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6g-simulation-platform

This simulation platform is presented in An Open Platform for Simulating the Physical Layer of 6G Communication Systems with Multiple Intelligent Surfaces.In this paper, we develop an open simulation platform, aimed at modeling the physical-layer electromagnetic coupling and propagation between RIS pairs. We present the platform by initially design

book-resource-allocation

Simulation code for the book “Optimal Resource Allocation in Coordinated Multi-Cell Systems” by Emil Björnson and Eduard Jorswieck, Foundations and Trends in Communications and Information Theory, vol. 9, no. 2-3, pp. 113-381, 2013

deep-learning-channel-estimation

Simulation code for “Channel Estimation in Massive MIMO under Hardware Non-Linearities: Bayesian Methods versus Deep Learning,” by Özlem Tugfe Demir, Emil Björnson, IEEE Open Journal of the Communications Society, To appear.

handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

irs-relaying

Simulation code for “Intelligent Reflecting Surface vs. Decode-and-Forward: How Large Surfaces Are Needed to Beat Relaying?,” by Emil Björnson, Özgecan Özdogan, Erik G. Larsson, IEEE Wireless Communications Letters, vol. 9, no. 2, pp. 244-248, February 2020.

irs_enhanced-wireless-network_joint-active-and-passive-beamformingdesign_qingqing-wu-and-rui-zhang

paper simulation "Intelligent Reflecting Surface Enhanced Wireless Network_Joint Active and Passive BeamformingDesign" Qingqing Wu and Rui Zhang

machine_learning_refined

Notes, examples, and Python demos for the textbook "Machine Learning Refined" (published by Cambridge University Press).

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_simo

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.

mimo_singleris

multi-ris

Tri Nhu Do, Georges Kaddoum, Thanh Luan Nguyen, Daniel Benevides da Costa, and Zygmunt J. Haas, “Multi-RIS-aided Wireless Systems: Statistical Characterization and Performance Analysis,” IEEE Transactions on Communications, Sep. 2021.

optimal-beamforming

Simulation code for “Optimal Multiuser Transmit Beamforming: A Difficult Problem with a Simple Solution Structure” by Emil Björnson, Mats Bengtsson, and Björn Ottersten, IEEE Signal Processing Magazine, vol. 31, no. 4, pp. 142-148, July 2014.

ratemaxmimoris

Simulation code for "Achievable Rate Optimization for MIMO Systems with Reconfigurable Intelligent Surfaces" by N. S. Perović, L.-N. Tran, M. Di Renzo, and M. F. Flanagan, IEEE Trans. Wireless Commun., vol. 20, no. 6, pp. 3865-3882, Jun. 2021.

scikit-learn

scikit-learn: machine learning in Python

sp_cup_2021

This repository contains the description of the IEEE Signal Processing Cup 2021. Everyone is welcome to join this cup!

sum-rate-maximization-for-linearly-precoded-downlink-multiuser-miso-systems-with-partial-csit-a-rate

paper simultaion "Sum-Rate Maximization for Linearly Precoded Downlink Multiuser MISO Systems With Partial CSIT A Rate-Splitting Approach code"