30.4 A 32Gb/s 2.9pJ/b Transceiver for Sequence-Coded PAM-4 Signalling with 4-to-6dB SNR Gain in 28nm FDSOI CMOS

2019 ◽  
Author(s):  
Aurangozeb ◽  
Carson Dick ◽  
Maruf Mohammad ◽  
Masum Hossain
Keyword(s):  
Snr Gain

scholarly journals Guard Band-Based Resource Sharing for Device-to-Device Communications Underlying Cellular Network

2016 ◽  
Vol 2016 ◽  
pp. 1-8
Author(s):  
Yue Li ◽  
Xuejun Sha ◽  
Liang Ye ◽  
Xiaojie Fang
Keyword(s):  
Outage Probability ◽  
Resource Sharing ◽  
Cellular Network ◽  
Small Area ◽  
Low Energy ◽  
Energy Characteristics ◽  
Guard Band ◽  
Sharing Strategy ◽  
Device To Device ◽  
Snr Gain

The performance of device-to-device (D2D) communication in a cellular network depends on the resource sharing between D2D links and cellular users. Existing researches on resource sharing mainly focus on power control between the D2D users and cellular users that operate in the same frequency band. However, the D2D outage probability performance is hampered by the cellular interference to D2D links. Therefore, the D2D users may not achieve satisfactory SINR performance when D2D users and cellular users are geographically located in a small area; as a result, the outage probability performance would be significantly degraded. In this paper, we provide a novel resource sharing strategy to mitigate the interference from cellular users to D2D receivers by utilizing the low energy characteristics of signals in the guard band and analyze the D2D outage probability performance mathematically. Both the mathematical analysis and numerical results show that the proposed resource sharing strategy provides 1.2 dB SNR gain in D2D outage probability performance while guaranteeing the cellular throughputs.

SIGNAL-TO-NOISE RATIO GAIN IN NON-DYNAMICAL AND DYNAMICAL BISTABLE STOCHASTIC RESONATORS

2002 ◽  
Vol 02 (03) ◽  
pp. L147-L155 ◽  
Author(s):  
PETER MAKRA ◽  
ZOLTAN GINGL ◽  
LASZLO B. KISH
Keyword(s):  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
Schmitt Trigger ◽  
Total Power ◽  
Signal To Noise ◽  
Simulation Techniques ◽  
Duty Cycles ◽  
Signal Simulation ◽  
Noise Ratio ◽  
Snr Gain

It has recently been reported that in some systems showing stochastic resonance, the signal-to-noise ratio (SNR) at the output can significantly exceed that at the input; in other words, SNR gain is possible. We took two such systems, the non-dynamical Schmitt trigger and the dynamical double wellpotential, and using numerical and mixed-signal simulation techniques, we examined what SNR gains these systems can provide. In the non-linear response limit, we obtained SNR gains much greater than unity for both systems. In addition to the classical narrow-band SNR definition, we also measured the ratio of the total power of the signal to the power of the noise part, and it showed even better signal improvement. Here we present a brief review of our results, and scrutinise, for both the Schmitt-trigger and the double well potential, the behaviour of the SNR gain by stochastic resonance for different signal amplitudes and duty cycles. We also discuss the mechanism of providing gains greater than unity.

UPPER BOUND ON SNR GAIN IN WAVELET TRANSFORM PREDICTIVE-ENTROPY IMAGE CODING

1998 ◽  
Vol 08 (02) ◽  
pp. 267-272
Author(s):  
DRAGORAD MILOVANOVIĆ ◽  
ZORAN BOJKOVIĆ ◽  
ANDREJA SAMČOVIĆ
Keyword(s):  
Wavelet Transform ◽  
Image Coding ◽  
Upper Bound ◽  
Rate Distortion ◽  
Upper Bounds ◽  
Discrete Wavelet ◽  
Theory Approach ◽  
Still Image ◽  
Rate Distortion Theory ◽  
Snr Gain

Using the rate-distortion theory approach and assuming the Laplacian probability density function of the quantizer input signal, we propose a function which gives the signal-to-quantizing-noise ratio (SNR) gain in discrete wavelet transform predictive-entropy coding over fullband predictive image coding. The upper bound on SNR gain is determined as a function of the subband number only. The practical SNR performances of realizable wavelet still image coders are compared with their theoretical bounds. The computer simulation results of wavelet based predictive coded test images show that the SNR gain grows faster with the subband number increment than its theoretical upper bounds do.

scholarly journals Deep learning based digital backpropagation demonstrating SNR gain at low complexity in a 1200 km transmission link

2020 ◽  
Vol 28 (20) ◽  
pp. 29318 ◽  
Author(s):  
Bertold Ian Bitachon ◽  
Amirhossein Ghazisaeidi ◽  
Marco Eppenberger ◽  
Benedikt Baeuerle ◽  
Masafumi Ayata ◽  
...  
Keyword(s):  
Deep Learning ◽  
Low Complexity ◽  
Transmission Link ◽  
Snr Gain

Noise-enhanced SNR gain in parallel array of bistable oscillators

2006 ◽  
Vol 42 (17) ◽  
pp. 1008 ◽  
Author(s):  
F. Duan ◽  
F. Chapeau-Blondeau ◽  
D. Abbott
Keyword(s):  
Parallel Array ◽  
Snr Gain

Deep learning based digital backpropagation enabling SNR gain at low complexity

2021 ◽  
Author(s):  
Bertold Ian Bitachon ◽  
Amirhossein Ghazisaeidi ◽  
Marco Eppenberger ◽  
Benedikt Baeuerle ◽  
Masafumi Ayata ◽  
...  
Keyword(s):  
Deep Learning ◽  
Low Complexity ◽  
Snr Gain

High SNR Gain by Stochastic Resonance in a Tristable System

2013 ◽  
Vol 11 (12) ◽  
Author(s):  
Pin Wan ◽  
Yi-Ju Zhan ◽  
Hui Zheng ◽  
Luo-Ya Zeng ◽  
Qin Deng
Keyword(s):  
Stochastic Resonance ◽  
Snr Gain

scholarly journals Coverage, Probability of SNR Gain, and DOR Analysis of RIS-Aided Communication Systems

2020 ◽  
Vol 9 (8) ◽  
pp. 1268-1272 ◽  
Author(s):  
Liang Yang ◽  
Yin Yang ◽  
Mazen O. Hasna ◽  
Mohamed-Slim Alouini
Keyword(s):  
Coverage Probability ◽  
Communication Systems ◽  
Snr Gain ◽  
Aided Communication

Study of Signal-Noise Separation Based on the Fuzzy Neural Network

2014 ◽  
Vol 687-691 ◽  
pp. 3822-3827
Author(s):  
Bin Liang ◽  
Yan Ping Bai
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Signal To Noise Ratio ◽  
Reference Signal ◽  
Signal Noise ◽  
Evaluation Indexes ◽  
Low Snr ◽  
Fuzzy Neural ◽  
S Model ◽  
Snr Gain

This paper introduces the basic mathematical model of fuzzy neural network and T-S model. It uses the fuzzy neural network for targeted emulational signal-noise separation and presents evaluation indexes of the fuzzy neural network’s denoising effect, analyzes its mean square error (MSE), signal-to-noise ratio (SNR), SNR gain, and similarity of signal and theoretical reference signal after denoising. The simulation results show that this algorithm has prominent effect of separation under high and low SNR environment. At last, the experiments for the second lake of Fenhe also validated the superiority and effectiveness of this algorithm.

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