Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency
==================
This code package is distributed along with the monograph:
Emil Bjornson, Jakob Hoydis and Luca Sanguinetti (2017), "[Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency](https://www.massivemimobook.com)", Foundations and Trends in Signal Processing: Vol. 11, No. 3-4, pp. 154-655. DOI: 10.1561/2000000093.
For further information about the monograph, please visit: [https://www.massivemimobook.com](https://www.massivemimobook.com)
The package contains a simulation environment, based on Matlab, that can be used to reproduce all the simulation results in the monograph. We hope that the code will support you in the learning of the Massive MIMO topic and also serve as a baseline for further research endeavors. *We encourage you to also perform reproducible research!*
## Abstract of the Monograph
Massive multiple-input multiple-output (MIMO) is one of the most promising technologies for the next generation of wireless communication networks because it has the potential to provide game-changing improvements in spectral efficiency (SE) and energy efficiency (EE). This monograph summarizes many years of research insights in a clear and self-contained way and provides the reader with the necessary knowledge and mathematical tools to carry out independent research in this area. Starting from a rigorous definition of Massive MIMO, the monograph covers the important aspects of channel estimation, SE, EE, hardware efficiency (HE), and various practical deployment considerations.
From the beginning, a very general, yet tractable, canonical system model with spatial channel correlation is introduced. This model is used to realistically assess the SE and EE, and is later extended to also include the impact of hardware impairments. Owing to this rigorous modeling approach, a lot of classic “wisdom” about Massive MIMO, based on too simplistic system models, is shown to be questionable.
The monograph contains many numerical examples, which can be reproduced using Matlab code that is available online.
## Content of the Code Package
This code package contains 74 Matlab scripts, 29 Matlab functions, and 7 binary files with Matlab data.
Each script is used to reproduce a particular simulation-generated figure in the monograph. The scripts are named using the convention sectionX_figureY, which is interpreted as the script that reproduces Figure X.Y. A few scripts are instead named as sectionX_figureY_Z and will then generate both Figure X.Y and Figure X.Z.
The functions are used by the scripts to carry out certain tasks, such as initiating a simulation setup, generating channel correlation matrices, generating channel realizations, computing channel estimates, computing SEs, computing the power consumption, etc.
The Matlab data files are of the type .mat and contain measurement results or particular precomputed simulation results.
See each script and function for further documentation. Note that some of the functions use [CVX](http://cvxr.com/cvx/) and [QuaDRiGa](http://quadriga-channel-model.de), which need to be installed separately; see below.
## Software and Hardware Requirements
The code was written to be used in Matlab and has been tested in Matlab 2015b. Some of the scripts and functions might also work in Octave, but there is no guarantee of compatibility.
A few scripts and functions require additional software packages that have been developed independently and are delivered with separate licenses. To generate Figures 7.2, 7.41, and 7.42, you need to solve convex optimization problems using CVX from CVX Research, Inc. ([http://cvxr.com/cvx/](http://cvxr.com/cvx/)). The code has been tested with CVX 2.1 (Build 1112) using the solver Mosek (version 7.1.0.12). We discourage the use of the solvers SDPT3 and SeDuMi since these crashed during the test. To generate Figures 7.41 and 7.42, you also need to generate channels using the QuaDRiGa channel model from the Fraunhofer Heinrich Hertz Institute ([http://www.quadriga-channel-model.de](http://www.quadriga-channel-model.de)). The code has been tested with QuaDRiGa version 1.4.8-571.
Since the running example in this monograph considers a setup with 16 cells, 100 antennas per BS, and 10 UEs per cell, some of the simulations require a lot of RAM to store the channel correlation matrices and channel realizations. The code has been tested successfully on a MacBook Pro with 8 GB 1600 MHz DDR3 RAM and a 2.6 GHz Intel Core i5 processor, which should be viewed as a minimum requirement for using this code. Some of the simulations can take days to run, therefore we recommend that you first set nbrOfSetups = 1 to check how much time it takes for each realization of random UE location and shadow fading.
## Acknowledgements
We would like to thank the editor Robert W. Heath Jr. for organizing the review of this monograph and the anonymous reviewers for their constructive and detailed comments. We are grateful for the feedback provided by our proof-readers Alessio Zappone (University of Cassino and Southern Lazio), Maximilian Arnold (University of Stuttgart), Andrea Pizzo (University of Pisa), Daniel Verenzuela, Hei Victor Cheng, Giovanni Interdonato, Marcus Karlsson, Antzela Kosta, Özgecan Özdogan (Linköping university), and Zahid Aslam (Siradel).
Emil Björnson has been supported by ELLIIT, CENIIT, and the Swedish Foundation for Strategic Research.
Luca Sanguinetti has been supported by the ERC Starting Grant 305123 MORE.
## License and Referencing
This code is licensed under the GPLv2 license. If you in any way use this code for research that results in publications, please cite our monograph as described above. We also recommend that you mention the existence of this code package in your manuscript, to spread the word about its existence and to ensure that you will not be accused of plagiarism by the reviewers of your manuscript.
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massivemimobook-master.rar_massive MIMO_massive MIMO MATLAB_matl (112个子文件)
functionComputeSE_DL_estimation.m 18KB
section7_figure41_42.m 13KB
functionComputeULPowerLevels_impairments.m 11KB
functionExampleSetup_Quadriga.m 10KB
section4_figure12_13.m 10KB
section1_figure16.m 9KB
functionChannelEstimates.m 9KB
functionComputeSE_DL_hardening.m 9KB
section7_figure31_32.m 7KB
functionComputeSE_UL_impairments.m 7KB
functionComputeULPowerLevels.m 7KB
functionExampleSetup.m 7KB
functionChannelEstimates_impairments.m 7KB
functionComputeSE_UL.m 7KB
functionComputeSE_DL_impairments.m 6KB
section4_figure20.m 6KB
functionComputeSE_DL_MR_impairments.m 6KB
section7_figure5.m 6KB
section7_figure18.m 6KB
functionComputeSE_UL_MR_MC.m 6KB
section7_figure2.m 6KB
section1_figure17.m 6KB
section4_figure17.m 6KB
section7_figure9.m 6KB
section4_figure21_22.m 5KB
functionCPcomputation.m 5KB
functionComputeSINR_DL.m 5KB
section4_figure26.m 5KB
section7_figure7.m 5KB
section7_figure3.m 5KB
section4_figure18_19.m 5KB
functionComputeSE_UL_MR_impairments.m 5KB
section1_figure18.m 5KB
section4_figure25.m 5KB
section4_figure7.m 5KB
section3_figure6.m 5KB
section6_figure7.m 5KB
section7_figure38_39.m 4KB
functionComputeULDLPowerLevels.m 4KB
functionExampleSetup_backhaul.m 4KB
functionPowerOptimization_maxmin.m 4KB
section5_figure14.m 4KB
section5_figure15.m 4KB
section6_figure4.m 4KB
section4_figure14.m 4KB
section6_figure10.m 4KB
section1_figure14.m 4KB
section5_figure11.m 4KB
section5_figure10.m 4KB
section4_figure8.m 4KB
section4_figure5_6.m 4KB
section4_figure9.m 4KB
section4_figure11.m 4KB
section6_figure5.m 4KB
functionComputeGainVariations.m 4KB
section3_figure7.m 4KB
functionPowerOptimization_prodSINR.m 4KB
section6_figure3.m 4KB
section6_figure9.m 4KB
section4_figure3.m 4KB
section5_figure3.m 4KB
section6_figure2.m 4KB
functionChannelEstimates_LS.m 3KB
section7_figure16.m 3KB
section6_figure8.m 3KB
functionRlocalscattering.m 3KB
section5_figure7.m 3KB
section3_figure5.m 3KB
section3_figure4.m 3KB
section5_figure12.m 3KB
section5_figure13.m 3KB
section7_figure26.m 3KB
section4_figure10.m 3KB
section7_figure20.m 3KB
functionRlocalscattering3D.m 3KB
functionMinAntennasPower.m 3KB
section1_figure9.m 3KB
section5_figure4.m 3KB
section5_figure9a.m 3KB
section5_figure5.m 3KB
section5_figure9b.m 3KB
section5_figure8.m 2KB
section3_figure2.m 2KB
section2_figure8.m 2KB
section5_figure6.m 2KB
section7_figure27.m 2KB
section1_figure13.m 2KB
section1_figure12.m 2KB
section2_figure6.m 2KB
section3_figure3.m 2KB
functionRlocalscatteringApprox.m 2KB
section2_figure7.m 2KB
section7_figure15.m 2KB
section7_figure13.m 2KB
functionCPmodel.m 2KB
section7_figure8.m 2KB
functionComputeSE_DL_poweralloc.m 2KB
section7_figure23.m 2KB
section3_figure8.m 1KB
section7_figure24.m 1KB
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