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lstmchannelstateestimation

Using LSTM Model(s) to estimate the state of a deep fading channel

machine-learning-course-andrew-ng

Solutions to Assignments for Machine Learning Course by Andrew Ng.

machine-learning-course-code

codes for Andrew Ng. machine learning course

machine-learning-octave

🤖 MatLab/Octave examples of popular machine learning algorithms with code examples and mathematics being explained

machinelearninginmatlab

Machine Learning algorithms implemented in MATLAB

mamimo-csi-positioning-using-cnns

This repository contains the source code used for the results in "CSI-based Positioning in Massive MIMO systems using Convolutional Neural Networks".

mamimo_outdoor_expermient-

MaMIMO outdoor experiment

massive-mimo-

massive-mimo-book-chapter

Simulation code for the book chapter “Massive MIMO Communications” by Trinh van Chien and Emil Björnson, 5G Mobile Communications, Springer, 2017

massive-mimo-drone-swarms

Codes for the paper: P. Chandhar, D. Danev and E. G. Larsson, "Massive MIMO for Communications With Drone Swarms," in IEEE Transactions on Wireless Communications, vol. 17, no. 3, pp. 1604-1629, March 2018.

massive-mimo-hardware-impairments

Simulation code for “Massive MIMO Systems with Non-Ideal Hardware: Energy Efficiency, Estimation, and Capacity Limits” by Emil Björnson, Jakob Hoydis, Marios Kountouris, Mérouane Debbah, IEEE Transactions on Information Theory, vol. 60, no. 11, pp. 7112-7139, November 2014.

massive-mimo-hybrid-beamforming-matlab-example

A simple example with how hybrid beamforming is employed at the transmit end of a massive MIMO communications system.

massive-mimo-los-outage-capacity

Codes for the paper: P. Chandhar, D. Danev and E. G. Larsson, "On the outage capacity in massive MIMO with line-of-sight," 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Sapporo, 2017, pp. 1-6.

massive-mimo-myths

Simulation code for “Massive MIMO: Ten Myths and One Critical Question” by Emil Björnson, Erik G. Larsson, Thomas L. Marzetta, IEEE Communications Magazine, vol. 54, no. 2, pp. 114-123, February 2016

massive-mimo1

1

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.

mathematical-optimization

数学系本科三年级《最优化理论与方法》

matlab-for-machine-learning

MATLAB for Machine Learning, published by Packt

matlab-gan

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

matlab-library

Matlab functions for wireless communications focussing mostly on LTE / 3GPP.

matlab-machine-learning

Source code for 'MATLAB Machine Learning' by Michael Paluszek and Stephanie Thomas

matlab-octave

This repository contains algorithms written in MATLAB/Octave. Developing algorithms in the MATLAB environment empowers you to explore and refine ideas, and enables you test and verify your algorithm.

matlab_simo

matlabfiguremaker

How I make my figures for Matlab LaTeX

matrix-calibration-based-cascaded-channel-estimation

Simulation Codes for Matrix-Calibration-Based Cascaded Channel Estimation for Reconfigurable Intelligent Surface Assisted Multiuser MIMO

maximal-se

Simulation code for “Massive MIMO for Maximal Spectral Efficiency: How Many Users and Pilots Should Be Allocated?” by Emil Björnson, Erik G. Larsson, Mérouane Debbah, IEEE Transactions on Wireless Communications, vol. 15, no. 2, pp. 1293-1308, 2016.

maximizing-minimum-phase-difference-based-hybrid-beamforming-for-multiuser-mmwave-massive-mimo-syste

This one is about the paper and the code of "Maximizing minimum phase difference based hybrid beamforming for multiuser mmWave massive MIMO systems".

meta-autoencoder

Code for the paper "Meta-Learning to Communicate: Fast End-to-End Training for Fading Channels"

mf-ris

mimo-channel-estimation

Simulation code for “A Framework for Training-Based Estimation in Arbitrarily Correlated Rician MIMO Channels with Rician Disturbance” by Emil Björnson and Björn Ottersten, IEEE Transactions on Signal Processing, vol. 58, no. 3, pp. 1807-1820, March 2010.