Reduction of Inter-Symbol Interference Using Artifical Neural Network System in Multicarrier OFDM System

Makka, Jyoti; Monga, Himanshu; Baghla, Silki

doi:10.5815/ijwmt.2018.05.02

Cited by 7 publications

(3 citation statements)

References 3 publications

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“…There existed some AI methods applied to communication systems for dealing with multipath propagation. An ISI reduction scheme based on multilayer neuron network and back‐propagation (BP) algorithm in orthogonal frequency division multiplexing system was proposed [14], which achieved a better performance. Neural network decoder based on multilayer neural network [15] and radial basis function [16] were proposed for decoding the information that did not need complicated theoretical decoding threshold.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence enhances the performance of chaotic baseband wireless communication

et al. 2021

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It was reported recently that chaos properties could be used to relieve inter-symbol interference caused by multipath propagation in chaos-based wireless communication system. Although there exists the optimal decoding threshold to theoretically eliminate the intersymbol interference, its practical implementation is still a challenge due to the strong requirement to know the future symbols to be transmitted. To tackle this almost 'impossible' task, convolutional neural network with deep learning structure is proposed to predict future symbols based on the received signal, to further reduce inter-symbol interference and to obtain a better bit error rate performance. Due to the short time predictability of chaotic signal, the proposed method is able to predict short-term future symbols and get a better threshold suitable for the time-variant channel. The analytical bit error rate of the proposed method is derived. The contributions of the paper are as follows: firstly, a convolutional neural network with deep learning structure is proposed for the first time to predict the future symbols in the chaos baseband wireless communication system, which does not require much training in this important application; secondly, the future bits predicted by the trained convolutional neural network are used together with the past decoded bits to calculate more accurate decoding threshold compared with the existing methods, yielding a better bit error rate performance. Numerical simulations and experimental results validate the effectiveness of our theory and the superiority of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Artificial intelligence enhances the performance of chaotic baseband wireless communication

et al. 2021

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“…As a result, solving this problem is extremely difficult, and it becomes increasingly more difficult as the issue size grows. In the last several years, researchers use different timetabling methods using population-based methods, constraint-based methods (ant colony optimization [5], genetic algorithms [6], [7] and memetic algorithms [8]), the metaheuristic methods (simulated annealing [9], tabu search [10], and great deluge [11]), variable neighborhood search, and hybrid and hyper-hybrid approaches, etc. As a result of this diversity in the use of algorithms, further consideration should be given to the UCTP study.…”

Section: Introductionmentioning

confidence: 99%

Timetabling problem solving based on best-nests cuckoo search

Jebur

Abdullah

2021

Bulletin EEI

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The university courses timetabling problem (UCTP) is a popular subject among institutions and academics because occurs every academic year. In general, UCTP is the distribution of events through slots time for each room based on the list of constraints for instance (hard constraint and soft constraint) supplied in one semester, intending to avoid conflicts in such assignments. Under no circumstances should hard constraints be broken while attempting to fulfill as many soft constraints as feasible. this article presented a modified best-nests cuckoo search (BNCS) algorithm depend on the base cuckoo search (CS) algorithm. BNSC algorithm was achieved by dividing the nests into two groups (best-nests and normal-nests). The BNCS algorithm selection was limited to the best-nests to generate new solutions. The comparison between BNCS and basic CS based on the experimental result is achieved. For performance evaluation, the BNCS has been tested on four variant-size datasets. It was observed that the BNCS has performed high performance and is faster at finding a solution from CS.

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“…Over the past 40 years, researchers have been using different time-tabling methods using constraint-based methods, population-based methods (eg, genetic algorithms [3], [4], ant colony optimization [5], and memetic algorithms [6]), the metaheuristic methods (eg, tabu search [7], simulated annealing [8], and great deluge [9]), variable neighbourhood search (VNS), and hybrid and hyper-hybrid approaches, etc., have been proposed. In this paper a new hybrid approach based on parallel genetic algorithm and local search is utilized in solving university course timetabling problem.…”

Section: Introductionmentioning

confidence: 99%

Solving University Course Timetabling Problem Using Parallel Genetic Algorithm

Rezaeipanah,

Abshirini,

Zade

2019

IJSRCSE

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Scheduling is one of the problems which so many researches have been conducted on it over the years. The university course timetabling problem (UCTP) which is an NP-Hard problem is a type of scheduling problem. The allocation of whole of events in timeslots and rooms performs by the university course timetabling process considering the list of hard and soft constraints presented in one semester, so that no conflict is created in such allocations. In general, it means assigning predefined courses to certain rooms and timeslots under specific constraints. In this paper we establish a hybrid algorithm based on Parallel Genetic Algorithm and Local Search to solve course timetabling problem (PGALS). This combines a direct representation of the timetable with heuristic crossover operators to ensure that the most fundamental constraints are never violated. We see how the algorithm is guaranteed to always produce a feasible solution by hard coding constraints which must not be broken. The proposed algorithm has been applied and evaluated against the latest methodologies in the literature with respect to standard benchmark problems. We demonstrate that the proposed algorithm produces some of the best-known results when tested on BenPaechter competition datasets.

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Reduction of Inter-Symbol Interference Using Artifical Neural Network System in Multicarrier OFDM System

Cited by 7 publications

References 3 publications

Artificial intelligence enhances the performance of chaotic baseband wireless communication

Artificial intelligence enhances the performance of chaotic baseband wireless communication

Timetabling problem solving based on best-nests cuckoo search

Solving University Course Timetabling Problem Using Parallel Genetic Algorithm

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