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itu_ml5g_phy_ncsu_mmwavenet

jcas_multibeam_optimization

Multibeam optimization for Joint Communication and Sensing (JCAS)

jcas_uav

JCAS

jointdesigninasymmetriccellfreenetworks

Submitted paperwork

jointdesigninmmwavecellfreenetworks

Submitted paperwork

jsac2020_prior-information-aided-deep-learning-method-for-grant-free-noma-in-mmtc

jscc

jstsp19

Wideband MIMO Channel Estimation for Hybrid Beamforming Millimeter Wave Systems via Random Spatial Sampling

knowledge-distillation-aided-end-to-end-learning-for-linear-precoding-in-multiuser-mimo-downlink-sys

latency-minimization-for-mmwave-d2d-mobile-edge-computing-systems

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.

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"

lidar_beam_tracking

lidar_unsupervised_hbf

LiDAR-Based Unsupervised Learning for Beamforming

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

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)

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.

mafenn_trans_2022

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

matlabcode

maximizing-energy-efficiency-of-massive-mimo-