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Deep Learning for Massive MIMO CSI Feedback
CsiNet is developed, a novel CSI sensing and recovery mechanism that learns to effectively use channel structure from training samples that can recover CSI with significantly improved reconstruction quality compared with existing compressive sensing (CS)-based methods.
Uplink Achievable Rate for Massive MIMO Systems With Low-Resolution ADC
In this letter, we derive an approximate analytical expression for the uplink achievable rate of a massive multiinput multioutput (MIMO) antenna system when finite precision analog-digital converters
Large Intelligent Surface-Assisted Wireless Communication Exploiting Statistical CSI
Numerical results show that using the proposed phase shift design can achieve the maximum ergodic spectral efficiency, and a 2-bit quantizer is sufficient to ensure spectral efficiency degradation of no more than 1 bit/s/Hz.
Bayes-Optimal Joint Channel-and-Data Estimation for Massive MIMO With Low-Precision ADCs
A Bayes-optimal JCD estimator is developed using a recent technique based on approximate message passing that allows the efficient evaluation of the performance of quantized massive MIMO systems and provides insights into effective system design.
A Model-Driven Deep Learning Network for MIMO Detection
Numerical results show that the proposed approach can improve the performance of the iterative algorithm significantly under Rayleigh and correlated MIMO channels.
Deep Learning-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems
The learned denoising-based approximate message passing (LDAMP) network is exploited and significantly outperforms state-of-the-art compressed sensing-based algorithms even when the receiver is equipped with a small number of RF chains.
On Capacity of Large-Scale MIMO Multiple Access Channels with Distributed Sets of Correlated Antennas
In this paper, a deterministic equivalent of ergodic sum rate and an algorithm for evaluating the capacity-achieving input covariance matrices for the uplink large-scale multiple-input
Deep Learning-Based CSI Feedback Approach for Time-Varying Massive MIMO Channels
A real-time CSI feedback architecture, called CsiNet-long short-term memory (LSTM), is developed by extending a novel deep learning (DL)-based CSI sensing and recovery network that outperforms existing compressive sensing-based and DL-based methods and is remarkably robust to CR reduction.
Decentralized Plug-in Electric Vehicle Charging Selection Algorithm in Power Systems
This paper uses a charging selection concept for plug-in electric vehicles (PEVs) to maximize user convenience levels while meeting predefined circuit-level demand limits, and develops a distributed optimization algorithm to solve the PEV-charging selection problem in a decentralized manner.
Channel Estimation for Massive MIMO Using Gaussian-Mixture Bayesian Learning
This paper proposes estimation of only the channel parameters of the desired links in a target cell, but those of the interference links from adjacent cells, which achieves much better performance in terms of the channel estimation accuracy and achievable rates in the presence of pilot contamination.