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Simulation codes for "Alternating minimization algorithms for hybrid precoding in millimeter wave MIMO systems," by Xianghao Yu, Juei-Chin Shen, Jun Zhang, and Khaled B. Letaief, IEEE J. Sel. Topics Signal Process., to appear, 2016.
A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.
Simulation code for “Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation,” by Emil Björnson and Luca Sanguinetti, IEEE Transactions on Wireless Communications, vol. 19, no. 1, pp. 77-90, January 2020
Implementations from the free course Deep Reinforcement Learning with Tensorflow
A repository for the DeepIRS project codes
Realization of MIMO-NOMA signal detection system based on **C. Lin et al., “A deep learning approach for MIMO-NOMA downlink signal detection,” MDPI Sensors, vol. 19, no. 11, pp. 2526, 2019.
Data-driven implementaion of soft iterative interference cancellation for MIMO detection
Source code for paper Deep Learning for Direct Hybrid Precoding in Millimeter Wave Massive MIMO Systems
Algorithmic solution for hybrid beamforming on full-duplex millimeter wave communications
Discrete Simulator for Scheduling in Full Duplex OFDMA Wireless Networks
Matlab simulation of fullduplex
Game Theory based Radio Resource Allocation for full-duplex systems
Index your Google Drive
Google Drive Directory Index
Paper about Small Cell, Massive MIMO and Full-duplex
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.
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.
This directory contains all the codes required to reproduce the results in our CAMSAP 2017 paper titled "Joint CFO and channel estimation in millimeter wave systems with one-bit ADCs"
Simulation code for "Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning" by Abdelrahman Taha, Muhammad Alrabeiah, Ahmed Alkhateeb, arXiv e-prints, p. arXiv:1904.10136, Apr 2019.
A simple example with how hybrid beamforming is employed at the transmit end of a massive MIMO communications system.
Book PDF and simulation code for the monograph "Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency" by Emil Björnson, Jakob Hoydis and Luca Sanguinetti, published in Foundations and Trends in Signal Processing, 2017.
Matlab code of the paper: N. T. Do, D. B. da Costa, T. Q. Duong, and B. An, “A BNBF User Selection Scheme for NOMA-Based Cooperative Relaying Systems With SWIPT,” IEEE Communications Letters, vol. 21, no. 3, pp. 664–667, Mar. 2017.
Matlab code of the paper: T.-N. Do, V.-D. Nguyen, O.-S Shin, and B. An, "Simultaneous Uplink and Downlink Transmissions for Wireless Powered Communication Networks," in IEEE Communications Letters, 2018. doi: 10.1109/LCOMM.2018.2885303
Matlab code of the paper: T. N. Do, D. B. da Costa, T. Q. Duong, and B. An, “Improving the Performance of Cell-Edge Users in NOMA Systems Using Cooperative Relaying,” IEEE Transactions on Communications, vol. 66, no. 5, pp. 1883–1901, May 2018.
Matlab code of the paper: T. N. Do, D. B. da Costa, T. Q. Duong, and B. An, “Improving the performance of cell-edge users in MISO-NOMA systems using TAS and SWIPT-based cooperative transmissions,” IEEE Transactions on Green Communications and Networking, vol. 2, no. 1, pp. 49–62, Mar. 2018.
Matlab code of the paper: N. T. Do, D. B. da Costa, T. Q. Duong, V. N. Q. Bao, and B. An, “Exploiting direct links in multiuser multirelay SWIPT cooperative networks with opportunistic scheduling,” IEEE Transactions on Wireless Communications, vol. 16, no. 8, pp. 5410–5427, Aug. 2017
Simulation Codes for Matrix-Calibration-Based Cascaded Channel Estimation for Reconfigurable Intelligent Surface Assisted Multiuser MIMO
Joint Subcarrier and Power Allocation algorithms for WSR maximization in NOMA published in IEEE TSP 2020
Final Year Project VIT
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.