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Found 83 papers, 17 papers with code
Semantic Satellite Communications Based on Generative Foundation Model
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
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
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
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
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
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.
Key Issues in Wireless Transmission for NTN-Assisted Internet of Things
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
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
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.
Federated Reinforcement Learning for Resource Allocation in V2X Networks
In this paper, we explore resource allocation in a V2X network under the framework of federated reinforcement learning (FRL).
Compression Ratio Learning and Semantic Communications for Video Imaging
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.
Communication-Efficient Decentralized Federated Learning via One-Bit Compressive Sensing
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.
Deep Plug-and-Play Prior for Multitask Channel Reconstruction in Massive MIMO Systems
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.
RIS-Enhanced Semantic Communications Adaptive to User Requirements
Simulation results demonstrate the adaptability and efficiency of the RIS-SC framework across diverse channel conditions and user requirements.
Semantic-Aware Image Compressed Sensing
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.
Deep-Unfolding for Next-Generation Transceivers
Then, some endeavors in applying deep-unfolding approaches in next-generation advanced transceiver design are presented.
Distributed Two-tier DRL Framework for Cell-Free Network: Association, Beamforming and Power Allocation
Intelligent wireless networks have long been expected to have self-configuration and self-optimization capabilities to adapt to various environments and demands.
Semantic Communication with Memory
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.
Semantic Communications with Variable-Length Coding for Extended Reality
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
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
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.
Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMO
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.
Graph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directions
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
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
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
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.
Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems
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
The step function is one of the simplest and most natural activation functions for deep neural networks (DNNs).
Robust Semantic Communications with Masked VQ-VAE Enabled Codebook
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.
QoE-Aware Resource Allocation for Semantic Communication Networks
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
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
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.
Deep Learning Enabled Semantic Communications with Speech Recognition and Synthesis
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.
Over-The-Air Federated Learning under Byzantine Attacks
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.
FedGiA: An Efficient Hybrid Algorithm for Federated Learning
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 via Inexact ADMM
One of the crucial issues in federated learning is how to develop efficient optimization algorithms.
Efficient Wireless Federated Learning with Partial Model Aggregation
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.
Wireless Semantic Communications for Video Conferencing
In this paper, we initially establish a basal semantic video conferencing (SVC) network, which dramatically reduces transmission resources while only losing detailed expressions.
Low-Complexity Multicast Beamforming for Millimeter Wave Communications
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
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
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
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
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
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
This paper investigates the robustness of over-the-air federated learning to Byzantine attacks.
Communication-Efficient ADMM-based Federated Learning
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.
Task-Oriented Multi-User Semantic Communications for VQA Task
In this letter, we consider a task-oriented multi-user semantic communication system for multimodal data transmission.
Semantic Communications for Speech Recognition
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.
Deep Source-Channel Coding for Sentence Semantic Transmission with HARQ
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
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
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
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
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
Phase Retrieval using Expectation Consistent Signal Recovery Algorithm based on Hypernetwork
In particular, we introduce a hypernetwork to generate the damping factors for GEC-SR.
Semantic Communications for Speech Signals
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
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.
AnciNet: An Efficient Deep Learning Approach for Feedback Compression of Estimated CSI in Massive MIMO Systems
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
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
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.
Deep Learning Enabled Semantic Communication Systems
To justify the performance of semantic communications accurately, we also initialize a new metric, named sentence similarity.
Model-Driven DNN Decoder for Turbo Codes: Design, Simulation and Experimental Results
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.
Acquisition of Channel State Information for mmWave Massive MIMO: Traditional and Machine Learning-based Approaches
The accuracy of channel state information (CSI) acquisition directly affects the performance of millimeter wave (mmWave) communications.
Framework on Deep Learning Based Joint Hybrid Processing for mmWave Massive MIMO Systems
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
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
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
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.
Deep Learning-based CSI Feedback for RIS-assisted Multi-user Systems
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
The centralized decision unit employs a deep Q-network to allocate resources and then sends the decision results to all vehicles.
Compression and Acceleration of Neural Networks for Communications
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
Meanwhile, there exists an optimal number of continuous feedback and binary feedback, respectively.
Model-Driven Deep Learning for MIMO Detection
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
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
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
In this paper, we propose a novel graph embedding based method for link scheduling in D2D networks.
Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning
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
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
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
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
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
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
Intelligent communication is gradually considered as the mainstream direction in future wireless communications.
Channel Agnostic End-to-End Learning based Communication Systems with Conditional GAN
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
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.
