Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency
==================
This repository contains the authors' free public PDF of the textbook:
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 book, please visit: [https://www.massivemimobook.com](https://www.massivemimobook.com)
**Simulation code:** The repository also contains the code package that is distributed along with the textbook. 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 Book
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 book. 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](http://quadriga-channel-model.de/wp-content/uploads/2016/09/QuaDriGa_2016.09.05_v1.4.8-571.zip).
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
The authors' version of the textbook, which is found in this repository, is delivered for free personal use. It may not be redistributed without permission and may not be sold. The copyright is owned by the authors, E. Björnson, J. Hoydis and L. Sanguinetti. You can buy color-printed hardback books from [https://www.nowpublishers.com/article/Details/SIG-093](https://www.nowpublishers.com/article/Details/SIG-093)
The simulation code is licensed under the GPLv2 license. If you in any way use this code for research that results in publications, please cite our textbook 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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massivemimo源代码
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conf:40个
mat:7个
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massivemimo源代码 (264个子文件)
WINNER_UMa2Indoor_C4_NLOS.conf 6KB
WINNER_UMi2Indoor_B4_NLOS.conf 6KB
MIMOSA_10-45_NLOS.conf 6KB
MIMOSA_35-45_NLOS.conf 6KB
MIMOSA_16-25_NLOS.conf 5KB
MIMOSA_25-35_NLOS.conf 5KB
3GPP_3D_UMa_NLOS.conf 5KB
MIMOSA_10-45_LOS.conf 5KB
WINNER_UMa2Indoor_C4_LOS.conf 5KB
MIMOSA_35-45_LOS.conf 5KB
3GPP_3D_UMa_NLOS_O2I.conf 5KB
WINNER_UMi_B1_NLOS.conf 5KB
WINNER_UMi2Indoor_B4_LOS.conf 5KB
MIMOSA_25-35_LOS.conf 5KB
MIMOSA_16-25_LOS.conf 5KB
WINNER_UMa_C2_LOS.conf 5KB
WINNER_UMa_C2_NLOS.conf 5KB
WINNER_SMa_C1_LOS.conf 5KB
3GPP_3D_UMi_NLOS.conf 5KB
WINNER_SMa_C1_NLOS.conf 5KB
WINNER_UMi_B1_LOS.conf 5KB
WINNER_Indoor_A1_NLOS.conf 5KB
3GPP_3D_UMi_NLOS_O2I.conf 5KB
mmMAGIC_initial_UMi_10-80_LOS_O2I.conf 5KB
mmMAGIC_initial_UMi_10-80_NLOS_O2I.conf 5KB
mmMAGIC_initial_UMi_10-80_LOS.conf 5KB
mmMAGIC_initial_UMi_10-80_NLOS.conf 5KB
mmMAGIC_initial_Indoor_10-80_LOS.conf 5KB
mmMAGIC_initial_Indoor_10-80_NLOS.conf 5KB
3GPP_3D_UMi_LOS_O2I.conf 5KB
3GPP_3D_UMa_LOS_O2I.conf 5KB
WINNER_Indoor_A1_LOS.conf 5KB
3GPP_3D_UMi_LOS.conf 5KB
3GPP_3D_UMa_LOS.conf 4KB
BERLIN_UMa_LOS.conf 4KB
BERLIN_UMa_NLOS.conf 4KB
DRESDEN_UMa_NLOS.conf 3KB
DRESDEN_UMa_LOS.conf 3KB
Freespace.conf 302B
LOSonly.conf 230B
version_history.html 27KB
get_channels.m 34KB
parameter_set.m 27KB
merge.m 27KB
array.m 24KB
generate.m 22KB
track.m 21KB
functionComputeSE_DL_estimation.m 18KB
generate_parameters.m 17KB
get_pl.m 16KB
layout.m 15KB
channel.m 14KB
simulation_parameters.m 13KB
section7_figure41_42.m 13KB
satellite_channel.m 12KB
get_channels_seg.m 12KB
update_parameters.m 12KB
functionComputeULPowerLevels_impairments.m 11KB
functionExampleSetup_Quadriga.m 11KB
set_scenario_table.m 10KB
create_parameter_sets.m 10KB
section4_figure12_13.m 10KB
power_map.m 10KB
generate_initial_angles.m 9KB
geometric_polarization.m 9KB
tutorial_ssf_interface.m 9KB
interpolate.m 9KB
get_los_channels.m 9KB
section1_figure16.m 9KB
lsf_ts_tutorial.m 9KB
functionChannelEstimates.m 9KB
functionComputeSE_DL_hardening.m 9KB
interpolate.m 9KB
set_pairing.m 8KB
visualize_clusters.m 8KB
time_evolution.m 8KB
generate_correlation_maps.m 8KB
generate.m 8KB
change_pol_basis.m 8KB
measurement_comparison.m 7KB
section7_figure31_32.m 7KB
functionComputeSE_UL_impairments.m 7KB
functionComputeULPowerLevels.m 7KB
solve_multi_bounce_opti.m 7KB
update_drifting.m 7KB
set_scenario.m 7KB
functionExampleSetup.m 7KB
functionChannelEstimates_impairments.m 7KB
generate.m 7KB
functionComputeSE_UL.m 7KB
channel_builder.m 7KB
rotate_pattern.m 7KB
functionComputeSE_DL_impairments.m 6KB
set_scenario.m 6KB
speed_profile_interpolation.m 6KB
section4_figure20.m 6KB
functionComputeSE_DL_MR_impairments.m 6KB
section7_figure5.m 6KB
section7_figure18.m 6KB
get_channels.m 6KB
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