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Beamforming Optimization for Active RIS-Aided Multiuser Communications With Hardware Impairments
Specifically, by exploiting the majorization-minimization approach, each subproblem is reformulated into a tractable surrogate problem, whose closed-form solutions are obtained by Lagrange dual decomposition approach and element-wise alternating sequential optimization method.
Semi-Passive Intelligent Reflecting Surface Enabled Sensing Systems
Towards this goal, we begin by deriving the CRB for the direction-of-arrival (DoA) estimation in closed-form and then theoretically analyze the IRS reflecting elements and sensors allocation design based on the CRB under the point target case with a single-antenna base station (BS).
Deep-Learning-Based Channel Estimation for IRS-Assisted ISAC System
A deep-learning framework is proposed to estimate the sensing and communication (S&C) channels in such a system.
Deep-Learning Channel Estimation for IRS-Assisted Integrated Sensing and Communication System
This problem is challenging due to the lack of signal processing capacity in passive IRS, as well as the presence of mutual interference between sensing and communication (SAC) signals in ISAC systems.
Extreme Learning Machine-based Channel Estimation in IRS-Assisted Multi-User ISAC System
Multi-user integrated sensing and communication (ISAC) assisted by intelligent reflecting surface (IRS) has been recently investigated to provide a high spectral and energy efficiency transmission.
Large Language Model Enhanced Multi-Agent Systems for 6G Communications
The rapid development of the Large Language Model (LLM) presents huge opportunities for 6G communications, e. g., network optimization and management by allowing users to input task requirements to LLMs by nature language.
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.
GNN-Based Beamforming for Sum-Rate Maximization in MU-MISO Networks
This paper studies the GNN-based learning approach for the sum-rate maximization in multiple-user multiple-input single-output (MU-MISO) networks subject to the users' individual data rate requirements and the power budget of the base station.
Liquid Crystal-Based RIS for VLC Transmitters: Performance Analysis, Challenges, and Opportunities
This article presents a novel approach of using reconfigurable intelligent surfaces (RISs) in the transmitter of indoor visible light communication (VLC) systems to enhance data rate uniformity and maintain adequate illumination.
Physical Layer Security for NOMA Systems: Requirements, Issues, and Recommendations
Nevertheless, from a security view-point, when multiple users are utilizing the same time-frequency resource, there may be concerns regarding keeping information confidential.
Deep Reinforcement Learning for RIS-Assisted FD Systems: Single or Distributed RIS?
It is further shown that the proposed algorithm significantly improves the sum-rate compared to the non-optimized scenario in both single and distributed RIS deployment schemes.
Two-timescale Resource Allocation for Automated Networks in IIoT
The rapid technological advances of cellular technologies will revolutionize network automation in industrial internet of things (IIoT).
Digital RIS (DRIS): The Future of Digital Beam Management in RIS-Assisted OWC Systems
We discuss the concept of DRIS and the application of DSP methods to the physical material.
Federated Learning for Smart Healthcare: A Survey
Recent advances in communication technologies and Internet-of-Medical-Things have transformed smart healthcare enabled by artificial intelligence (AI).
Hybrid-Layers Neural Network Architectures for Modeling the Self-Interference in Full-Duplex Systems
In contrast to the state-of-the-art NNs that employ dense or recurrent layers for SI modeling, the proposed NNs exploit, in a novel manner, a combination of different hidden layers (e. g., convolutional, recurrent, and/or dense) in order to model the SI with lower computational complexity than the polynomial and the state-of-the-art NN-based cancelers.
Perturbation Theory-Aided Learned Digital Back-Propagation Scheme for Optical Fiber Nonlinearity Compensation
The proposed scheme is evaluated by numerical simulations of a single carrier optical fiber communication system operating at 32 Gbaud with 64-quadrature amplitude modulation and 20*80 km transmission distance.
Deep Reinforcement Learning for Optimizing RIS-Assisted HD-FD Wireless Systems
The complexity analysis and Monte Carlo simulations illustrate that the proposed DRL algorithm significantly improves the rate compared to the non-optimized scenario in both HD and FD operating modes using a single parameter setting.
Integration of Backscatter Communication with Multi-cell NOMA: A Spectral Efficiency Optimization under Imperfect SIC
This paper proposes a new optimization approach to enhance the spectral efficiency of nonorthogonal multiple access (NOMA)-BC network.
Deep Learning-based Time-varying Channel Estimation for RIS Assisted Communication
Reconfigurable intelligent surface (RIS) is considered as a revolutionary technology for future wireless communication networks.
Deep Learning Based Antenna-time Domain Channel Extrapolation for Hybrid mmWave Massive MIMO
We design a latent ordinary differential equation (ODE)-based network under the variational auto-encoder (VAE) framework to learn the mapping function from the partial uplink channels to the full downlink ones at the BS side.
Deep Reinforcement Learning for Intelligent Reflecting Surface-assisted D2D Communications
In this paper, we propose a deep reinforcement learning (DRL) approach for solving the optimisation problem of the network's sum-rate in device-to-device (D2D) communications supported by an intelligent reflecting surface (IRS).
Cascaded Channel Estimation for RIS Assisted mmWave MIMO Transmissions
Channel estimation is challenging for the reconfigurable intelligence surface (RIS) assisted millimeter wave (mmWave) communications.
Deep Learning Based RIS Channel Extrapolation with Element-grouping
Reconfigurable intelligent surface (RIS) is considered as a revolutionary technology for future wireless communication networks.
Coverage Characterization of STAR-RIS Networks: NOMA and OMA
A sum coverage range maximization problem is formulated for both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA), where the resource allocation at the access point and the transmission and reflection coefficients at the STAR-RIS are jointly optimized to satisfy the communication requirements of users.
Design of a Power Amplifying-RIS for Free-Space Optical Communication Systems
Leveraging on this capacity of a doped LC, we mitigate the double-attenuation faced by the transmitted signal.
Incremental Relaying for Power Line Communication: Performance Analysis and Power Allocation
By employing power allocation to minimize the outage probability, it is realized that the power should be allocated in accordance with the inter-node distances and channel parameters.
Secrecy Performance of Small-Cell Networks with Transmitter Selection and Unreliable Backhaul under Spectrum Sharing Environment
{We also propose an optimal selection scheme and compare performances with the sub-optimal selection schemes.}
Deep Learning based Channel Extrapolation for Large-Scale Antenna Systems: Opportunities, Challenges and Solutions
With the depletion of spectrum, wireless communication systems turn to exploit large antenna arrays to achieve the degree of freedom in space domain, such as millimeter wave massive multi-input multioutput (MIMO), reconfigurable intelligent surface assisted communications and cell-free massive MIMO.
Information Theory Signal Processing Information Theory
Recurrent Neural Network Assisted Transmitter Selection for Secrecy in Cognitive Radio Network
In this paper, we apply the long short-term memory (LSTM), an advanced recurrent neural network based machine learning (ML) technique, to the problem of transmitter selection (TS) for secrecy in an underlay small-cell cognitive radio network with unreliable backhaul connections.
Performance Analysis of RIS-Based nT-FSO Link Over G-G Turbulence With Pointing Errors
One of the main problems faced by communication systems is the presence of skip-zones in the targeted areas.
STAR-RISs: Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surfaces
In this letter, simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) are studied.
Re-configurable Intelligent Surface-based VLC Receivers Using Tunable Liquid-crystals: The Concept
Visible light communication (VLC) enables access to huge unlicensed bandwidth, a higher security level, and no radio frequency interference.
Ordinary Differential Equation-based CNN for Channel Extrapolation over RIS-assisted Communication
The reconfigurable intelligent surface (RIS) is considered as a promising new technology for reconfiguring wireless communication environments.
Information Theory Information Theory
Energy Efficiency Maximization in RIS-Aided Cell-Free Network with Limited Backhaul
Integrating the reconfigurable intelligent surface in a cell-free (RIS-CF) network is an effective solution to improve the capacity and coverage of future wireless systems with low cost and power consumption.
Robotic Communications for 5G and Beyond: Challenges and Research Opportunities
Furthermore, dynamic trajectory design and resource allocation for both indoor and outdoor robots are provided to verify the performance of robotic communications in the context of typical robotic application scenarios.
Robotics Systems and Control Signal Processing Systems and Control
Full-Duplex Non-Orthogonal Multiple Access Cooperative Overlay Spectrum-Sharing Networks with SWIPT
Numerical results show that FD, SWIPT, and NOMA techniques greatly boost the performance of cooperative spectrum-sharing network in terms of outage probability, system throughput, and sum rate compared to that of half-duplex NOMA and the conventional orthogonal multiple access-time division multiple access networks.
Learning-Assisted User Clustering in Cell-Free Massive MIMO-NOMA Networks
To address this problem, we develop two efficient unsupervised machine learning (ML) based UC algorithms, namely k-means++ and improved k-means++, to effectively cluster users into disjoint clusters in cell-free massive multiple-input multiple-output (CFmMIMO) system.
Low Complexity Neural Network Structures for Self-Interference Cancellation in Full-Duplex Radio
The core idea of these two structures is to mimic the non-linearity and memory effect introduced to the SI signal in order to achieve proper SI cancellation while exhibiting low computational complexity.
Towards the Use of Re-configurable Intelligent Surfaces in VLC Systems: Beam Steering
On the other hand, re-configurable intelligent surface (RIS) is a technology exploited in radio frequency to solve dead zones and loss of signal.
Deep Learning Based Antenna Selection for Channel Extrapolation in FDD Massive MIMO
In massive multiple-input multiple-output (MIMO) systems, the large number of antennas would bring a great challenge for the acquisition of the accurate channel state information, especially in the frequency division duplex mode.
Deep Learning Optimized Sparse Antenna Activation for Reconfigurable Intelligent Surface Assisted Communication
In this paper, we add signal processing units for a few antennas at RIS to partially acquire the channels.
On the Complexity Reduction of Uplink Sparse Code Multiple Access for Spatial Modulation
The first algorithm is referred to as successive user detection (SUD), while the second algorithm is the modified version of SUD, namely modified SUD (MSUD).
Rate-Splitting Multiple Access: Unifying NOMA and SDMA in MISO VLC Channels
Then, we introduce rate-splitting multiple access (RSMA), which was initially proposed for RF systems and discuss its potentials in VLC systems.
Large Intelligent Surface Assisted Non-Orthogonal Multiple Access: Performance Analysis
Specifically, we consider a downlink NOMA system, in which data transmission between a base station (BS) and $L$ NOMA users is assisted by an LIS comprising $M$ reflective elements.
A Novel Heap-based Pilot Assignment for Full Duplex Cell-Free Massive MIMO with Zero-Forcing
This paper investigates the combined benefits of full-duplex (FD) and cell-free massive multiple-input multipleoutput (CF-mMIMO), where a large number of distributed access points (APs) having FD capability simultaneously serve numerous uplink and downlink user equipments (UEs) on the same time-frequency resources.
Spectral-Energy Efficiency Trade-off-based Beamforming Design for MISO Non-Orthogonal Multiple Access Systems
In particular, we formulate a joint SE-EE based design as a multi-objective optimization (MOO) problem to achieve a good tradeoff between the two performance metrics.
A Prospective Look: Key Enabling Technologies, Applications and Open Research Topics in 6G Networks
To accommodate the stringent requirements of their prospective applications, which are data-driven and defined by extremely low-latency, ultra-reliable, fast and seamless wireless connectivity, research initiatives are currently focusing on a progressive roadmap towards the sixth generation (6G) networks.
Fiber Nonlinearity Mitigation via the Parzen Window Classifier for Dispersion Managed and Unmanaged Links
Machine learning techniques have recently received significant attention as promising approaches to deal with the optical channel impairments, and in particular, the nonlinear effects.
A Machine Learning-Based Detection Technique for Optical Fiber Nonlinearity Mitigation
In this case, digital back propagation compensates for the deterministic nonlinearity and the Parzen window deals with the stochastic nonlinear signal-noise interactions, which are not taken into account by digital back propagation.
