little-fairy-wang

little-fairy-wang's Stars

• datawhalechina/easy-rl

  强化学习中文教程（蘑菇书🍄），在线阅读地址：https://datawhalechina.github.io/easy-rl/

  Language:Jupyter Notebook9.2k791431.8k

• LyricYang/MIMO_OFDM

  《MIMO-OFDM无线通信技术及MATLAB实现》随书源码

  Language:Matlab760151272

• ChihebTrabelsi/deep_complex_networks

  Implementation related to the Deep Complex Networks

  Language:Python7254238280

• nyuwireless-unipd/ns3-mmwave

  ns-3 module for simulating mmWave-based cellular systems. See https://ieeexplore.ieee.org/document/8344116/ (open access) as a reference.

  Language:C++29844247193

• TianLin0509/BF-design-with-DL

  Beamforming design with deep learning.

  Language:MATLAB2511010105

• DeepMIMO/DeepMIMO-matlab

  DeepMIMO dataset and codes for mmWave and massive MIMO applications

  Language:MATLAB146111053

• farismismar/Deep-Reinforcement-Learning-for-5G-Networks

  Code for my publication: Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination. Paper accepted for publication to IEEE Transactions on Communications.

  Language:Python14414387

• TianLin0509/Communication_eBooks

  上传一些关于通信的好书，以便不时之需。

  853038

• lasseufpa/5gm-data

  Datasets and code for machine learning in 5G mmWave MIMO systems involving mobility (5GMdata)

  Language:Python8313635

• BJTU-MIMO/cell-free-survey

  765031

• HamedHojatian/HBF-Net

  Simulation code for "Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming" by Hamed Hojatian, Jeremy Nadal, Jean-Francois Frigon, Francois Leduc-Primeau, 2020.

  Language:Python624214

• trunk96/wireless-network-simulator

  4G/5G network simulation with UE handovers

  Language:Python581013

• lasseufpa/5gm-beam-selection

  Beam-selection for mmWave MIMO using machine learning

  Language:Python4212223

• hqyyqh888/DDQN_BeamSelection

  Joint Deep Reinforcement Learning and Unfolding: Beam Selection and Precoding for mmWave Multiuser MIMO With Lens Arrays

  Language:Python401015

• malrabeiah/Sub6-Preds-mmWave

  Using sub-6 GHz channels to predict mmWave beams and link blockage.

  Language:MATLAB343419

• wineslab/deepbeam

  M. Polese, F. Restuccia, and T. Melodia, "DeepBeam: Deep Waveform Learning for Coordination-Free Beam Management in mmWave Networks", Proc. of ACM Intl. Symp. on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (ACM MobiHoc), July 2021.

  Language:Python29616

• Naren920421/Millimeter-Wave-Beam-Training-

  The MATLAB code for implementing the paper "Low-Complexity Beam Training for Tracking Spatially Consistent Millimeter Wave Channels"

  Language:MATLAB23007

• tranlenam/cellfreeMIMOenergyefficiency

  Simulation for "On the total energy efficiency of cell-free massive MIMO" by H. Q. Ngo, L.-N. Tran, T. Q. Duong, M. Matthaiou, E. G. Larsson, IEEE Trans. Green Commun. and Network., vol. 2, no. 1, pp. 25-39, Mar. 2018.

  Language:MATLAB22206

• loaydatrain/Optimizing_Millimeter_Wave_Communication

  Used convex optimization to tune millimetre wave based wireless communication parameters. Replicated the IEEE paper "Multi-Beam Power Allocation for mmWave Communications under Random Blockage"

  Language:MATLAB211011

• jmoraispk/Position-Beam-Prediction

  Repository for the code user in the paper...

  Language:Python18106

• farismismar/Handover-Improvement-ML

  Code for my publication: Partially Blind Handovers for mmWave New Radio Aided by Sub-6 GHz LTE Signaling. Paper accepted to IEEE International Conference of Communications Workshops, 2018.

  Language:Python14209

• gowtham-chandrasekaran/Handover-Optimisation-in-5G-using-Reinforcement-Learning

  Optimised the number of handovers using the Q-Learning algorithm of Reinforcement Learning

  Language:Jupyter Notebook13

• malrabeiah/learningCB

  Learning Environment-aware and hardware-compatible beam-forming codebooks

  Language:Python13214

• YuqiangHeng/ML-mmWave-Beam-Alignment

  Language:Jupyter Notebook9106

• YiWei0129/Fast-beam-alignment

  Fast Beam Alignment via Pure Exploration in Multi-armed Bandits

  7201

• SeokhyunJeong/DualThreshold_BeamTraining

  This is fast beam training algorithm based on E. Eltayeb's opportunistic beam training. In this work, two thresholds algorithm is proposed. If beam SNR exceeds the first threshold, we use that beam for communication without further searching. Else if SNR exceeds the second threshold(=0.65 * the first threshold), we search narrower range centered on that beam(range *= 1/4 or 1/8) so that we can find the desired beam.

  Language:MATLAB510

• KimYeongJe95/V2Xcomm_contexual_bandit

  5G_based_beam alignment problem solving using contextual bandit

  Language:MATLAB41

• sagar-kaushik/Beam-Prediction

  mmWave beam prediction using sub-6GHz waves and user location

  Language:Jupyter Notebook41

• YuZhang-GitHub/CBL_Self_Supervised

  Language:Python4101

• KingsYR123/Paper-with-Code-of-Wireless-communication-Based-on-DL

  无线与深度学习结合的论文代码整理/Paper-with-Code-of-Wireless-communication-Based-on-DL

  3000