## WSR maximization for RIS system
This repository contains the source codes of the Fig.4(b) in the paper ``Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks'' published in IEEE Transactions on Wireless Communications.
In the paper, BSUM algorithm is adopted which achieves a KKT solution for the MU-WSR problem. In addition, imperfect CSI issue is addressed by stochastic optimization.
## Introduction of the codes
### Plot fig.4(b)
Run the file ``converge_plot.m''. You may get the following figure
<img src="./fig4.jpg" height="360" width="450" >
### Generate random channel
To generate one snapshot for simulation, one may run following three files in sequence:
+ ``generate_location.m'': Random the users' locations
+ ``generate_pathloss.m'': Calucate the pathloss according to the locations
+ ``generate_channel.m''" Randomly generate the channel realizations
### Main codes for the algorithms
The following are the main code files for the 5 algorihtms shown in the figure.
+ ``without_RIS.m'': There's no RIS in the system.
+ ``RIS_phaserand.m'': The RIS adopts random phase.
+ ``converge_AO_perfect.m'': Alternating optimization approach illustrated in Section III. Note that, to support the Riemannian conjugate gradient (RCG) algorithm, one should download the Manopt toolbox from <https://www.manopt.org/> at first.
+ ``converge_A2_perfect.m'': Proposed algorithm under the perfect CSI setup.
+ ``converge_A2_imperfect.m'': Proposed algorithm under the imperfect CSI setup.
## Some of My Related Works
### IRS channel estimation
+ H. Guo and V. K. N. Lau, "Uplink Cascaded Channel Estimation for Intelligent Reflecting Surface Assisted Multiuser MISO Systems," in IEEE Transactions on Signal Processing, vol. 70, pp. 3964-3977, 2022. (see <https://ieeexplore.ieee.org/document/9839429>).
We propose a novel two-stage channel estimation protocol without the need of on-off amplitude control to avoid the reflection power loss. One may refer the source code from <https://github.com/guohuayan/IRS_Channel_Estimation>
### IRS phase shift optimization with statistical CSI
+ H. Guo, Y. -C. Liang and S. Xiao, "Intelligent Reflecting Surface Configuration With Historical Channel Observations," in IEEE Wireless Communications Letters, vol. 9, no. 11, pp. 1821-1824, Nov. 2020. (see <https://ieeexplore.ieee.org/document/9120336>).
We propose a general IRS statistical CSI configuration algorithm, which can be adopted to any statistical CSI model even for the case when there is no LoS link between BS and IRS. One may refer the source code from <https://github.com/guohuayan/IRS_opt_statistical_CSI>
## Note
You may cite us by
@ARTICLE{8982186,
author={H. Guo and Y.-C. Liang and J. Chen and E. G. Larsson},
journal={IEEE Trans. Wireless Commun.},
title={Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks},
volume = {19},
number = {5},
pages = {3064-3076},
ISSN = {1558-2248},
DOI = {10.1109/TWC.2020.2970061},
year = {2020},
type = {Journal Article}
}
One previous version of this paper is named ``Weighted Sum-Rate Optimization for Intelligent Reflecting Surface Enhanced Wireless Networks'', which can be found in ArXiv as well (see <https://arxiv.org/abs/1905.07920>). The short version has been presented in IEEE GLOBECOM 2019.
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WSR_maximization_for_RIS_system-master.zip
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WSR_maximization_for_RIS_system-master.zip (50个子文件)
WSR_maximization_for_RIS_system-master
fig4
channel_G.m 135B
converge_stochastic_phi_rho_05_v3.mat 1KB
update_lambda_v2.m 311B
power_W.m 101B
Proxlinear_beam_para.m 188B
generate_location.m 530B
user_location.mat 428B
converge_stochastic_phi_rho_01_v3.mat 1KB
mystopfun.m 129B
init_W.m 496B
converge_speed_step_phi_CG_n5.mat 1KB
SCA_phi_step_para.m 470B
without_RIS.m 1KB
upadte_beta.m 244B
converge_plot.m 1KB
down_without_IRS.mat 428B
Proxlinear_beam_v2.m 371B
par_fun_B.m 600B
Proxlinear_w.m 406B
RIS_phaserand.m 1KB
sum_rate_direct_egrad.m 543B
generate_pathloss.m 561B
man_opt_theta_direct.m 828B
user_channel.mat 1.34MB
channel_Hr.m 246B
update_SINR.m 242B
fun_theta_obj.m 406B
path_LOS.m 149B
generate_channel.m 1KB
converge_A2_manopt_WMMSE_n5.mat 1KB
path_NLOS.m 234B
downlink_beam_WMMSE_v2.m 2KB
user_pathloss.mat 334B
armijo_theta.m 583B
ULA_fun.m 76B
surface_U_v_direct.m 652B
sum_rate_direct.m 338B
downbeam_lambda.m 189B
fun_theta_package.m 279B
new_channel_Hr.m 343B
new_channel_G.m 230B
downbeam_lambda_WMMSE.m 175B
armijo_theta_fullcycle_v3.m 529B
converge_AO_perfect.m 2KB
converge_A2_perfect.m 2KB
down_IRS_phaserand.mat 472B
update_beam_v2.m 2KB
converge_A2_imperfect.m 3KB
README.md 3KB
fig4.jpg 55KB
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