shaohua0720 Goto Github PK

followers: 28.0 following: 145.0 repos: 196.0 gists: 1.0

Name: Ai. Shao

Type: User

Company: University of Science and Technology Beijing

Location: Beijing, P.R. China

Ai. Shao's Projects

compressai

A PyTorch library and evaluation platform for end-to-end compression research

configuration_files

Contains a list of configuration files for AFF3CT.

construct-long-length-block-and-convolutional-met-qc-ldpc-

construction high perfomance long length, low error floor block Multi-edge Type (MET) QC-LDPC codes and Tail-Biting convolutional MET QC-LDPC Codes (MET QC-LDPCC codes)

This is the COST2100 channel model, a MATLAB implementation of a spatially consistent radio channel model for MIMO and Massive MIMO communication. Originally developed within COST 2100 (http://www.cost2100.org/) and then further extended in COST IC 1004 (http://www.ic1004.org/) and COST CA15104 IRACON (http://www.iracon.org/)

cpp-btree

Automatically exported from code.google.com/p/cpp-btree

culdpc

CUDA implementation of LDPC decoding algorithm

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.

ddlcn

[WACV 2019 Oral] Deep Micro-Dictionary Learning and Coding Network

decentralizedfeedforwardequalizer

Simple MATLAB simulator for decentralized feedforward equalization in massive MU-MIMO systems

deep-learning-channel-estimation

Simulation code for “Channel Estimation in Massive MIMO under Hardware Non-Linearities: Bayesian Methods versus Deep Learning,” by Özlem Tugfe Demir, Emil Björnson, IEEE Open Journal of the Communications Society, To appear.

deep-learning-for-svd-and-hybrid-beamforming

Deep neural network (DNN) architectures to compute the SVD and hybrid beamformers

deeplearning_mimo-noma

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.

demonstration-dft-ps-psd

This is a demonstration to show how to calculate power spectra and power spectral densities in real time. We calculate power spectra directly using DFT (or FFT). There are many conventions for DFT. We use the convention is the paper “Analysis of Relationship between Continuous Time Fourier Transform (CTFT), Discrete Time Fourier Transform (DTFT), Fourier Series (FS), and Discrete Fourier Transform (DFT)”. We calculate power spectral and power spectral densities using the MATLAB function periodogram. We could use pwelch to replace periodogram. The only difference between periodogram and pwelch is that pwelch supports segmentation and averaging, whereas periodogram does not. For the sake of simplicity, we only use periodogram in this demonstration. One will see that the power spectrum is equal to the square of the absolute value of DFT. When manually calculating a power spectrum, the hard job is to calculate the argument vector, or the independent variable vector, which is a frequency vector in this case. The frequency vector depends on the representation of the power spectrum. In general, there are three ways to represent a power spectrum for a real valued signal. One way is called “two-sided”. This is the default way to represent a power spectrum with DFT. However, this representation is not intuitive. The frequency vector is calculated by f = (0:N-1)/T, where T is the time period (or duration) of the input signal. When using the MATLAB function, periodogram, one can specify this representation using “onesided”. A more natural way is to use a centered representation. In this case, the frequency 0 is centered in the spectrum. If the number of spectral lines (equal to the number of input points) is odd, then we have a unique centered representation. If the number of spectral lines is even, then we have a problem. Let us assume that we use a zero-based index for spectral lines. The spectral line 0 is the DC component, and it is put in the f = 0 location. However, the spectral line N/2 can be placed on the positive side or the negative side. Different conventions may have different placements. In order to obtain this representation, one has to shift the FFT result. One way is to use the MATLAB function fftshift. This MATLAB function always places the N/2 spectral line on the negative side. When using the MATLAB function, periodogram, one can specify this representation using “centered”. It should be noted that the MATLAB function, periodogram, usually puts the N/2 spectral line on the positive side. The last way to represent a power spectrum is the one-sided representation. For this representation, we need to combine negative frequency components and positive components together, and we only show the positive half as well as the DC component. The combination process depends the evenness or oddness of the number of spectral lines. If the number of spectral lines is odd, we can simply combine spectral lines 1 to (N-1)/2 with spectral lines (N+1)/2 to N-1. The spectral line 0 is left untouched. If the number of spectral lines is even, we need to combine spectral lines 1 to N/2-1 with lines N/2+1 to N-1. The spectral lines 0 and N/2 are left untouched. In order to obtain this representation, one has to manually carry out the combination process. The combination process is different depending on the evenness or oddness of the number of spectral lines. When using the MATLAB function, periodogram, one can specify this representation using “onesided”. In this demonstration, we only use the centered representation. Hence, there is no need to do combination. One can see that the sum of all power spectral lines in a power spectrum is equal to the power of the input signal. One can alternatively calculate the PSD with the periodogram function by specifying “psd” instead of “power”. In fact, the PSD obtained by periodogram is an equivalent noise power spectral density. One can see that ENPSD is related to PS by a factor of 1/T. It should be noted that a power spectrum is a discrete sequence, or a discrete continuous-argument function, whereas an ENPSD is a non-discrete continuous argument function. For emphasize this, I used stem for power spectra and plot for ENPSD. In this demonstration, we start with a sinusoidal signal with various parameters. We then proceed with an actual audio signal.

density-evolution-awgn

Density evolution for LDPC codes construction under AWGN-channel: reciprocal-channel approximation (RCA), Gaussian Evolution, Covariance Evolution

dictol

DICTOL - A Dictionary Learning Toolbox in Matlab and Python

doa-estimation-music

Estimation of direction of arrivals (DOA) using the MUSIC algorithm

dvb

Distributed variational Bayesian algorithms over sensor networks

energy_efficiency_optimization_for_mimo_interference_networks

Solving energy efficiency optimization problem for MIMO interference wireless network using iterative semi-definite programming with WMMSE (SDP-WMMSE) algorithm and successive convex approximation (SCA) algorithm

etfe

C++ Empirical Transfer Function Estimate (ETFE) Similar to MATLAB tfestimate, pwelch, and cpsd

exact-and-approximate-inference-for-graphical-models

Message Passing Over a Junction Tree, Loopy Belief Propagation, Max-Product (MAP inference) variant of the MP and LBP inference algorithms

exit-chart

This application estimate iterative decoding threshold using degree description, Stephan ten Brink. More general decription of LDPC codes ensemble

exit-matlab

Tools for plotting Extrinsic Information Transfer (EXIT) charts in Matlab

face-detection-and-skin-retouching

人脸检测与自动磨皮

factor-graph

matlab/c++ factor graph framework

fecl

FeCl is a channel coding library to help analysis of communication systems in research and education.

fftw-convolution-example-1d

A simple example of performing a one-dimensional discrete convolution using the FFTW library.

fldrl-in-wireless-communication

Apply Deep Reinforcement Learning aided by Federated Learning to Wireless Comunication

flearadio

Digital FM Radio Receiver for FPGA

free-programming-books-zh_cn

免费的计算机编程类中文书籍，欢迎投稿

gaimc

Graph Algorithms in Matlab Code

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