scholarly journals Satsisfiability and Systematicity

2015 ◽  
Vol 53 ◽  
pp. 497-540
Author(s):  
Matthew L Ginsberg
Keyword(s):  
Satisfiability Algorithms ◽  
Memory Polynomial

We introduce a new notion of systematicity for satisfiability algorithms with restarts, saying that an algorithm is strongly systematic if it is systematic independent of restart policy but weakly systematic if it is systematic for some restart policies but not others. We show that existing satisfiability engines are generally only weakly systematic, and describe FLEX, a strongly systematic algorithm that uses an amount of memory polynomial in the size of the problem. On large number factoring problems, FLEX appears to outperform weakly systematic approaches.

scholarly journals Neural Network DPD for Aggrandizing SM-VCSEL-SSMF-Based Radio over Fiber Link Performance

Photonics ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 19
Author(s):  
Muhammad Hadi ◽  
Muhammad Awais ◽  
Mohsin Raza ◽  
Kiran Khurshid ◽  
Hyun Jung
Keyword(s):  
Neural Network ◽  
Single Mode ◽  
Single Mode Fiber ◽  
Radio Over Fiber ◽  
Error Vector Magnitude ◽  
Computational Overhead ◽  
Link Performance ◽  
Modulation Signal ◽  
Experimental Findings ◽  
Memory Polynomial

This paper demonstrates an unprecedented novel neural network (NN)-based digital predistortion (DPD) solution to overcome the signal impairments and nonlinearities in Analog Optical fronthauls using radio over fiber (RoF) systems. DPD is realized with Volterra-based procedures that utilize indirect learning architecture (ILA) and direct learning architecture (DLA) that becomes quite complex. The proposed method using NNs evades issues associated with ILA and utilizes an NN to first model the RoF link and then trains an NN-based predistorter by backpropagating through the RoF NN model. Furthermore, the experimental evaluation is carried out for Long Term Evolution 20 MHz 256 quadraturre amplitude modulation (QAM) modulation signal using an 850 nm Single Mode VCSEL and Standard Single Mode Fiber to establish a comparison between the NN-based RoF link and Volterra-based Memory Polynomial and Generalized Memory Polynomial using ILA. The efficacy of the DPD is examined by reporting the Adjacent Channel Power Ratio and Error Vector Magnitude. The experimental findings imply that NN-DPD convincingly learns the RoF nonlinearities which may not suit a Volterra-based model, and hence may offer a favorable trade-off in terms of computational overhead and DPD performance.

Research on Modeling Methods of Power Amplifier with Memory Effect in Broadband Communication

2014 ◽  
Vol 687-691 ◽  
pp. 4060-4063
Author(s):  
Lu Sun ◽  
Long Long Xue ◽  
Jia Li Wang ◽  
Chun Yang Zhou
Keyword(s):  
Memory Effect ◽  
Power Amplifier ◽  
Polynomial Model ◽  
Behavioral Modeling ◽  
Design System ◽  
Behavioral Models ◽  
Electric Characteristic ◽  
Memory Polynomial ◽  
With Memory ◽  
Frequency Power

Memory effect of power amplifier (PA) due to broadband signals which adopt new modulation technique is becoming obvious, and worsening the linearity of PA. Behavioral modeling for RF (Radio Frequency) power amplifier is indispensable to design digital pre-distortion system by which we can promote the efficiency of PA. According to the simulation data in the software of ADS (Advanced Design System), behavioral models of MRF21085 amplifier can be established by MATLAB. Comparison of memory polynomial model and ADS simulation of MRF21085 amplifier demonstrate memory polynomial model can represent the electric characteristic of it exactly.

Sign in / Sign up

Export Citation Format

Share Document