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Found 14 papers, 3 papers with code
Joint Channel Estimation and Cooperative Localization for Near-Field Ultra-Massive MIMO
To address the joint channel estimation and cooperative localization problem for near-field UM-MIMO systems, we propose a variational Newtonized near-field channel estimation (VNNCE) algorithm and a Gaussian fusion cooperative localization (GFCL) algorithm.
Bayes-Optimal Unsupervised Learning for Channel Estimation in Near-Field Holographic MIMO
In this paper, we address the fundamental challenge of designing a low-complexity Bayes-optimal channel estimator in near-field HMIMO systems operating in unknown EM environments.
Green Edge AI: A Contemporary Survey
Guided by these principles, we then explore energy-efficient design methodologies for the three critical tasks in edge AI systems, including training data acquisition, edge training, and edge inference.
Learning Bayes-Optimal Channel Estimation for Holographic MIMO in Unknown EM Environments
Holographic MIMO (HMIMO) has recently been recognized as a promising enabler for future 6G systems through the use of an ultra-massive number of antennas in a compact space to exploit the propagation characteristics of the electromagnetic (EM) channel.
Message Passing Meets Graph Neural Networks: A New Paradigm for Massive MIMO Systems
As one of the core technologies for 5G systems, massive multiple-input multiple-output (MIMO) introduces dramatic capacity improvements along with very high beamforming and spatial multiplexing gains.
An Adaptive and Robust Deep Learning Framework for THz Ultra-Massive MIMO Channel Estimation
For practical usage, the proposed framework is further extended to wideband THz UM-MIMO systems with beam squint effect.
Lightweight and Flexible Deep Equilibrium Learning for CSI Feedback in FDD Massive MIMO
In this paper, we propose a lightweight and flexible deep learning-based CSI feedback approach by capitalizing on deep equilibrium models.
Blind Performance Prediction for Deep Learning Based Ultra-Massive MIMO Channel Estimation
Reliability is of paramount importance for the physical layer of wireless systems due to its decisive impact on end-to-end performance.
Augmented Deep Unfolding for Downlink Beamforming in Multi-cell Massive MIMO With Limited Feedback
In limited feedback multi-user multiple-input multiple-output (MU-MIMO) cellular networks, users send quantized information about the channel conditions to the associated base station (BS) for downlink beamforming.
Intelligent Reflecting Surface-Aided Maneuvering Target Sensing: True Velocity Estimation
Maneuvering target sensing will be an important service of future vehicular networks, where precise velocity estimation is one of the core tasks.
Hybrid Far- and Near-Field Channel Estimation for THz Ultra-Massive MIMO via Fixed Point Networks
We draw inspirations from fixed point theory to develop an efficient deep learning based channel estimator with adaptive complexity and linear convergence guarantee.
Learn to Communicate with Neural Calibration: Scalability and Generalization
Furthermore, such networks will vary dynamically in a significant way, which makes it intractable to develop comprehensive analytical models.
Neural Calibration for Scalable Beamforming in FDD Massive MIMO with Implicit Channel Estimation
Channel estimation and beamforming play critical roles in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems.
Communication-Efficient Federated Learning with Dual-Side Low-Rank Compression
Federated learning (FL) is a promising and powerful approach for training deep learning models without sharing the raw data of clients.
