along with the prosperity of generative artificial intelligence (AI), its potential for solving conventional challenges in wireless communications has also surfaced. Inspired by th…
Mini-Batch Gradient-Based MCMC for Decentralized Massive MIMO Detection
Xingyu Zhou, Le Liang, Jing Zhang +2
Massive multiple-input multiple-output (MIMO) technology has significantly enhanced spectral and power efficiency in cellular communications and is expected to further evolve towar…
Near-Optimal MIMO Detection Using Gradient-Based MCMC in Discrete Spaces
Xingyu Zhou, Le Liang, Jing Zhang +2
The discrete nature of transmitted symbols poses challenges for achieving optimal detection in multiple-input multiple-output (MIMO) systems associated with a large number of anten…
Low-Complexity Joint Beamforming for RIS-Assisted MU-MISO Systems Based on Model-Driven Deep Learning
Weijie Jin, Jing Zhang, Chao-Kai Wen +3
Reconfigurable intelligent surfaces (RIS) can improve signal propagation environments by adjusting the phase of the incident signal. However, optimizing the phase shifts jointly wi…
Graph Neural Network-Enhanced Expectation Propagation Algorithm for MIMO Turbo Receivers
Xingyu Zhou, Jing Zhang, Chao-Kai Wen +2
Deep neural networks (NNs) are considered a powerful tool for balancing the performance and complexity of multiple-input multiple-output (MIMO) receivers due to their accurate feat…
Gradient-Based Markov Chain Monte Carlo for MIMO Detection
Xingyu Zhou, Le Liang, Jing Zhang +2
Accurately detecting symbols transmitted over multiple-input multiple-output (MIMO) wireless channels is crucial in realizing the benefits of MIMO techniques. However, optimal MIMO…
Meta Learning-based MIMO Detectors: Design, Simulation, and Experimental Test
Jing Zhang, Yunfeng He, Yu-Wen Li +2
Deep neural networks (NNs) have exhibited considerable potential for efficiently balancing the performance and complexity of multiple-input and multiple-output (MIMO) detectors. We…
Model-Driven DNN Decoder for Turbo Codes: Design, Simulation and Experimental Results
Yunfeng He, Jing Zhang, Shi Jin +2
This paper presents a novel model-driven deep learning (DL) architecture, called TurboNet, for turbo decoding that integrates DL into the traditional max-log-maximum a posteriori (…
TurboNet: A Model-driven DNN Decoder Based on Max-Log-MAP Algorithm for Turbo Code
Yunfeng He, Jing Zhang, Chao-Kai Wen +1
This paper presents TurboNet, a novel model-driven deep learning (DL) architecture for turbo decoding that combines DL with the traditional max-log-maximum a posteriori (MAP) algor…
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…
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…
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…