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deep-learning-power-allocation-in-massive-mimo

This is the code package related to the follow scientific article: Luca Sanguinetti, Alessio Zappone, Merouane Debbah 'Deep-Learning-Power-Allocation-in-Massive-MIMO' presented at the Asilomar Conference on Signals, Systems, and Computers, 2018. http://www.asilomarsscconf.org

emi-ris-communications

energy_consumption_in_mu_mimo_with_mobility

This code computes the energy consumption in the downlink of a single-cell multi-user MIMO system in which the base station (BS) makes use of N antennas to communicate with K single-antenna user equipments (UEs). The UEs move around in the cell according to a random walk mobility model.

holographic-mimo-small-scale-fading

ln-game-theory

Matlab code for the figures and the examples used in G. Bacci, L. Sanguinetti, and M. Luise, "Understanding game theory via wireless power control,' submitted to IEEE Signal Process. Mag., Oct. 2014.

massive-mimo-rician-channels

This code computes the spectral efficiency in the downlink of a Massive MIMO systems over Uncorrelated Rician Fading Channels. In particular, it generates Figs. 4 and 5 of a manuscript that is currently under review for publication on IEEE Transactions on Communications (submitted May 2018). The manuscript will be made available soon on arxiv.

max-ee-multislope-path-loss

This is a code package is related to the follow scientific article: Andrea Pizzo, Daniel Verenzuela, Luca Sanguinetti and Emil Björnson, "Network Deployment for Maximal Energy Efficiency in Uplink with Multislope Path Loss," IEEE Transactions on Green Communications and Networking, Submitted to. The package contains a simulation environment, based on Matlab, that reproduces all the numerical results and figures in the article. We encourage you to also perform reproducible research!

solving-energy-efficiency-problems-through-polynomial-optimization-theory

This is a code package is related to the follow scientific article: Andrea Pizzo, Alessio Zappone and Luca Sanguinetti, "Solving Energy Efficiency Problems through Polynomial Optimization Theory," IEEE Signal Processing Letters, Submitted to. The package contains a simulation environment, based on Matlab, that reproduces all the numerical results and figures in the article. We encourage you to also perform reproducible research!