scholarly journals The Listsize Capacity of the Gaussian Channel with Decoder Assistance

Entropy ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 29
Author(s):  
Amos Lapidoth ◽  
Yiming Yan
Keyword(s):  
Euclidean Norm ◽  
Channel Noise ◽  
Gaussian Channel ◽  
Zero Rate ◽  
Noise Sequence ◽  
Cutoff Rate

The listsize capacity is computed for the Gaussian channel with a helper that—cognizant of the channel-noise sequence but not of the transmitted message—provides the decoder with a rate-limited description of said sequence. This capacity is shown to equal the sum of the cutoff rate of the Gaussian channel without help and the rate of help. In particular, zero-rate help raises the listsize capacity from zero to the cutoff rate. This is achieved by having the helper provide the decoder with a sufficiently fine quantization of the normalized squared Euclidean norm of the noise sequence.

CMA Blind Multiuser Detector Based on FLOS in Impulse Noise

2011 ◽  
Vol 403-408 ◽  
pp. 187-190
Author(s):  
Xu Xiu Zhang ◽  
Chang An Huang
Keyword(s):  
Gradient Descent ◽  
Stochastic Gradient Descent ◽  
Channel Noise ◽  
Gaussian Channel ◽  
Constant Modulus Algorithm ◽  
Multiuser Detector ◽  
Gradient Descent Algorithm ◽  
Non Gaussian ◽  
Theoretical Analyses ◽  
Special Case

Blind multiuser detector can suppress the MAI(multiple address interference) effectively. Gaussian channel noise is assumed in the traditional methods, but the non-Gaussian channel noise is more realistic. This paper proposes a new CMA(Constant Modulus Algorithm) criterion employing FLOS(fractional lower-order statistic). Theoretical analyses and the computer simulations indicate that the associated FLOS-CMA blind MUD(MultiUser Detection) method,based on a stochastic gradient descent algorithm has a good performance in BER(bit error rate). The traditional MUD algorithm is the special case of this algorithm.

scholarly journals ʅ1-Norm Constrained Minimum Eror Entropy Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 2350-2354
Keyword(s):  
Linear Constraint ◽  
Channel Noise ◽  
Gaussian Channel ◽  
Linear Phase ◽  
Minimum Error ◽  
Phase Condition ◽  
Sparse System Identification ◽  
Non Gaussian ◽  
High Order Statistics ◽  
Entropy Criterion

This work proposes a linear phase sparse minimum error entropy adaptive filtering algorithm. The linear phase condition is obtained by considering symmetry or anti symmetry condition onto the system coefficients. The proposed work integrates linear constraint based on linear phase of the system and -norm for sparseness into minimum error entropy adaptive algorithm. The proposed -norm linear constrained minimum error entropy criterion ( -CMEE) algorithm makes use of high-order statistics, hence worthy for non-Gaussian channel noise. The experimental results obtained for linear phase sparse system identification in the presence of non-Gaussian channel noise reveal that the proposed algorithm has lower steady state error and higher convergence rate than other existing MEE variants.

MAD Robust Fusion with Non-Gaussian Channel Noise

2009 ◽  
Vol E92-A (5) ◽  
pp. 1293-1300 ◽  
Author(s):  
Nga-Viet NGUYEN ◽  
Georgy SHEVLYAKOV ◽  
Vladimir SHIN
Keyword(s):  
Channel Noise ◽  
Gaussian Channel ◽  
Non Gaussian

EFFECT OF NON-GAUSSIAN CHANNEL NOISE ON COHERENCE RESONANCE IN COUPLED STOCHASTIC HODGKIN–HUXLEY NEURONS

2012 ◽  
Vol 11 (02) ◽  
pp. 1250013 ◽  
Author(s):  
YUBING GONG ◽  
LI WANG ◽  
XIU LIN
Keyword(s):  
Information Processing ◽  
Ion Channel ◽  
Gaussian Distribution ◽  
Underlying Mechanism ◽  
Channel Noise ◽  
Gaussian Channel ◽  
Coherence Resonance ◽  
Patch Area ◽  
Neuron Number ◽  
Non Gaussian

In this paper, we study the effect of non-Gaussian channel noise (NCN) on the spiking coherence of a single and an array of bi-directionally coupled Hodgkin–Huxley neurons, mainly investigating how the non-Gaussian character of channel noise affects the coherence resonance (CR) induced by channel noise and by the number of neurons. It is found that, when NCN's deviation q from Gaussian distribution is increased, the CR moves to larger patch area (smaller channel noise) and smaller neuron number, which means that CR occurs in bigger ion channel clusters and smaller neuron number when q is increased. This result shows that, depending on the type of NCN, CR occurs in different sizes of ion channel clusters and numbers of neurons. The underlying mechanism is briefly discussed in terms of the property of NCN. These findings may help to better understand the roles of NCN for improving the time precision of the information processing in coupled stochastic neurons.

scholarly journals Compartive study on performance of the LDPC codes and turbo-codes used in the image transmission in the gaussian and rayleigh channel

2019 ◽  
Vol 17 (2) ◽  
Author(s):  
Fayssal Menezla ◽  
Rachida Meliani ◽  
Zoubir Mahdjoub
Keyword(s):  
Turbo Codes ◽  
Turbo Code ◽  
Digital Signal ◽  
Image Transmission ◽  
Channel Noise ◽  
Gaussian Channel ◽  
Image Correction ◽  
Rayleigh Channel ◽  
Number Of Iterations

The errors induced by channel noise influence on the quality of digital transmission. The latter depends on the probability of errors in the transmitted symbols. To control these errors, a technique of digital signal processing is used, including the information code to be transmitted. As these techniques are used for controlling the transmssion, they are called "channel encoding". In this paper, we introduced and studied two major correction code families' error; LDPC (Low Density Parity Check) and the turbo code. We used our simulation model to evaluate the performance of a Gaussian channel. The results show the effect of iterative LDPC and Turbo code on the transmission and quality of information. Our numerical simulation has shown that the resulting image was corrected gradually as the number of iterations increases for both channels. On the Rayleigh channel, the image correction is obtained for a higher number of iterations over the Gaussian channel.

Sign in / Sign up

Export Citation Format

Share Document