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Found 27 papers, 0 papers with code
Channel Fingerprint Construction for Massive MIMO: A Deep Conditional Generative Approach
To make the proposed CGDM lightweight, we further leverage the additivity of network layers and introduce a one-shot pruning approach along with a multi-objective knowledge distillation technique.
EnvCDiff: Joint Refinement of Environmental Information and Channel Fingerprints via Conditional Generative Diffusion Model
The proposed CDiff simultaneously refines environmental information and CF, reconstructing a fine-grained CF that incorporates environmental information, referred to as EnvCF, from its coarse-grained counterpart.
Near-Field Channel Estimation for XL-MIMO: A Deep Generative Model Guided by Side Information
This paper investigates the near-field (NF) channel estimation (CE) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems.
Statistical CSI Acquisition for Multi-frequency Massive MIMO Systems
First, we introduce a multi-frequency massive MIMO channel model and analyze the mapping relationship between two types of statistical CSI, namely the angular power spectrum (APS) and the spatial covariance matrix, along with their correlation across different frequency bands.
GNN-enabled Precoding for Massive MIMO LEO Satellite Communications
The integration of massive multiple-input multiple-output (MIMO) technology is being actively explored to enhance the performance of LEO satellite communications.
CKMDiff: A Generative Diffusion Model for CKM Construction via Inverse Problems with Learned Priors
One fundamental problem for CKM-enabled wireless systems lies in how to construct high-quality and complete CKM for all locations of interest, based on only limited and noisy on-site channel knowledge data.
CF-CGN: Channel Fingerprints Extrapolation for Multi-band Massive MIMO Transmission based on Cycle-Consistent Generative Networks
In this paper, we propose CF-CGN (Channel Fingerprints with Cycle-consistent Generative Networks) to extrapolate CF for multi-band massive MIMO transmission where licensed and unlicensed spectra cooperate to provide ubiquitous connectivity.
GDM4MMIMO: Generative Diffusion Models for Massive MIMO Communications
Massive multiple-input multiple-output (MIMO) offers significant advantages in spectral and energy efficiencies, positioning it as a cornerstone technology of fifth-generation (5G) wireless communication systems and a promising solution for the burgeoning data demands anticipated in sixth-generation (6G) networks.
Distributed satellite information networks: Architecture, enabling technologies, and trends
In this context, the distributed satellite information networks (DSIN), exemplified by the cohesive clustered satellites system, have emerged as an innovative architecture, bridging information gaps across diverse satellite systems, such as communication, navigation, and remote sensing, and establishing a unified, open information network paradigm to support resilient space information services.
Score-Based Variational Inference for Inverse Problems
We show that RMP can be implemented by solving a variational inference problem, which can be further decomposed as minimizing a reverse KL divergence at each reverse step.
Ubiquitous Integrated Sensing and Communications for Massive MIMO LEO Satellite Systems
The next sixth generation (6G) networks are envisioned to integrate sensing and communications in a single system, thus greatly improving spectrum utilization and reducing hardware costs.
An I2I Inpainting Approach for Efficient Channel Knowledge Map Construction
Specifically, we first view the channel knowledge as a 2-D image and transform the CKM construction problem into an image-to-image (I2I) inpainting task, which predicts the channel knowledge at a specific location by recovering the corresponding pixel value in the image matrix.
Precoder Design for User-Centric Network Massive MIMO with Matrix Manifold Optimization
In this paper, we investigate the precoder design for user-centric network (UCN) massive multiple-input multiple-output (mMIMO) downlink with matrix manifold optimization.
On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds
A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc.
Channel Estimation for LEO Satellite Massive MIMO OFDM Communications
In addition, we devise a novel two-stage channel estimation (TSCE) approach, in which the received signals at the satellite are manipulated with per-subcarrier space domain processing followed by per-user frequency domain processing.
Pervasive wireless channel modeling theory and applications to 6G GBSMs for all frequency bands and all scenarios
In this paper, a pervasive wireless channel modeling theory is first proposed, which uses a unified channel modeling method and a unified equation of channel impulse response (CIR), and can integrate important channel characteristics at different frequency bands and scenarios.
A Novel 3D Non-Stationary GBSM for 6G THz Ultra-Massive MIMO Wireless Systems
Terahertz (THz) communication is now being considered as one of possible technologies for the sixth generation (6G) wireless communication systems.
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.
A General 3D Space-Time-Frequency Non-Stationary THz Channel Model for 6G Ultra-Massive MIMO Wireless Communication Systems
The proposed THz channel model is very general having the capability to capture different channel characteristics in multiple THz application scenarios such as indoor scenarios, device-to-device (D2D) communications, ultra-massive multiple-input multiple-output (MIMO) communications, and long traveling paths of users.
A General 3D Non-Stationary Wireless Channel Model for 5G and Beyond
In this paper, a novel three-dimensional (3D) non-stationary geometry-based stochastic model (GBSM) for the fifth generation (5G) and beyond 5G (B5G) systems is proposed.
Downlink Transmit Design for Massive MIMO LEO Satellite Communications
We show that, in this case, the design of precoding vectors can be simplified into that of scalar variables, for which an effective algorithm is developed.
6G Oriented Wireless Communication Channel Characteristics Analysis and Modeling
Based on the vision on the 6G wireless communication network, i. e., global coverage, all spectrums and all applications, we comprehensively survey 6G related wireless channel measurements, channel characteristics, and channel models for all frequency bands and all scenarios.
Multi-Frequency Multi-Scenario Millimeter Wave MIMO Channel Measurements and Modeling for B5G Wireless Communication Systems
Millimeter wave (mmWave) bands have been utilized for the fifth generation (5G) communication systems and will no doubt continue to be deployed for beyond 5G (B5G).
Network Massive MIMO Transmission Over Millimeter-Wave and Terahertz Bands: Mobility Enhancement and Blockage Mitigation
Based on these insights, the network massive MIMO precoding design is reduced to a network sum-rate maximization problem with respect to beam domain power allocation.
Spectral Efficiency and Energy Efficiency Tradeoff in Massive MIMO Downlink Transmission with Statistical CSIT
As a key technology for future wireless networks, massive multiple-input multiple-output (MIMO) can significantly improve the energy efficiency (EE) and spectral efficiency (SE), and the performance is highly dependant on the degree of the available channel state information (CSI).
Learning to Localize: A 3D CNN Approach to User Positioning in Massive MIMO-OFDM Systems
In this paper, we consider the user positioning problem in the massive multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system with a uniform planner antenna (UPA) array.
Compressive Sensing Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO
This paper considers massive access in massive multiple-input multiple-output (MIMO) systems and proposes an adaptive active user detection and channel estimation scheme based on compressive sensing.
