Search Results for author: Geoffrey Ye Li

Found 83 papers, 17 papers with code

Semantic Satellite Communications Based on Generative Foundation Model

no code implementations18 Apr 2024 Peiwen Jiang, Chao-Kai Wen, Xiao Li, Shi Jin, Geoffrey Ye Li

Considering the high speed of satellites, an adaptive encoder-decoder is proposed to protect important features and avoid frequent retransmissions.

A Universal Deep Neural Network for Signal Detection in Wireless Communication Systems

no code implementations3 Apr 2024 Khalid Albagami, Nguyen Van Huynh, Geoffrey Ye Li

Recently, deep learning (DL) has been emerging as a promising approach for channel estimation and signal detection in wireless communications.

Environment Reconstruction based on Multi-User Selection and Multi-Modal Fusion in ISAC

no code implementations26 Mar 2024 Bo Lin, Chuanbin Zhao, Feifei Gao, Geoffrey Ye Li

Integrated sensing and communications (ISAC) has been deemed as a key technology for the sixth generation (6G) wireless communications systems.

Distributionally Robust Beamforming and Estimation of Wireless Signals

no code implementations22 Jan 2024 Shixiong Wang, Wei Dai, Geoffrey Ye Li

This paper investigates signal estimation in wireless transmission from the perspective of statistical machine learning, where the transmitted signals may be from an integrated sensing and communication system; that is, 1) signals may be not only discrete constellation points but also arbitrary complex values; 2) signals may be spatially correlated.

Distributed Task-Oriented Communication Networks with Multimodal Semantic Relay and Edge Intelligence

no code implementations18 Jan 2024 Jie Guo, Hao Chen, Bin Song, Yuhao Chi, Chau Yuen, Fei Richard Yu, Geoffrey Ye Li, Dusit Niyato

In this article, we present a novel framework, named distributed task-oriented communication networks (DTCN), based on recent advances in multimodal semantic transmission and edge intelligence.

Federated Multi-View Synthesizing for Metaverse

no code implementations18 Dec 2023 Yiyu Guo, Zhijin Qin, Xiaoming Tao, Geoffrey Ye Li

With recent advances in edge intelligence and deep learning, we have developed a novel multi-view synthesizing framework that can efficiently provide computation, storage, and communication resources for wireless content delivery in the metaverse.

Domain Adaptation Federated Learning +1

Key Issues in Wireless Transmission for NTN-Assisted Internet of Things

no code implementations25 Nov 2023 Chenhao Qi, Jing Wang, Leyi Lyu, Lei Tan, Jinming Zhang, Geoffrey Ye Li

The long-distance wireless signal propagation in NTNs leads to severe path loss and large latency, where the accurate acquisition of channel state information (CSI) is another challenge, especially for fast-moving non-terrestrial base stations (NTBSs).

Simultaneous Beam Training and Target Sensing in ISAC Systems with RIS

no code implementations25 Nov 2023 Kangjian Chen, Chenhao Qi, Octavia A. Dobre, Geoffrey Ye Li

Based on the SBTTS and PAOE schemes, we further compute the angle-of-arrival and angle-of-departure for the channels between the RIS and the UTs by exploiting the geometry relationship to accomplish the beam alignment of the ISAC system.

Robust Waveform Design for Integrated Sensing and Communication

1 code implementation31 Oct 2023 Shixiong Wang, Wei Dai, Haowei Wang, Geoffrey Ye Li

Therefore, we formulate robust waveform design problems by studying the worst-case channels and prove that the robustly-estimated performance is guaranteed to be attainable in real-world operation.

Robust Design

Federated Reinforcement Learning for Resource Allocation in V2X Networks

no code implementations15 Oct 2023 Kaidi Xu, Shenglong Zhou, Geoffrey Ye Li

In this paper, we explore resource allocation in a V2X network under the framework of federated reinforcement learning (FRL).

Federated Learning reinforcement-learning

Compression Ratio Learning and Semantic Communications for Video Imaging

no code implementations10 Oct 2023 BoWen Zhang, Zhijin Qin, Geoffrey Ye Li

In this article, we also investigate the data transmission methods for programmable sensors, where the performance of communication systems is evaluated by the reconstructed images or videos rather than the transmission of sensor data itself.

Compressive Sensing Video Compressive Sensing

Communication-Efficient Decentralized Federated Learning via One-Bit Compressive Sensing

no code implementations31 Aug 2023 Shenglong Zhou, Kaidi Xu, Geoffrey Ye Li

Compared to the centralized version, training a shared model among a large number of nodes in DFL is more challenging, as there is no central server to coordinate the training process.

Compressive Sensing Computational Efficiency +1

Deep Plug-and-Play Prior for Multitask Channel Reconstruction in Massive MIMO Systems

1 code implementation9 Aug 2023 Weixiao Wan, Wei Chen, Shiyue Wang, Geoffrey Ye Li, Bo Ai

The proposed method corresponding to these three channel reconstruction tasks employs a common DL model, which greatly reduces the overhead of model training and storage.

Multi-Task Learning

RIS-Enhanced Semantic Communications Adaptive to User Requirements

no code implementations30 Jul 2023 Peiwen Jiang, Chao-Kai Wen, Shi Jin, Geoffrey Ye Li

Simulation results demonstrate the adaptability and efficiency of the RIS-SC framework across diverse channel conditions and user requirements.

Semantic-Aware Image Compressed Sensing

no code implementations6 Jul 2023 BoWen Zhang, Zhijin Qin, Geoffrey Ye Li

According to the base CS results, the encoder then employs a policy network to analyze the semantic information in images and determines the measurement matrix for different image areas.

Image Compressed Sensing

Deep-Unfolding for Next-Generation Transceivers

no code implementations15 May 2023 Qiyu Hu, Yunlong Cai, Guangyi Zhang, Guanding Yu, Geoffrey Ye Li

Then, some endeavors in applying deep-unfolding approaches in next-generation advanced transceiver design are presented.

Semantic Communication with Memory

no code implementations22 Mar 2023 Huiqiang Xie, Zhijin Qin, Geoffrey Ye Li

While semantic communication succeeds in efficiently transmitting due to the strong capability to extract the essential semantic information, it is still far from the intelligent or human-like communications.

Distributed Two-tier DRL Framework for Cell-Free Network: Association, Beamforming and Power Allocation

1 code implementation22 Mar 2023 Kaiwen Yu, Chonghao Zhao, Gang Wu, Geoffrey Ye Li

Intelligent wireless networks have long been expected to have self-configuration and self-optimization capabilities to adapt to various environments and demands.

Semantic Communications with Variable-Length Coding for Extended Reality

no code implementations17 Feb 2023 BoWen Zhang, Zhijin Qin, Geoffrey Ye Li

Wireless extended reality (XR) has attracted wide attentions as a promising technology to improve users' mobility and quality of experience.

Semantic Sensing and Communications for Ultimate Extended Reality

no code implementations16 Dec 2022 BoWen Zhang, Zhijin Qin, Yiyu Guo, Geoffrey Ye Li

In particular, semantic sensing is used to improve the sensing efficiency by exploring the spatial-temporal distributions of semantic information.

Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNet

1 code implementation15 Dec 2022 Kaidi Xu, Nguyen Van Huynh, Geoffrey Ye Li

To overcome these limitations, we propose a multi-agent deep reinforcement learning (MADRL) based power control scheme for the HetNet, where each access point makes power control decisions independently based on local information.

Multi-agent Reinforcement Learning Q-Learning +2

Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMO

no code implementations13 Dec 2022 Houssem Sifaou, Geoffrey Ye Li

Cell-free massive MIMO is emerging as a promising technology for future wireless communication systems, which is expected to offer uniform coverage and high spectral efficiency compared to classical cellular systems.

Federated Learning

Graph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directions

no code implementations8 Dec 2022 Mengyuan Lee, Guanding Yu, Huaiyu Dai, Geoffrey Ye Li

As an efficient graph analytical tool, graph neural networks (GNNs) have special properties that are particularly fit for the characteristics and requirements of wireless communications, exhibiting good potential for the advancement of next-generation wireless communications.

CSI-PPPNet: A One-Sided One-for-All Deep Learning Framework for Massive MIMO CSI Feedback

no code implementations29 Nov 2022 Wei Chen, Weixiao Wan, Shiyue Wang, Peng Sun, Geoffrey Ye Li, Bo Ai

The CSI is compressed via linear projections at the UE, and is recovered at the BS using deep learning (DL) with plug-and-play priors (PPP).


Wireless Semantic Transmission via Revising Modules in Conventional Communications

no code implementations2 Oct 2022 Peiwen Jiang, Chao-Kai Wen, Shi Jin, Geoffrey Ye Li

Therefore, the novel semantic-based coding methods and performance metrics have been investigated and the designed semantic systems consist of various modules as in the conventional communications but with improved functions.

Learn to Adapt to New Environment from Past Experience and Few Pilot

no code implementations2 Sep 2022 Ouya Wang, Jiabao Gao, Geoffrey Ye Li

Most of the existing works are based on data-driven deep learning, which requires a significant amount of training data for the communication model to adapt to new environments and results in huge computing resources for collecting data and retraining the model.

Few-Shot Learning

Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems

no code implementations29 Jun 2022 Jiajia Guo, Chao-Kai Wen, Shi Jin, Geoffrey Ye Li

Many performance gains achieved by massive multiple-input and multiple-output depend on the accuracy of the downlink channel state information (CSI) at the transmitter (base station), which is usually obtained by estimating at the receiver (user terminal) and feeding back to the transmitter.

0/1 Deep Neural Networks via Block Coordinate Descent

no code implementations19 Jun 2022 HUI ZHANG, Shenglong Zhou, Geoffrey Ye Li, Naihua Xiu

The step function is one of the simplest and most natural activation functions for deep neural networks (DNNs).

3D Instance Segmentation

Robust Semantic Communications with Masked VQ-VAE Enabled Codebook

1 code implementation8 Jun 2022 Qiyu Hu, Guangyi Zhang, Zhijin Qin, Yunlong Cai, Guanding Yu, Geoffrey Ye Li

Although semantic communications have exhibited satisfactory performance for a large number of tasks, the impact of semantic noise and the robustness of the systems have not been well investigated.

Feature Importance

QoE-Aware Resource Allocation for Semantic Communication Networks

no code implementations28 May 2022 Lei Yan, Zhijin Qin, Rui Zhang, Yongzhao Li, Geoffrey Ye Li

Specifically, an approximate measure of semantic entropy is first developed to quantify the semantic information for different tasks, based on which a novel quality-of-experience (QoE) model is proposed.

Long-Lasting UAV-aided RIS Communications based on SWIPT

1 code implementation IEEE Wireless Communications and Networking Conference (WCNC) 2022 Haoran Peng, Li-Chun Wang, Geoffrey Ye Li, Ang-Hsun Tsai

Reconfigurable intelligent surface (RIS) is a promising technology for energy efficient wireless communications and has drawn significant attention recently.

CSI-fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network

no code implementations11 May 2022 BoWen Zhang, Houssem Sifaou, Geoffrey Ye Li

On the other hand, considering the generality of a tracking system, we decouple the tracking system from the CSI environments so that one tracking system for all environments becomes possible.

Denoising Indoor Localization

Deep Learning Enabled Semantic Communications with Speech Recognition and Synthesis

1 code implementation9 May 2022 Zhenzi Weng, Zhijin Qin, Xiaoming Tao, Chengkang Pan, Guangyi Liu, Geoffrey Ye Li

In this paper, we develop a deep learning based semantic communication system for speech transmission, named DeepSC-ST. We take the speech recognition and speech synthesis as the transmission tasks of the communication system, respectively.

speech-recognition Speech Recognition +1

Over-The-Air Federated Learning under Byzantine Attacks

no code implementations5 May 2022 Houssem Sifaou, Geoffrey Ye Li

One of the main challenges of FL is the communication overhead, where the model updates of the participating clients are sent to the central server at each global training round.

Federated Learning

FedGiA: An Efficient Hybrid Algorithm for Federated Learning

1 code implementation3 May 2022 Shenglong Zhou, Geoffrey Ye Li

Federated learning has shown its advances recently but is still facing many challenges, such as how algorithms save communication resources and reduce computational costs, and whether they converge.

Federated Learning

Federated Learning via Inexact ADMM

1 code implementation22 Apr 2022 Shenglong Zhou, Geoffrey Ye Li

One of the crucial issues in federated learning is how to develop efficient optimization algorithms.

Federated Learning

Efficient Wireless Federated Learning with Partial Model Aggregation

no code implementations20 Apr 2022 Zhixiong Chen, Wenqiang Yi, Arumugam Nallanathan, Geoffrey Ye Li

On this basis, we maximize the scheduled data size to minimize the global loss function through jointly optimize the device scheduling, bandwidth allocation, computation and communication time division policies with the assistance of Lyapunov optimization.

Federated Learning Scheduling

Wireless Semantic Communications for Video Conferencing

no code implementations16 Apr 2022 Peiwen Jiang, Chao-Kai Wen, Shi Jin, Geoffrey Ye Li

In this paper, we initially establish a basal semantic video conferencing (SVC) network, which dramatically reduces transmission resources while only losing detailed expressions.

Video Compression

Low-Complexity Multicast Beamforming for Millimeter Wave Communications

no code implementations12 Mar 2022 Zhaohui Li, Chenhao Qi, Geoffrey Ye Li

To develop a low-complexity multicast beamforming method for millimeter wave communications, we first propose a channel gain estimation method in this article.

Robust Semantic Communications Against Semantic Noise

no code implementations7 Feb 2022 Qiyu Hu, Guangyi Zhang, Zhijin Qin, Yunlong Cai, Guanding Yu, Geoffrey Ye Li

In this paper, we first propose a framework for the robust end-to-end semantic communication systems to combat the semantic noise.


Resource allocation for text semantic communications

1 code implementation16 Jan 2022 Lei Yan, Zhijin Qin, Rui Zhang, Yongzhao Li, Geoffrey Ye Li

Semantic communications have shown its great potential to improve the transmission reliability, especially in the low signal-to-noise regime.

Semantic Communications: Principles and Challenges

no code implementations30 Dec 2021 Zhijin Qin, Xiaoming Tao, Jianhua Lu, Wen Tong, Geoffrey Ye Li

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 accurate reception of each single symbol or bit regardless of its meaning.

Deep-Unfolding Beamforming for Intelligent Reflecting Surface assisted Full-Duplex Systems

no code implementations4 Dec 2021 Yanzhen Liu, Qiyu Hu, Yunlong Cai, Guanding Yu, Geoffrey Ye Li

Moreover, due to the high computational complexity caused by the matrix inversion computation in the SSCA-based optimization algorithm, we further develop a deep-unfolding neural network (NN) to address this issue.

Accretionary Learning with Deep Neural Networks

no code implementations21 Nov 2021 Xinyu Wei, Biing-Hwang Fred Juang, Ouya Wang, Shenglong Zhou, Geoffrey Ye Li

In this paper, we propose a new learning method named Accretionary Learning (AL) to emulate human learning, in that the set of objects to be recognized may not be pre-specified.

Robust Federated Learning via Over-The-Air Computation

no code implementations1 Nov 2021 Houssem Sifaou, Geoffrey Ye Li

This paper investigates the robustness of over-the-air federated learning to Byzantine attacks.

Federated Learning

Communication-Efficient ADMM-based Federated Learning

1 code implementation28 Oct 2021 Shenglong Zhou, Geoffrey Ye Li

Federated learning has shown its advances over the last few years but is facing many challenges, such as how algorithms save communication resources, how they reduce computational costs, and whether they converge.

Federated Learning

Task-Oriented Multi-User Semantic Communications for VQA Task

no code implementations16 Aug 2021 Huiqiang Xie, Zhijin Qin, Geoffrey Ye Li

In this letter, we consider a task-oriented multi-user semantic communication system for multimodal data transmission.

Question Answering Visual Question Answering

Semantic Communications for Speech Recognition

no code implementations22 Jul 2021 Zhenzi Weng, Zhijin Qin, Geoffrey Ye Li

The traditional communications transmit all the source date represented by bits, regardless of the content of source and the semantic information required by the receiver.

speech-recognition Speech Recognition

Deep Source-Channel Coding for Sentence Semantic Transmission with HARQ

no code implementations6 Jun 2021 Peiwen Jiang, Chao-Kai Wen, Shi Jin, Geoffrey Ye Li

Even if semantic communication has been successfully applied in the sentence transmission to reduce semantic errors, existing architecture is usually fixed in the codeword length and is inefficient and inflexible for the varying sentence length.


Deep Learning-based Implicit CSI Feedback in Massive MIMO

no code implementations21 May 2021 Muhan Chen, Jiajia Guo, Chao-Kai Wen, Shi Jin, Geoffrey Ye Li, Ang Yang

By using environment information, the NNs can achieve a more refined mapping between the precoding matrix and the PMI compared with codebooks.

Adaptive Channel Estimation Based on Model-Driven Deep Learning for Wideband mmWave Systems

no code implementations28 Apr 2021 Weijie Jin, Hengtao He, Chao-Kai Wen, Shi Jin, Geoffrey Ye Li

Channel estimation in wideband millimeter-wave (mmWave) systems is very challenging due to the beam squint effect.

Deep Multi-Stage CSI Acquisition for Reconfigurable Intelligent Surface Aided MIMO Systems

no code implementations23 Apr 2021 Shen Gao, Peihao Dong, Zhiwen Pan, Geoffrey Ye Li

This article aims to reduce huge pilot overhead when estimating the reconfigurable intelligent surface (RIS) relayed wireless channel.

On Channel Reciprocity in Reconfigurable Intelligent Surface Assisted Wireless Network

no code implementations5 Mar 2021 Wankai Tang, Xiangyu Chen, Ming Zheng Chen, Jun Yan Dai, Yu Han, Shi Jin, Qiang Cheng, Geoffrey Ye Li, Tie Jun Cui

Channel reciprocity greatly facilitates downlink precoding in time-division duplexing (TDD) multiple-input multiple-output (MIMO) communications without the need for channel state information (CSI) feedback.

Information Theory Information Theory

Semantic Communications for Speech Signals

no code implementations9 Dec 2020 Zhenzi Weng, Zhijin Qin, Geoffrey Ye Li

We consider a semantic communication system for speech signals, named DeepSC-S.

FusionNet: Enhanced Beam Prediction for mmWave Communications Using Sub-6GHz Channel and A Few Pilots

no code implementations6 Sep 2020 Chenghong Bian, Yuwen Yang, Feifei Gao, Geoffrey Ye Li

In this paper, we propose a new downlink beamforming strategy for mmWave communications using uplink sub-6GHz channel information and a very few mmWave pilots.

Data Augmentation

AnciNet: An Efficient Deep Learning Approach for Feedback Compression of Estimated CSI in Massive MIMO Systems

no code implementations17 Aug 2020 Yuyao Sun, Wei Xu, Lisheng Fan, Geoffrey Ye Li, George K. Karagiannidis

Accurate channel state information (CSI) feedback plays a vital role in improving the performance gain of massive multiple-input multiple-output (m-MIMO) systems, where the dilemma is excessive CSI overhead versus limited feedback bandwith.

Symbiotic Radio: Cognitive Backscattering Communications for Future Wireless Networks

no code implementations3 Jul 2020 Ying-Chang Liang, Qianqian Zhang, Erik G. Larsson, Geoffrey Ye Li

To exploit the full potential of SR, in this paper, we address three fundamental tasks in SR: (1) enhancing the backscattering link via active load; (2) achieving highly reliable communications through joint decoding; and (3) capturing PTx's RF signals using reconfigurable intelligent surfaces.

Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach

no code implementations30 Jun 2020 Hengtao He, Rui Wang, Weijie Jin, Shi Jin, Chao-Kai Wen, Geoffrey Ye Li

By utilizing the Stein's unbiased risk estimator loss, the LDGEC network can be trained only with limited measurements corresponding to the pilot symbols, instead of the real channel data.

Compressive Sensing Denoising

Deep Learning Enabled Semantic Communication Systems

no code implementations18 Jun 2020 Huiqiang Xie, Zhijin Qin, Geoffrey Ye Li, Biing-Hwang Juang

To justify the performance of semantic communications accurately, we also initialize a new metric, named sentence similarity.

Sentence Sentence Similarity +1

Acquisition of Channel State Information for mmWave Massive MIMO: Traditional and Machine Learning-based Approaches

no code implementations16 Jun 2020 Chenhao Qi, Peihao Dong, Wenyan Ma, Hua Zhang, Zaichen Zhang, Geoffrey Ye Li

The accuracy of channel state information (CSI) acquisition directly affects the performance of millimeter wave (mmWave) communications.

BIG-bench Machine Learning

Model-Driven DNN Decoder for Turbo Codes: Design, Simulation and Experimental Results

no code implementations16 Jun 2020 Yunfeng He, Jing Zhang, Shi Jin, Chao-Kai Wen, Geoffrey Ye Li

The TurboNet inherits the superiority of the max-log-MAP algorithm and DL tools and thus presents excellent error-correction capability with low training cost.

Framework on Deep Learning Based Joint Hybrid Processing for mmWave Massive MIMO Systems

1 code implementation5 Jun 2020 Peihao Dong, Hua Zhang, Geoffrey Ye Li

For millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, hybrid processing architecture is essential to significantly reduce the complexity and cost but is quite challenging to be jointly optimized over the transmitter and receiver.

Federated Learning and Wireless Communications

no code implementations11 May 2020 Zhijin Qin, Geoffrey Ye Li, Hao Ye

In contrast to other machine learning tools that require no communication resources, federated learning exploits communications between the central server and the distributed local clients to train and optimize a machine learning model.

Information Theory Signal Processing Information Theory

Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities

no code implementations2 May 2020 Mohamed A. ElMossallamy, Hongliang Zhang, Lingyang Song, Karim G. Seddik, Zhu Han, Geoffrey Ye Li

Recently there has been a flurry of research on the use of reconfigurable intelligent surfaces (RIS) in wireless networks to create smart radio environments.

Reinforcement Learning Based Cooperative Coded Caching under Dynamic Popularities in Ultra-Dense Networks

no code implementations8 Mar 2020 Shen Gao, Peihao Dong, Zhiwen Pan, Geoffrey Ye Li

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.

Q-Learning Reinforcement Learning (RL)

Deep Learning-based CSI Feedback for RIS-assisted Multi-user Systems

no code implementations6 Mar 2020 Jiajia Guo, Xi Yang, Chao-Kai Wen, Shi Jin, Geoffrey Ye Li

These enhancements are tasked with the precise retrieval and fusion of shared and individual data.

Information Theory Signal Processing Information Theory

Learn to Compress CSI and Allocate Resources in Vehicular Networks

no code implementations12 Aug 2019 Liang Wang, Hao Ye, Le Liang, Geoffrey Ye Li

The centralized decision unit employs a deep Q-network to allocate resources and then sends the decision results to all vehicles.

Decision Making Quantization

Compression and Acceleration of Neural Networks for Communications

no code implementations31 Jul 2019 Jiajia Guo, Jinghe Wang, Chao-Kai Wen, Shi Jin, Geoffrey Ye Li

Deep learning (DL) has achieved great success in signal processing and communications and has become a promising technology for future wireless communications.

Information Theory Signal Processing Information Theory

Learn to Allocate Resources in Vehicular Networks

no code implementations30 Jul 2019 Liang Wang, Hao Ye, Le Liang, Geoffrey Ye Li

Meanwhile, there exists an optimal number of continuous feedback and binary feedback, respectively.

Decision Making Quantization

Model-Driven Deep Learning for MIMO Detection

no code implementations22 Jul 2019 Hengtao He, Chao-Kai Wen, Shi Jin, Geoffrey Ye Li

In this paper, we investigate the model-driven deep learning (DL) for MIMO detection.

Deep Learning based Wireless Resource Allocation with Application to Vehicular Networks

no code implementations7 Jul 2019 Le Liang, Hao Ye, Guanding Yu, Geoffrey Ye Li

The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve the problem to a certain level of optimality.


Convolutional Neural Network based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis

1 code implementation14 Jun 2019 Jiajia Guo, Chao-Kai Wen, Shi Jin, Geoffrey Ye Li

Massive multiple-input multiple-output (MIMO) is a promising technology to increase link capacity and energy efficiency.

Signal Processing Information Theory Information Theory

Graph Embedding based Wireless Link Scheduling with Few Training Samples

1 code implementation7 Jun 2019 Mengyuan Lee, Guanding Yu, Geoffrey Ye Li

In this paper, we propose a novel graph embedding based method for link scheduling in D2D networks.

Graph Embedding Scheduling

Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning

1 code implementation8 May 2019 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 frequency spectrum preoccupied by vehicle-to-infrastructure (V2I) links.

Information Theory Information Theory

Deep Learning Based on Orthogonal Approximate Message Passing for CP-Free OFDM

no code implementations4 May 2019 Jing Zhang, Hengtao He, Chao-Kai Wen, Shi Jin, Geoffrey Ye Li

The DL-OAMP receiver includes a channel estimation neural network (CE-Net) and a signal detection neural network based on OAMP, called OAMP-Net.

Artificial Intelligence-aided Receiver for A CP-Free OFDM System: Design, Simulation, and Experimental Test

no code implementations12 Mar 2019 Jing Zhang, Chao-Kai Wen, Shi Jin, Geoffrey Ye Li

The AI receiver includes a channel estimation neural network (CE-NET) and a signal detection neural network based on orthogonal approximate message passing (OAMP), called OAMP-NET.

Information Theory Information Theory

Deep Learning based End-to-End Wireless Communication Systems with Conditional GAN as Unknown Channel

no code implementations6 Mar 2019 Hao Ye, Le Liang, Geoffrey Ye Li, Biing-Hwang Fred Juang

We propose to use a conditional generative adversarial net (GAN) to represent channel effects and to bridge the transmitter DNN and the receiver DNN so that the gradient of the transmitter DNN can be back-propagated from the receiver DNN.

Information Theory Information Theory

Learning to Branch: Accelerating Resource Allocation in Wireless Networks

1 code implementation5 Mar 2019 Mengyuan Lee, Guanding Yu, Geoffrey Ye Li

Moreover, we develop a mixed training strategy to further reinforce the generalization ability and a deep neural network (DNN) with a novel loss function to achieve better dynamic control over optimality and computational complexity.

Information Theory Information Theory

AI-Aided Online Adaptive OFDM Receiver: Design and Experimental Results

no code implementations17 Dec 2018 Peiwen Jiang, Tianqi Wang, Bin Han, Xuanxuan Gao, Jing Zhang, Chao-Kai Wen, Shi Jin, Geoffrey Ye Li

From the OTA test, the AI-aided OFDM receivers, especially the SwitchNet receiver, are robust to real environments and promising for future communication systems.

Model-Driven Deep Learning for Physical Layer Communications

no code implementations17 Sep 2018 Hengtao He, Shi Jin, Chao-Kai Wen, Feifei Gao, Geoffrey Ye Li, Zongben Xu

Intelligent communication is gradually considered as the mainstream direction in future wireless communications.

Intelligent Communication

Channel Agnostic End-to-End Learning based Communication Systems with Conditional GAN

no code implementations2 Jul 2018 Hao Ye, Geoffrey Ye Li, Biing-Hwang Fred Juang, Kathiravetpillai Sivanesan

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 as encoding, decoding, modulation, and equalization.

Information Theory Information Theory

Toward Intelligent Vehicular Networks: A Machine Learning Framework

no code implementations1 Apr 2018 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 vehicular environments and thus motive rethinking of traditional wireless design methodologies.

BIG-bench Machine Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.