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

[CVPR 2024 Oral, Best Paper Award Candidate] Official repository of "PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness"

Simulation of Digital Communication (physical layer) in Python.

A simple example with how hybrid beamforming is employed at the transmit end of a massive MIMO communications system.

📡 Using Software Designed Radio to transmit MIMO-OFDM QPSK signals at 5 GHz

Optimization algorithms for hybrid precoding in mmWave MIMO systems: Version 1.1.0

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.

Channel Estimation for MIMO OFDM Systems (LSE& MMSE)

Subspace methods for MIMO system identification

Numerical simulation platform to evaluate the perfomances of a 480 Gb/s optical coherent communication system using different advanced technologies deployed in optical networks, including MIMO equalization techniques.

Repository for the models and code used in "UE Tracking Beamforming using a MIMO Software-Defined Radio" MSc project.

A Comparative Study of Deep Learning and Iterative Algorithms for Joint Channel Estimation and Signal Detection in OFDM Systems

Source code for "Space-time design for deep joint source channel coding of images Over MIMO channels", SPAWC 2023, https://ieeexplore.ieee.org/document/10304536

The simulation of paper: Joint Cooperation Clustering and Content Caching in Cell-Free Massive MIMO Networks

Real-Time Inference of 5G NR Multi-user MIMO Neural Receivers

Implementation of "Over-the-Air Design of GAN Training for mmWave MIMO Channel Estimation"

Source Code for Paper "Augmenting Channel Charting with Classical Wireless Source Localization Techniques"

5G Tookit provides a rich set of 3GPP standards compliant modules and libraries. These modules can be used for reseach and development on physical channels and procedures in uplink and downlink communication.

Scripts for the "ITU-ML5G-PS-006: ML5G-PHY-Reinforcement learning: scheduling and resource allocation"

Low-Complexity Precoding for Extremely Large-Scale MIMO Over Non-Stationary Channels