文件列表:

checkHexagonal.m (1431, 2016-10-25)
computeEnvironment.m (23391, 2016-10-25)
functionHeuristicPowerAllocation.m (4298, 2016-10-25)
functionMRT.m (1456, 2016-10-25)
functionZFBF.m (1593, 2016-10-25)
function_capacity_broadcast.m (1463, 2016-10-25)
simulationFigure2.m (4637, 2016-10-25)
simulationFigure3.m (5417, 2016-10-25)
simulationFigure8.m (13345, 2016-10-25)
simulationFigure9.m (8980, 2016-10-25)

Massive MIMO Communications ================== This is a code package is related to the following book chapter: Trinh Van Chien, Emil Bjornson, “Massive MIMO Communications,” in 5G Mobile Communications, W. Xiang et al. (eds.), pp. 77-116, Springer, 2017. 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!* ##Abstract of Chapter Every new network generation needs to make a leap in area data throughput, to manage the growing wireless data traffic. The Massive MIMO technology can bring at least ten-fold improvements in area throughput by increasing the spectral efficiency (bit/s/Hz/cell), while using the same bandwidth and density of base stations as in current networks. These extraordinary gains are achieved by equipping the base stations with arrays of a hundred antennas to enable spatial multiplexing of tens of user terminals. This chapter explains the basic motivations and communication theory behind the Massive MIMO technology, and provides implementation-related design guidelines. ##Content of Code Package The book chapter contains 4 simulation figures: Figure 2, Figure 3, Figure 8, and Figure 9. These are generated by the Matlab scripts simulationFigure2.m, simulationFigure3.m, simulationFigure8.m, and simulationFigure9.m, respectively. The package contains six additional Matlab functions which are called by the main scripts: checkHexagonal.m, computeEnvironment.m, function_capacity_broadcast.m, functionHeuristicPowerAllocation.m, functionMRT.m, and functionZFBF.m. These are all borrowed from other code packages. See each file for further documentation. The convex optimization problem solved in Figure 2, to achieve the capacity, is implemented using the modeling language [CVX](http://cvxr.com/cvx/). ##Acknowledgements This work was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No ***1***5 (5Gwireless). It was also supported by ELLIIT and CENIIT. ##License and Referencing This code package is licensed under the GPLv2 license. If you in any way use this code for research that results in publications, please cite our original book chapter listed above.

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