rerrhello-nb's starred repositories
OCDM-Basics
This repository holds the basic OCDM model and principles for communication and radar systems
HFCommSystem-MPSK-AdaptEqualizers-ITURHFMultipathChannel
MATLAB Imitation Modeling for the BER of the HF Communication System using M-PSK Modulation in the HF Communication Channel with Multipath and Signal Fading that constructed according to ITU-R Recommendation F.1487
TimeDelayEstimation
时间延迟估计(TDOA)相关算法
Uplink_impulsive
MATLAB codes for "Uplink channel estimation for massive MIMO systems with impulsive noise"
rand-kaczmarz-alg-for-mmimo
Codes for reproducing the numerical results reported in both: "Randomized Kaczmarz Algorithm for Massive MIMO Systems with Channel Estimation and Spatial Correlation" by Victor Croisfelt Rodrigues, José Carlos Marinello Filho, and Taufik Abrão. Wiley International Journal of Communication Systems, 2019. "Kaczmarz Precoding and Detection for Massive MIMO Systems" by the same authors. IEEE Wireless Communications and Networking Conference, Marrakech, Morroco, 2019: 1-6.
OFDM-FBMC-Transceiver
BSc Thesis
noise_simu
how to simulate colornoise
FBMC_UFMC_OFDM_5G
Measure the performance of three modulation techniques using PAPR, spectral efficiency, BER
FBMC-channel-estimation-based-on-SVR
Based on the SVR interpolation, a new method about the time-varying channel estimation of FBMC is proposed.
Channel-Estimation
Simulates an FBMC and OFDM transmission over a doubly-selective channel. Allows to reproduce all figures from "Doubly-Selective Channel Estimation in FBMC-OQAM and OFDM Systems", IEEE VTC Fall, 2018
FrFT-by-MATLAB
A realization of fractional Fourier transform based on MATLAB
uwblfm-cs-frft
parameter estimation for uwblfm signal
Low-Complex-Methods-for-Robust-Channel-Estimation-in-Doubly-Dispersive-Environments
This repository includes the source code of the DFT-based channel estimators proposed in "Low Complex Methods for Robust Channel Estimation in Doubly Dispersive Environments" paper [1] that is published in the IEEE Access, 2022.
Handgesture-recognition-using-depth-images
Algorithm recognizes the hand gestures signifying numbers from 1 to 5 from the depth images and their respective depth values using Finger Earth Mover’s Distance
Clustering--based--density--peaks
Clustering algorithms by fast search and find of density peaks
kernel-density-peaks
We develop the clustering approach by fast-search-and-find of density peaks which use the kernel function for mapping the input sample objects to the high dimensional feature space and amplifying the original sample objects’ characteristics, thereby researching the local density of the object attribute based on similarity measure. In order to identify our method’s effectiveness, we compare our method to other popular clustering algorithms. From the result analysis, we could conclude that our method have a better result.