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Found 24 papers, 3 papers with code
Near/Far-Field Channel Estimation For Terahertz Systems With ELAAs: A Block-Sparse-Aware Approach
In this paper, we consider the problem of hybrid near/far-field channel estimation by taking spherical wave propagation into account.
Communication Efficient ConFederated Learning: An Event-Triggered SAGA Approach
Due to the potentially massive number of users involved, it is crucial to reduce the communication overhead of the CFL system.
Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding
This research aims to accelerate the inference speed of large language models (LLMs) with billions of parameters.
BiTA: Bi-Directional Tuning for Lossless Acceleration in Large Language Models
Large language models (LLMs) commonly employ autoregressive generation during inference, leading to high memory bandwidth demand and consequently extended latency.
Diffusion Probabilistic Model Based Accurate and High-Degree-of-Freedom Metasurface Inverse Design
Inverse design methods based on optimization algorithms, such as evolutionary algorithms, and topological optimizations, have been introduced to design metamaterials.
Algorithm Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks
To improve the efficiency of the D-ADMM in distributed BSs, we develop a monodirectional information exchange strategy with a small signaling overhead.
Compressed CPD-Based Channel Estimation and Joint Beamforming for RIS-Assisted Millimeter Wave Communications
Also, by utilizing the singular value decomposition-like structure of the effective channel, this paper develops a joint active and passive beamforming method based on the estimated cascade channels.
Confederated Learning: Federated Learning with Decentralized Edge Servers
Most studies on FL consider a centralized framework, in which a single server is endowed with a central authority to coordinate a number of devices to perform model training in an iterative manner.
Spatial Channel Covariance Estimation and Two-Timescale Beamforming for IRS-Assisted Millimeter Wave Systems
We consider the problem of spatial channel covariance matrix (CCM) estimation for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) communication systems.
Compressed Channel Estimation for IRS-Assisted Millimeter Wave OFDM Systems: A Low-Rank Tensor Decomposition-Based Approach
We consider the problem of downlink channel estimation for intelligent reflecting surface (IRS)-assisted millimeter Wave (mmWave) orthogonal frequency division multiplexing (OFDM) systems.
Beam Training and Alignment for RIS-Assisted Millimeter Wave Systems:State of the Art and Beyond
The deployment of RIS, however, complicates the system architecture and poses a significant challenge for beam training (BT)/ beam alignment (BA), a process that is required to establish a reliable link between the transmitter and the receiver.
Joint Active and Passive Beamforming for IRS-Assisted Radar
Intelligent reflecting surface (IRS) is a promising technology being considered for future wireless communications due to its ability to control signal propagation.
Recent Advances on Sub-Nyquist Sampling-Based Wideband Spectrum Sensing
Cognitive radio (CR) is a promising technology enabling efficient utilization of the spectrum resource for future wireless systems.
Fast Beam Training and Alignment for IRS-Assisted Millimeter Wave/Terahertz Systems
Intelligent reflecting surface (IRS) has emerged as a competitive solution to address blockage issues in millimeter wave (mmWave) and Terahertz (THz) communications due to its capability of reshaping wireless transmission environments.
Signal Detection in Distributed MIMO Radar with Non-Orthogonal Waveforms and Sync Errors
We consider the impact of non-orthogonal waveforms and their cross terms on target detection with or without timing, frequency, and phase errors.
Power Allocation for Coexisting Multicarrier Radar and Communication Systems in Cluttered Environments
The first is a joint design where the subchannel powers of both the radar and communication systems are jointly optimized.
Compressed Channel Estimation and Joint Beamforming for Intelligent Reflecting Surface-Assisted Millimeter Wave Systems
In this paper, we consider channel estimation for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) systems, where an IRS is deployed to assist the data transmission from the base station (BS) to a user.
Intelligent Reflecting Surface-Assisted Millimeter Wave Communications: Joint Active and Passive Precoding Design
In this framework, we study joint active and passive precoding design for IRS-assisted mmWave systems, where multiple IRSs are deployed to assist the data transmission from a base station (BS) to a single-antenna receiver.
Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models
The problem of low rank matrix completion is considered in this paper.
Robust Bayesian Compressed sensing
We consider the problem of robust compressed sensing whose objective is to recover a high-dimensional sparse signal from compressed measurements corrupted by outliers.
Low-Rank Tensor Decomposition-Aided Channel Estimation for Millimeter Wave MIMO-OFDM Systems
Different from most existing studies that are concerned with narrowband channels, we consider estimation of wideband mmWave channels with frequency selectivity, which is more appropriate for mmWave MIMO-OFDM systems.
Information Theory Information Theory
An Iterative Reweighted Method for Tucker Decomposition of Incomplete Multiway Tensors
In this paper, we focus on Tucker decomposition which represents an Nth-order tensor in terms of N factor matrices and a core tensor via multilinear operations.
Sparse Bayesian Dictionary Learning with a Gaussian Hierarchical Model
In this paper, we propose a new hierarchical Bayesian model for dictionary learning, in which a Gaussian-inverse Gamma hierarchical prior is used to promote the sparsity of the representation.
Pattern-Coupled Sparse Bayesian Learning for Recovery of Block-Sparse Signals
In this paper, we develop a new sparse Bayesian learning method for recovery of block-sparse signals with unknown cluster patterns.
