scholarly journals 3D-MIMO Channel Estimation under Non-Gaussian Noise with Unknown PDF

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 316 ◽  
Author(s):  
Wei Chen ◽  
Feng Li ◽  
Yiting Peng
Keyword(s):  
Channel Estimation ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Gaussian Mixture ◽  
Mimo Channel ◽  
Estimation Model ◽  
Channel Matrix ◽  
Common Support ◽  
3D Mimo ◽  
Non Gaussian

Three-dimensional-multiple-input-multiple-output (3D-MIMO) technology has attracted a lot of attention in the field of wireless communication. Most of the research mainly focuses on channel estimation model which is affected by additive-white-Gaussian-noise (AWGN). However, under the influence of some specified factors, such as electronic interference and man-made noise, the noise of the channel does not follow the Gaussian distribution anymore. Sometimes, the probability density function (PDF) of the noise is unknown at the receiver. Based on this reality, this paper tries to address the problem of channel estimation under non-Gaussian noise with unknown PDF. Firstly, the common support of angle domain channel matrix is estimated by compressed sensing (CS) reconstruction algorithm and a decision rule. Secondly, after modeling the received signal as a Gaussian mixture model (GMM), a data pruning algorithm is exerted to calculate the order of GMM. Lastly, an expectation maximization (EM) algorithm for linear regression is implemented to estimate the the channel matrix iteratively. Furthermore, sparsity, not only in the time domain, but in addition in the angle domain, is utilized to improve the channel estimation performance. The simulation results demonstrate the merits of the proposed algorithm compared with the traditional ones.

scholarly journals Rate-compatible LDPC convolutional codes over non-gaussian noise channel

2020 ◽  
Vol 309 ◽  
pp. 01010
Author(s):  
Qiang Liu
Keyword(s):  
Gaussian Noise ◽  
Iterative Decoding ◽  
Belief Propagation ◽  
Additive White Gaussian Noise ◽  
Convolutional Codes ◽  
Gaussian Mixture ◽  
Performance Study ◽  
Shift Keying ◽  
Non Gaussian ◽  
The Impact

This paper is aimed to study the characteristics of the underwater acoustic channel with non-Gaussian noise channel. And Gaussian mixture model (GMM) is utilized to fit the background noise over the non-Gaussian noise channel. Furthermore, coding techniques which use a sequence of rate-compatible low-density parity-check (RC-LDPC) convolutional codes with separate rates are constructed based on graph extension method. The performance study of RC-LDPC convolutional codes over non-Gaussian noise channel and the additive white Gaussian noise (AWGN) channel is performed. Study implementation of simulation is that modulation with binary phase shift keying (BPSK), and iterative decoding based on pipeline log-likelihood rate belief propagation (LLRBP) algorithm. Finally, it is shown that RC-LDPC convolutional codes have good bit-rate-error (BER) performance and can effectively reduce the impact of noise.

M-Ary Alpha-Stable Noise Modulation in Spread-Spectrum Communication

2015 ◽  
Vol 14 (03) ◽  
pp. 1550022 ◽  
Author(s):  
Mehmet Emre Cek
Keyword(s):  
Gaussian Noise ◽  
Spread Spectrum ◽  
Additive White Gaussian Noise ◽  
Characteristic Exponent ◽  
Random Signal ◽  
Spread Spectrum Communication ◽  
Order Moment ◽  
Gaussian Density ◽  
Shift Keying ◽  
Non Gaussian

In this paper, a spread-spectrum communication system based on a random carrier is proposed which transmits M-ary information. The random signal is considered as a single realization of a random process taken from prescribed symmetric α-stable (SαS) distribution that carries digital M-ary information to be transmitted. Considering the noise model in the channel as additive white Gaussian noise (AWGN), the transmitter sends the information carrying random signal from non-Gaussian density. Alpha-stable distribution is used to encode the M-ary message. Inspired by the chaos shift keying techniques, the proposed method is called M-ary symmetric alpha-stable differential shift keying (M-ary SαS-DSK). The main purpose of preferring non-Gaussian noise instead of conventional pseudo-noise (PN) sequence is to overcome the drawback of self-repeating noise-like sequences which are detectable due to the periodic behavior of the autocorrelation function of PN sequences. Having infinite second order moment in α-stable random carrier offers secrecy of the information due to the non-constant autocorrelation behavior. The bit error rate (BER) performance of the proposed method is illustrated by Monte Carlo simulations with respect to various characteristic exponent values and different data length.

Robust correntropy ICA based blind channel estimation for MIMO-OFDM systems

2015 ◽  
Vol 34 (3) ◽  
pp. 962-978
Author(s):  
Khaled Abdulaziz Alaghbari ◽  
Lim Heng Siong ◽  
Alan W.C. Tan
Keyword(s):  
Channel Estimation ◽  
Simulation Study ◽  
Gaussian Noise ◽  
Blind Channel Estimation ◽  
Channel Estimator ◽  
Content Type ◽  
Comparison Results ◽  
Blind Channel ◽  
Mimo Ofdm ◽  
Non Gaussian

Purpose – The purpose of this paper is to propose a robust correntropy assisted blind channel estimator for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) for improved channel gains estimation and channel ordering and sign ambiguities resolution in non-Gaussian noise channel. Design/methodology/approach – The correntropy independent component analysis with L1-norm cost function is used for blind channel estimation. Then a correntropy-based method is formulated to resolve the sign and order ambiguities of the channel estimates. Findings – Simulation study on Gaussian noise scenario shows that the proposed method achieves almost the same performance as the conventional L2-norm based method. However, in non-Gaussian noise scenarios performance of the proposed method significantly outperforms the conventional and other popular estimators in terms of mean square error (MSE). To solve the ordering and sign ambiguities problems, an auto-correntropy-based method is proposed and compared with the extended cross-correlation-based method. Simulation study shows improved performance of the proposed method in terms of MSE. Originality/value – This paper presents for the first time, a correntropy-based blind channel estimator for MIMO-OFDM as well as simulated comparison results with traditional correlation-based methods in non-Gaussian noise environment.

scholarly journals Positioning Techniques in Indoor Environments Based on Stochastic Modeling of UWB Round Trip Time Measurements

2021 ◽  
Author(s):  
Paolo Carbone
Keyword(s):  
Particle Filter ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Least Square ◽  
Indoor Environments ◽  
Round Trip Time ◽  
Round Trip ◽  
Positioning Systems ◽  
Trip Time ◽  
Non Gaussian

<div><div><div><p>In this paper, a technique for modeling propagation of Ultra Wide Band (UWB) signals in indoor or outdoor environments is proposed, supporting the design of a positioning systems based on Round Trip Time (RTT) measurements and on a particle filter. By assuming that nonlinear pulses are transmitted in an Additive White Gaussian Noise Channel, and detected using a threshold based receiver, it is shown that RTT measurements may be affected by a non-Gaussian noise. RTT noise properties are analyzed, and the effects of non-Gaussian noise on the performance of a RTT based positioning system are investigated. To this aim, a classical Least Square, an extended Kalman Filter and a Particle Filter are compared when used to detect a slowly moving target in presence of the modeled noise. It is shown that, in a realistic indoor environment, the Particle Filter solution may be a competitive solution, at a price of increased computational complexity. Experimental verifications validate the presented approach.</p></div></div></div>

scholarly journals Underwater surveillance in non-Gaussian noisy environment

2020 ◽  
Vol 53 (1-2) ◽  
pp. 250-261
Author(s):  
B Omkar Lakshmi Jagan ◽  
S Koteswara Rao
Keyword(s):  
Kalman Filter ◽  
Lower Bound ◽  
Extended Kalman Filter ◽  
Gaussian Noise ◽  
Unscented Kalman Filter ◽  
Doppler Frequency ◽  
Gaussian Mixture ◽  
Noisy Environment ◽  
Sampled Measurements ◽  
Non Gaussian

The aim of this paper is to evaluate the performance of different filtering algorithms in the presence of non-Gaussian noise environment for tracking underwater targets, using Doppler frequency and bearing measurements. The tracking using Doppler frequency and bearing measurements is popularly known as Doppler-bearing tracking. Here the measurements, that is, bearings and Doppler frequency, are considered to be corrupted with two types of non-Gaussian noises namely shot noise and Gaussian mixture noise. The non-Gaussian noise sampled measurements are assumed to be obtained (a) randomly throughout the process and (b) repeatedly at some particular time samples. The efficiency of these filters with the increase in non-Gaussian noise samples is discussed in this paper. The performance of filters is compared with that of Cramer-Rao Lower Bound. Doppler-bearing extended Kalman filter and Doppler-bearing unscented Kalman filter are chosen for this work.

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