Maximum Rate Scheduling With Adaptive Modulation in Mixed Impulsive Noise and Additive White Gaussian Noise Environments

Abstract

This article proposes an opportunistic scheduling scheme based on adaptive modulation for users in a mixed noise environment, where some users are under additive white Gaussian noise (AWGN) and other users are exposed to impulsive noise. Unlike the scenario, where all the users are in an AWGN environment, the maximum signal-to-noise ratio (SNR) scheduler does not provide the maximum capacity if users are in a mixed noise environment. In the proposed scheduling scheme, called maximum rate scheduler, the user with the highest rate (or highest modulation order), instead of highest SNR, is selected. To evaluate the performance of the proposed scheduling scheme, the analyses of average spectral efficiency, outage probability and system capacity are provided along with a simple calculation of SNR thresholds for adaptive modulation in an impulsive noise environment satisfying target symbol error rate (SER). Simulation results illustrate that system capacity with the proposed scheduling given target SER = 10 −2 and the average spectral efficiency are improved by 156% and 124%, respectively, at SNR = 5 dB when a strong impulsive noise is present.

Parallel Selector for Feature Reduction

“…In our experiments, the label noise is used to generate data perturbation. Specifically, the perturbated labels are used to inject into raw labels, and if the perturbation ratio is given as β%, the injection is realized by randomly selecting β% number of samples and injecting white Gaussian noise(WGN) [63] into their labels. It should be emphasized that excessive WGN ratio of raw labels will lead to the data losing their original semantics.…”

Fault Classification with Convolutional Neural Networks for Microgrid Systems

“…us, in addition to 20 dB SNR with additive white Gaussian noise (AWGN) [22][23][24], the additive impulse Gaussian noise (AIGN) [24][25][26] is added to mimic the impulses generated due to random switching of loads in the MG and impulses created from the switching operation of inverters [27,28]. Furthermore, this research adapts the widely used DWT along with the convolutional neural networks (CNNs) to develop the fault classification approach.…”

Phase Congruential White Noise Generator

“…An analysis of the literature shows that most of the articles describe different applications of white noise, as well as its recognition in transmitted signals. This includes the areas such as theoretical and applied mathematics [5][6][7][8][9], physical research [3,4,[10][11][12][13], electronic and radio engineering [14][15][16][17][18][19][20][21][22][23], acoustics and noise phenomena [1][2][3][4]24,25], computer algorithms [26,27], geological prospecting and exploration [28,29], medical and biological research [30][31][32][33][34][35][36], psychology and psychiatry [37,38], and others. It is worth noting that significant results have been achieved in those fields.…”