basket163's starred repositories
sqsgenerator
A command line tool written in Python/C++ for finding optimized SQS structures
SQS_generation
This tutorial explains how to use ATAT to generate SQS for disordered alloys. It covers basic steps of creating a unit cell, generating cluster information, generating structure and then converting it to POSCAR file
Latex_ChinesePaper
模板是针对国内的一些期刊论文提供的，可以定制自己的模板
little-tsp
A C++ implementation of the branch and bound TSP algorithm described by Little et al in their 1963 paper
SCABeamformingIRS
Simulation code for "A Novel SCA-Based Method for Beamforming Optimization in IRS/RIS-Assisted MU-MISO Downlink," by V. Kumar, R. Zhang, M. D. Renzo and L. -N. Tran, IEEE Wireless Communications Letters, doi: 10.1109/LWC.2022.3224316.
IRS_MIMO_UnderlaySpectrumSharing
Simulation code for "Achievable Rate Maximization for Underlay Spectrum Sharing MIMO System with Intelligent Reflecting Surface," by V. Kumar, M. F. Flanagan, R. Zhang, and L. -N. Tran, IEEE Wireless Communications Letters, 2022, doi: 10.1109/LWC.2022.3180988.
RIS-Codes-Collection
RIS-Codes-Collection: A Complete Collection contains the Codes for RIS(IRS) Researches.
IRS-relaying
Simulation code for “Intelligent Reflecting Surface vs. Decode-and-Forward: How Large Surfaces Are Needed to Beat Relaying?,” by Emil Björnson, Özgecan Özdogan, Erik G. Larsson, IEEE Wireless Communications Letters, vol. 9, no. 2, pp. 244-248, February 2020.
IRS-modeling
Simulation code for “Intelligent Reflecting Surfaces: Physics, Propagation, and Pathloss Modeling,” by Özgecan Özdogan, Emil Björnson, Erik G. Larsson, IEEE Wireless Communications Letters, To appear.
IRSconfigurationDRL
Source code for "Intelligent Reflecting Surface Configurations for Smart Radio Using Deep Reinforcement Learning", IEEE JSAC.
IRSdiagnosis
MATLAB codes for implementing the diagnostic techniques in "Diagnosis of Intelligent Reflecting Surface in Millimeter-wave Communication Systems". Original article available at https://ieeexplore.ieee.org/abstract/document/9614042
IRS-continuous
Source code for "Intelligent Reflecting Surface Operation under Predictable Receiver Mobility: A Continuous Time Propagation Model" by Bho Matthiesen, Emil Björnson, Elisabeth De Carvalho, and Petar Popovski published in IEEE Wireless Communications Letters
IRS_Aided_Vehicular_Communications
Simulations on IRS aided vehicular communications
Framework-of-Robust-Transmission-Design-for-IRS-Aided-MISO-Communications
Simulation code for “A Framework of Robust Transmission Design for IRS-Aided MISO Communications With Imperfect Cascaded Channels,” by G. Zhou, C. Pan, et al, IEEE TSP, vol. 68, pp. 5092-5106, 2020.
CDRN-channel-estimation-IRS
Code for our paper 'Deep Residual Network Empowered Channel Estimation for IRS-Assisted Multi-User Communication Systems'.
IRS_Enhanced-Wireless-Network_Joint-Active-and-Passive-BeamformingDesign_Qingqing-Wu-and-Rui-Zhang
paper simulation "Intelligent Reflecting Surface Enhanced Wireless Network_Joint Active and Passive BeamformingDesign" Qingqing Wu and Rui Zhang
adaptive-beamforming
Neural network approach to adaptive beam forming for future ITS
DeepLearning-CoordinatedBeamforming
Simulation code for "Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems" by Ahmed Alkhateeb, Sam Alex, Paul Varkey, Ying Li, Qi Qu, and Djordje Tujkovic, in IEEE Access, vol. 6, pp. 37328-37348, 2018.
Power-Allocation-Algorithms-for-Massive-MIMO-Systems-with-Multi-Antenna-Users
Modern 5G wireless cellular networks use massive multiple-input multiple-output (MIMO) technology. This concept entails using an antenna array at a base station to concurrently service many mobile devices that have several antennas on their side. In this field, a significant role is played by the precoding (beamforming) problem. During downlink, an important part of precoding is the power allocation problem that distributes power between transmitted symbols. In this paper, we consider the power allocation problem for a class of precodings that asymptotically work as regularized zero-forcing. Under some realistic assumptions, we simplify the sum spectral efficiency functional and obtain tractable expressions for it. We prove that equal power allocation provides optimum for the simplified functional with total power constraint (TPC). Also, low-complexity algorithms that improve equal power allocation in the case of per-antenna power constraints (PAPC) are proposed. On simulations using Quadriga, the proposed algorithms show a significant gain in sum spectral efficiency while using a similar computing time as the reference solutions.