[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.

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.

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

Source code of the Paper "Diffusion-Based Generative Prior for Low-Complexity MIMO Channel Estimation"

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

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

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.

Official implementation of "RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection" paper.

Source code for the EMNLP 2019 paper "Multi-Input Multi-Output Sequence Labeling for Joint Extraction of Fact and Condition Tuples from Scientific Text" (给定科研文本如生物医药文献，联合抽取其中事实三元组、条件三元组，即对文献进行信息结构化)

This is a python simulation of a MIMO communication system, including M-QAM modulation and demodulation.

Partial implementation of the research paper "Sohrabi, Foad, & Yu, Wei (2016). Hybrid Digital and Analog Beamforming Design for Large-Scale Antenna Arrays" in Python.

Reinforcement learning environment for MIMO communications.

A comparative study of deep learning models for predicting Channel State Information (CSI) in massive MIMO systems. Integrates COST2100 dataset with STNet compression and evaluates models based on NMSE, RMSE, and spectral efficiency.

source codes for paper "RIS-Assisted MIMO Semantic Communication System for Speech Transmission"

This repository contains the code, datasets, and simulation tools for the paper "Machine Learning-Based mmWave MIMO Beam Tracking in V2I Scenarios: Algorithms and Datasets", published at IEEE Latincom 2024.

Python code for the paper "A Low-Complexity MIMO Channel Estimator with Implicit Structure of a Convolutional Neural Network".

This repo implements a MIMO-based method for semantic communications, learning precoder/decoder pairs to compress latent spaces and align semantics across devices. Includes both a linear ADMM-based model and a neural model under power and complexity constraints.

This repository tackles latent space misalignment in multi-agent AI-native semantic communications. It introduces a federated approach where an access point shares a semantic encoder, while user devices use local semantic equalizers to enhance mutual understanding.