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ld-gamp

Implement of LD-GAMP

ldamp_based-channel-estimation

This code is for the following paper: H. He, C. Wen, S. Jin, and G. Y. Li, “Deep learning-based channel estimation for beamspace mmwave massive MIMO systems,” IEEE Wireless Commun. Lett., vol. 7, no. 5, pp. 852–855, Oct. 2018.

ldpc-and-mimo-in-vehicular-communication

This project demonstrates how Low Density Parity Check (LDPC) Code and Multiple Input Multiple Output (MIMO) can be employed in Vehicular Communication.

learning-based-power-control-for-mmwave-massive-mimo-against-jamming

论文中包含的论文原文以及原文所涉及的简化版代码。

learning-mmse-est

Julia code for the paper "Learning the MMSE Channel Estimator"

learning-to-learn-beamforming

lidar_beam_tracking

lidar_unsupervised_hbf

LiDAR-Based Unsupervised Learning for Beamforming

light-sernet

Light-SERNet: A lightweight fully convolutional neural network for speech emotion recognition

lightweight-gan

Implementation of 'lightweight' GAN, proposed in ICLR 2021, in Pytorch. High resolution image generations that can be trained within a day or two

lightweight-neural-architecture-search

This is a collection of our zero-cost NAS and efficient vision applications.

limited-feedback-channel-estimation-in-massive-mimo-systems

Today, plenty of cellular systems utilize frequency-division duplexing (FDD). Downlink training for channel state information in FDD is difficult since training and feedback overhead is proportional to the number of antennas at the base station, which is large in a Massive MIMO systems. To deal with the limited feedback mechanism of downlink channel in FDD Massive MIMO system, we can adopt the double directional model. This is applicable for the 5G systems to get high capacity and data rate. We analyse and test the performance of the Limited feedback channel with DD model via the MATLAB and we had the better performance rather than other models.

lis-deeplearning

Simulation code for "Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning" by Abdelrahman Taha, Muhammad Alrabeiah, and Ahmed Alkhateeb, published in IEEE Access, March 2021.

lista-ce

Adaptive Channel Estimation Based on Model-Driven Deep Learning for Wideband mmWave Systems, GLOBECOM 2021 (Tensorflow Code)

lonestar

MATLAB scripts for LoneSTAR: analog beamforming codebooks for full-duplex mmWave communication systems.

low-complexity-hybrid-beamforming-for-mmwave-full-duplex-integrated-access-and-backhaul

Simulation code for the paper "Low Complexity Hybrid Beamforming for mmWave Full-Duplex Integrated Access and Backhaul" in IEEE Globecom 2022

low-complexity-matrix-inversion-algorithm-for-xl-mimo

low-complexity-sum-capacity-maximization-for-intelligent-reflecting-surface-aided-mimo-systems

This is code for the paper accepted in WCL (Wireless Communications Letters) in 2022, written in MATLAB.

low-cplx-xl-mimo-detection

Codes for reproducing the numerical results reported in: "Low-Complexity Distributed XL-MIMO for Multiuser Detection" by Victor Croisfelt, Abolfazl Amiri, Taufik Abrão, Elisabeth de Carvalho, and Petar Popovski.

lpan

Channel Estimation for RIS-aided Systems

lrpca

Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection

m-mimo-channel-reconstruction

mafenn_trans_2022

malaga-distribution

Simulation code for Malaga distribution implemented in the paper "Aggregate Hardware Impairments Over Mixed RF/FSO Relaying Systems With Outdated CSI," in IEEE Transactions on Communications, vol. 66, no. 3, pp. 1110-1123, March 2018

marllib

A universal MARL benchmark for research and industry

massive-mimo-deep-decoder

Implementation of "Massive MIMO Channel Estimation with an untrained deep neural network"

massive-mimo-precoding

Linear and Nonlinear precoding in downlink Multi-user Massive MIMO systems

massive_mimo

Massiv MIMO OFDM + wavelet

massivemimobook

Book PDF and simulation code for the monograph "Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency" by Emil Björnson, Jakob Hoydis and Luca Sanguinetti, published in Foundations and Trends in Signal Processing, 2017.

matlab-gan

MATLAB implementations of Generative Adversarial Networks -- from GAN to Pixel2Pixel, CycleGAN