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Found 52 papers, 8 papers with code
Deep Learning Based Near-Field User Localization with Beam Squint in Wideband XL-MIMO Systems
Secondly, a user localization scheme combining CBS and beam training is proposed.
An Overview on IRS-Enabled Sensing and Communications for 6G: Architectures, Fundamental Limits, and Joint Beamforming Designs
Next, we consider a single IRS to facilitate integrated sensing and communication (ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming.
Low-Complexity Minimum BER Precoder Design for ISAC Systems: A Delay-Doppler Perspective
Orthogonal time frequency space (OTFS) modulation is anticipated to be a promising candidate for supporting integrated sensing and communications (ISAC) systems, which is considered as a pivotal technique for realizing next generation wireless networks.
Active-IRS-Enabled Target Detection
This letter studies an active intelligent reflecting surface (IRS)-enabled non-line-of-sight (NLoS) target detection system, in which an active IRS equipped with active reflecting elements and sensors is strategically deployed to facilitate target detection in the NLoS region of the base station (BS) by processing echo signals through the BS-IRS-target-IRS link.
Globally Optimal Movable Antenna-Enhanced multi-user Communication: Discrete Antenna Positioning, Motion Power Consumption, and Imperfect CSI
In particular, we optimize the beamforming and the MA positions at the BS to minimize the total BS power consumption, encompassing both radiated and MA motion power while guaranteeing a minimum required SINR for each user.
Networked Integrated Sensing and Communications for 6G Wireless Systems
Furthermore, we analyze the mean square error of the proposed distributed algorithm as a networked sensing performance metric and propose a beamforming design for the proposed network ISAC scheme to maximize the networked sensing accuracy and communication performance subject to a transmit power constraint.
Hybrid-Field Channel Estimation for XL-MIMO Systems with Stochastic Gradient Pursuit Algorithm
For the first scenario in which the prior knowledge of the specific proportion of the number of near-field and far-field channel paths is known, the scheme can effectively leverage the angular-domain sparsity of the far-field channels and the polar-domain sparsity of the near-field channels such that the channel estimation in these two fields can be performed separately.
Secure Communications in Near-Filed ISCAP Systems with Extremely Large-Scale Antenna Arrays
In this system, the BS sends confidential messages to a single communication user (CU), and at the same time wirelessly senses a point target and charges multiple energy receivers (ERs).
Intelligent Reflecting Surface-Enabled Anti-Detection for Secure Sensing and Communications
The ever-increasing reliance on wireless communication and sensing has led to growing concerns over the vulnerability of sensitive information to unauthorized detection and interception.
DRL-Based Orchestration of Multi-User MISO Systems with Stacked Intelligent Metasurfaces
Stacked intelligent metasurfaces (SIM) represents an advanced signal processing paradigm that enables over-the-air processing of electromagnetic waves at the speed of light.
Integrated Sensing, Communication, and Powering (ISCAP): Towards Multi-functional 6G Wireless Networks
Specifically, a multi-functional base station (BS) can enable multi-functional transmission, by exploiting the same radio signals to perform target/environment sensing, wireless communication, and wireless power transfer (WPT), simultaneously.
Optimal BER Minimum Precoder Design for OTFS-Based ISAC Systems
This paper investigates the bit error rate (BER) minimum pre-coder design for an orthogonal time frequency space (OTFS)-based integrated sensing and communications (ISAC) system, which is considered as a promising technique for enabling future wireless networks.
Fully-Passive versus Semi-Passive IRS-Enabled Sensing: SNR and CRB Comparison
Accordingly, we analyze the sensing signal-to-noise ratio (SNR) performance for a target detection scenario and the estimation Cram\'er-Rao bound (CRB) performance for a target's direction-of-arrival (DoA) estimation scenario, in cases where the transmit beamforming at the BS and the reflective beamforming at the IRS are jointly optimized.
A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading
To improve the MEC performance, it is required to design an optimal offloading strategy that includes offloading decision (i. e., whether offloading or not) and computational resource allocation of MEC.
A Partially Observable Deep Multi-Agent Active Inference Framework for Resource Allocation in 6G and Beyond Wireless Communications Networks
Simulation results demonstrate that our proposed framework can significantly improve the sum transmission rate of the secondary network compared to various benchmark schemes.
Communication-Efficient Framework for Distributed Image Semantic Wireless Transmission
Multi-node communication, which refers to the interaction among multiple devices, has attracted lots of attention in many Internet-of-Things (IoT) scenarios.
Energy-Efficient Beamforming Design for Integrated Sensing and Communications Systems
In this paper, we investigate the design of energy-efficient beamforming for an ISAC system, where the transmitted waveform is optimized for joint multi-user communication and target estimation simultaneously.
Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving
While federated learning (FL) improves the generalization of end-to-end autonomous driving by model aggregation, the conventional single-hop FL (SFL) suffers from slow convergence rate due to long-range communications among vehicles and cloud server.
Predictive Precoder Design for OTFS-Enabled URLLC: A Deep Learning Approach
This paper investigates the orthogonal time frequency space (OTFS) transmission for enabling ultra-reliable low-latency communications (URLLC).
Edge Computing for Semantic Communication Enabled Metaverse: An Incentive Mechanism Design
Fourth, we present the design of the DL-based auction for edge resource allocation in SemCom-enabled Metaverse.
Integrated Communication and Positioning Design in RIS-empowered OFDM System: a Correlation Dispersion Scheme
The channel frequency responses on pilots (CFROPs) of places of interest are used for online mapping with the offline CFROP database.
Scalable Predictive Beamforming for IRS-Assisted Multi-User Communications: A Deep Learning Approach
With the proposed predictive approach, we can avoid full-scale CSI estimation and facilitate low-dimensional CE for transmit beamforming design such that the signaling overhead is reduced by a scale of $\frac{1}{N}$, where $N$ is the number of IRS elements.
Deep CLSTM for Predictive Beamforming in Integrated Sensing and Communication-enabled Vehicular Networks
Predictive beamforming design is an essential task in realizing high-mobility integrated sensing and communication (ISAC), which highly depends on the accuracy of the channel prediction (CP), i. e., predicting the angular parameters of users.
Grant-Free NOMA-OTFS Paradigm: Enabling Efficient Ubiquitous Access for LEO Satellite Internet-of-Things
With the blooming of Internet-of-Things (IoT), we are witnessing an explosion in the number of IoT terminals, triggering an unprecedented demand for ubiquitous wireless access globally.
Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Aided Tera-Hertz Massive MIMO
Then, to acquire accurate CSI at the BS for the investigated RSMA precoding scheme to achieve higher spectral efficiency, we propose a CSI acquisition network (CAN) with low pilot and feedback signaling overhead, where the downlink pilot transmission, CSI feedback at the user equipments (UEs), and CSI reconstruction at the BS are modeled as an end-to-end neural network based on Transformer.
On the Energy-Efficiency Maximization for IRS-Assisted MIMOME Wiretap Channels
Security and energy efficiency have become crucial features in the modern-era wireless communication.
Multi-Point Integrated Sensing and Communication: Fusion Model and Functionality Selection
Integrated sensing and communication (ISAC) represents a paradigm shift, where previously competing wireless transmissions are jointly designed to operate in harmony via the shared use of the hardware platform for improving the spectral and energy efficiencies.
Disentangled Representation Learning for RF Fingerprint Extraction under Unknown Channel Statistics
Deep learning (DL) applied to a device's radio-frequency fingerprint~(RFF) has attracted significant attention in physical-layer authentication due to its extraordinary classification performance.
Reconfigurable Intelligent Surface-Aided 6G Massive Access: Coupled Tensor Modeling and Sparse Bayesian Learning
Then, by associating the data sequences to multiple rank-one tensors and exploiting the angular sparsity of the RIS-BS channel, the detection problem is cast as a high-order coupled tensor decomposition problem without the need of exploiting pilot sequences.
Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification
However, it is challenging to determine the network resources and road sensor placements for multi-stage training with multi-modal datasets in multi-variant scenarios.
Secure Communication in Multifunctional Active Intelligent Reflection Surface-assisted Systems
In this paper, the secure performance of multiuser multiple-input single-output wireless communications systems assisted by a multifunctional active intelligent reflection surface (IRS) is investigated.
Unit-Modulus Wireless Federated Learning Via Penalty Alternating Minimization
Wireless federated learning (FL) is an emerging machine learning paradigm that trains a global parametric model from distributed datasets via wireless communications.
Learning-based Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks
Specifically, the convolution and LSTM modules are successively adopted in the proposed HCL-Net to exploit the spatial and temporal dependencies of communication channels to further improve the learning performance.
A Generalizable Model-and-Data Driven Approach for Open-Set RFF Authentication
To enable the discrimination of RFF from both known and unknown devices, we propose a new end-to-end deep learning framework for extracting RFFs from raw received signals.
LEO Satellite Constellations for 5G and Beyond: How Will They Reshape Vertical Domains?
The rapid development of communication technologies in the past decades has provided immense vertical opportunities for individuals and enterprises.
Integrated Sensing and Communication-assisted Orthogonal Time Frequency Space Transmission for Vehicular Networks
Benefiting from the OTFS-ISAC signals, the roadside unit (RSU) is capable of simultaneously transmitting downlink information to the vehicles and estimating the sensing parameters of vehicles, e. g., locations and speeds, based on the reflected echoes.
Hybrid Relay-Reflecting Intelligent Surface-Aided Wireless Communications: Opportunities, Challenges, and Future Perspectives
In this paper, we provide an overview of a hybrid relay-reflecting intelligent surface (HR-RIS) architecture, in which only a few elements are active and connected to power amplifiers and radio frequency chains.
Terahertz Ultra-Massive MIMO-Based Aeronautical Communications in Space-Air-Ground Integrated Networks
According to the proposed prior-aided iterative angle estimation algorithm, azimuth/elevation angles can be estimated, and these angles are adopted to achieve precise beam-alignment and refine GTTDU module for further eliminating delay-beam squint.
Information Theory Signal Processing Information Theory
Edge Federated Learning Via Unit-Modulus Over-The-Air Computation
Simulation results show that the proposed UMAirComp framework with PAM algorithm achieves a smaller mean square error of model parameters' estimation, training loss, and test error compared with other benchmark schemes.
Off-grid Channel Estimation with Sparse Bayesian Learning for OTFS Systems
OTFS channel estimation is first formulated as a one-dimensional (1D) off-grid sparse signal recovery (SSR) problem based on a virtual sampling grid defined in the DD space, where the on-grid and off-grid components of the delay and Doppler shifts are separated for estimation.
Information Theory Information Theory
Physical Layer Security Enhancement With Reconfigurable Intelligent Surface-Aided Networks
Reconfigurable intelligent surface (RIS)-aided wireless communications have drawn significant attention recently.
Information Theory Signal Processing Information Theory
Deep Residual Network Empowered Channel Estimation for IRS-Assisted Multi-User Communication Systems
Channel estimation is of great importance in realizing practical intelligent reflecting surface-assisted multi-user communication (IRS-MC) systems.
Deep Transfer Learning-Assisted Signal Detection for Ambient Backscatter Communications
Existing tag signal detection algorithms inevitably suffer from a high bit error rate (BER) due to the difficulties in estimating the channel state information (CSI).
A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence
On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference.
Location-aware Predictive Beamforming for UAV Communications: A Deep Learning Approach
Unmanned aerial vehicle (UAV)-assisted communication becomes a promising technique to realize the beyond fifth generation (5G) wireless networks, due to the high mobility and maneuverability of UAVs which can adapt to heterogeneous requirements of different applications.
Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications
To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of channel and directly recover tag symbols.
Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User Communications
To this end, we first develop a versatile DReL-based channel estimation framework where a deep residual network (DRN)-based MMSE estimator is derived in terms of Bayesian philosophy.
Joint Radio Resource Allocation and Cooperative Caching in PD-NOMA-Based HetNets
Caching phase where the contents are saved in the storage of the BSs, and 2.
Secrecy Performance for Finite-Alphabet Inputs Over Fluctuating Two-Ray Channels in FDA Communications
To provide system design insights for practical communication systems equipped with the frequency diverse array (FDA), this paper investigates the secrecy performance of FDA systems exploiting finite-alphabet inputs over fluctuating two-ray (FTR) fading channels.
Hybrid Beamforming for Massive MIMO Over-the-Air Computation
It is shown that for the special case with a fully-digital receiver at the AP, the achieved MSE of the massive MIMO AirComp system is inversely proportional to the number of receive antennas.
Sum-Rate Maximization for Multiuser MISO Downlink Systems with Self-sustainable IRS
This paper investigates multiuser multi-input single-output (MISO) downlink communications assisted by a self-sustainable intelligent reflection surface (IRS), which can harvest power from the received signals.
Bayesian Predictive Beamforming for Vehicular Networks: A Low-overhead Joint Radar-Communication Approach
To accurately estimate the motion parameters of vehicles in real-time, we propose a novel message passing algorithm based on factor graph, which yields near optimal solution to the maximum a posteriori estimation.
