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-downlink-massive-mu-mimo-precoding-techniques_dissertaion_code

1-bit-adcs

Deep Learning for Massive MIMO with 1-Bit ADCs

1bit_precoding

1bit_precoding_ofdm

2020-rw-constrained-optimisation

2302_learning_beamforming_code

3d_array_joint_loc_and_com

3d_scm

Signal processing for digital communication written in Matlab

5g-wireless-network

5gm-rwi-simulation

5GMdata project: Scripts to repeatedly invoke Remcom Wireless Insite and SUMO

6g-channel-estimation-dataset

6G Wireless Communication Security - Deep Learning Based Channel Estimation Dataset

6g_security

6G and Security repository for telecommunications and AI research. We will share our implementations and publications in 5G and beyond technology, 6G, Security, Machine learning on 6G, Massive MIMO, THz communication and communication networks.

a-bft-beamforming-802.11ad

IEEE 802.11ad A-BFT beamforming training simulator in Python

a-comparison-of-successive-interference-cancellation

Simulation code for the comparitive analysis of the number of SICs increase with the number of increase in devices in NOMA, and cooperative NOMA types

a-markovian-model-driven-deep-learning-framework-for-massive-mimo-csi-feedback

Reproduction of CsiNet-LSTM

a-multi-irs-ris-multi-user-beamforming-method-based-on-improved-particle-swarm-algorithm

patent

a-spatially-separable-attention-mechanism-for-massive-mimo-csi-feedback

Spatially Separable Attention Transformer Network implemented in Pytorch

acmix

Official repository of ACmix (CVPR2022)

acoustic_precoding

acrnet

This is an implementation of ACRNet for results reproduction on COST2100

active-ris-isac

Simulation codes for my paper in TCOM: Active RIS-Aided ISAC Systems: Beamforming Design and Performance Analysis

ad_sbl_unfold

adaptive-regularized-zero-forcing-beamforming-in-massive-mimo-with-multi-antenna-users

Modern wireless cellular networks use massive multiple-input multiple-output (MIMO) technology. This technology involves operations with an antenna array at a base station that simultaneously serves multiple mobile devices which also use multiple antennas on their side. For this, various precoding and detection techniques are used, allowing each user to receive the signal intended for him from the base station. There is an important class of linear precoding called Regularized Zero-Forcing (RZF). In this work, we propose Adaptive RZF (ARZF) with a special kind of regularization matrix with different coefficients for each layer of multi-antenna users. These regularization coefficients are defined by explicit formulas based on SVD decompositions of user channel matrices. We study the optimization problem, which is solved by the proposed algorithm, with the connection to other possible problem statements. We also compare the proposed algorithm with state-of-the-art linear precoding algorithms on simulations with the Quadriga channel model. The proposed approach provides a significant increase in quality with the same computation time as in the reference methods.

adaptive-widely-linear-constrained-constant-modulus-reduced-rank-beamforming

Simulation code for "X. Wu, Y. Cai, M. Zhao, R. C. de Lamare and B. Champagne, "Adaptive Widely Linear Constrained Constant Modulus Reduced-Rank Beamforming," in IEEE Transactions on Aerospace and Electronic Systems, vol. 53, no. 1, pp. 477-492, Feb. 2017, doi: 10.1109/TAES.2017.2650838."

add-gcn

ADD-GCN: Attention-Driven Dynamic Graph Convolutional Network for Multi-Label Image Recognition (ECCV 2020)

add-gcn-1

Unofficial PyTorch implementation of the paper "ADD-GCN: Attention-Driven Dynamic Graph Convolutional Network for Multi-Label Image Recognition" ECCV 2020

admm-neuralnetworks

Training Neural Networks Without Gradients: A ADMM Approach

admm-pruning

Prune DNN using Alternating Direction Method of Multipliers (ADMM)

advanced-practical-machine-learning

Clustering, Image Denoising, Manifold Learning and Autoencoders

ai-empowered-ris-assisted-noma-networks-