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1-bit-adcs

Deep Learning for Massive MIMO with 1-Bit ADCs

202008-hybrid-rcg

An implementation of the Hybrid Riemannian conjugate gradient method

acoustic-direction-finding-using-single-acoustic-vector-sensor-under-high-reverberation

We propose a novel and robust method for acoustic direction finding, which is solely based on acoustic pressure and pressure gradient measurements from single Acoustic Vector Sensor (AVS). We do not make any stochastic and sparseness assumptions regarding the signal source and the environmental characteristics. Hence, our method can be applied to a wide range of wideband acoustic signals including the speech and noise-like signals in various environments. Our method identifies the “clean” time frequency bins that are not distorted by multipath signals and noise, and estimates the 2D-DOA angles at only those identified bins. Moreover, the identification of the clean bins and the corresponding DOA estimation are performed jointly in one framework in a computationally highly efficient manner. We mathematically and experimentally show that the false detection rate of the proposed method is zero, i.e., none of the time-frequency bins with multiple sources are wrongly labeled as single-source, when the source directions do not coincide. Therefore, our method is significantly more reliable and robust compared to the competing state-of-the-art methods that perform the time-frequency bin selection and the DOA estimation separately. The proposed method, for performed simulations, estimates the source direction with high accuracy (less than 1 degree error) even under significantly high reverberation conditions.

adaptivefilters.jl

Classical adaptive linear filters in Julia

admmirnn

ADMMiRNN: Training RNN with Stable Convergence via An Efficient ADMM Approach

alternating-minimization-algorithms-for-hybrid-precoding-in-millimeter-wave-mimo-systems

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.

amath-515-code

Useful code such as proxgrad, FISTA,ADMM, coordinate descent (all for lasso) plus projection onto unit ball... from AMATH 515

ambiguity-function

Calculating and plotting ambiguity function of a radar-repeater network

antenna-selection

Some basic antenna selection algorithms

antenna-selection-and-beamforming-with-bandb-and-ml

Machine learning accelerated Branch and Bound for Joint beamforming and antenna selection

arraysim

Simulation of atenna array. Beamforming and DOA estimation.

attention_channel_estimation

automatic-modulation-classification

Some Code for Master Thesis - Research on Deep Learning Based Modulation Recognition Technologies

automatic-modulation-classification-1

Accumulated Polar Feature-based Deep Learning for Automatic Modulation Classification

awesome-deep-neuroevolution

A collection of Deep Neuroevolution resources or evolutionary algorithms applying in Deep Learning (constantly updating)

awesome-latex-cv

Latex CV template built with Font Awesome.

awesome-ml4co

awesome-model-quantization

A list of papers, docs, codes about model quantization. This repo is aimed to provide the info for model quantization research, we are continuously improving the project. Welcome to PR the works (papers, repositories) that are missed by the repo.

awesome-speech-enhancement

speech enhancement\speech seperation\sound source localization

bayesianoptimization

A Python implementation of global optimization with gaussian processes.

bayesopt

BayesOpt: A toolbox for bayesian optimization, experimental design and stochastic bandits.

beampatterndesign

Optimal Beam Pattern Design for Hybrid Beamforming in Millimeter Wave Communications

bf-design-with-dl

Beamforming design with deep learning.

botorch

Bayesian optimization in PyTorch

bsca

Matlab code for block successive convex approximation algorithms

cell-free-mimo-refs

Cell-Free MIMO 学习交流资料

cell-free-survey

comparison-of-blind-source-separation-techniques

Compare AIRES BSS with TRINICON, ILRMA and AuxIVA (online and offline versions)

d-amp_toolbox

This package contains the code to run Learned D-AMP, D-AMP, D-VAMP, D-prGAMP, and DnCNN algorithms. It also includes code to train Learned D-AMP, DnCNN, and Deep Image Prior U-net using the SURE loss.

d3l-hb

data-driven deep learning-based hybrid beamforming