ntvНаучно-технический вестник информационных технологий, механики и оптикиScientific and Technical Journal of Information Technologies, Mechanics and Optics2226-14942500-0373Университет ИТМО10.17586/2226-1494-2022-22-1-206-216ntv-296Research ArticleМАТЕМАТИЧЕСКОЕ И КОМПЬЮТЕРНОЕ МОДЕЛИРОВАНИЕMODELING AND SIMULATIONСравнительный анализ алгоритмов вычислительного интеллекта для оценки канала LTEA comparative analysis of computational intelligence algorithms for estimation of LTE channelshttps://orcid.org/0000-0002-1728-3030ПатанС.PathanS.

Патан Сирадж — исследователь, магистр

Джапур, 303006

Siraj Pathan — MEng, Research Scholar

Jaipur, 303006

sirajpathan404@gmail.comhttps://orcid.org/0000-0002-3110-4908НунияА.NooniaA.

Нуния Аджит — PhD, доцент, доцент

sc 57221779854

Джапур, 303006

Ajit Noonia — PhD, Assistant Professor

sc 57221779854

Jaipur, 303006

ajit009noonia@gmail.comhttps://orcid.org/0000-0002-5891-3713ТамболиМ.TamboliM.

Тамболи Муджиб — PhD, доцент, доцент

Панвел, 410206

Mujib Tamboli — PhD, Assistant Professor

Panvel, 410206

mujibtamboli@yahoo.co.inhttps://orcid.org/0000-0001-7409-9814ПатхакС.PathakS.

Патхак Сунил — PhD, доцент

sc 57194562539

Джапур, 303006

Sunil Pathak — PhD, Associate Professor

sc 57194562539

Jaipur, 303006

sunilpath@gmail.comАмити Университет РаджастанаИндияAmity University RajasthanIndiaТехнический кампус Анджуман-и-Ислам в КалсекареИндияAnjuman-I-Islam’s Kalsekar Technical CampusIndia202217122024221206216Copyright © Патан С., Нуния А., Тамболи М., Патхак С., 20242024Патан С., Нуния А., Тамболи М., Патхак С.Pathan S., Noonia A., Tamboli M., Pathak S.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ntv.elpub.ru/jour/article/view/296

Выполнение моделирования и точной оценки канала беспроводной связи в стандарте Long-Term Evolution (LTE) необходимо для работы многочисленных приложений, таких как потоковое видео, а также для эффективного использования полосы пропускания и энергии. Это связано с постоянным увеличением трафика данных и развитием интернета вещей. Существующие исследования в основном направлены на изучение моделей оценки канала с использованием традиционных алгоритмов вычисления минимальной среднеквадратичной ошибки Minimum Mean Square Error (MMSE) и наименьших квадратов Least Square (LS). Предложенная модель позволяет улучшить оценку канала в мобильных сетях стандарта LTE. Модель основана на объединении методов наименьших квадратов и наименьшей среднеквадратической ошибки с применением Тагучи-генетического алгоритма и алгоритма оптимизации роя частиц Particle Swarm Intelligence (PSO). Приведен пример сети LTE, работающей в диапазоне 5,8 ГГц. Случайные пилотные сигналы следуют вместе с данными для получения сведений о канале, помогают декодировать сигнал в приемнике и оценивать LS и MMSE за счет сочетания Тагучи-генетического алгоритма и PSO соответственно. Выполнена оценка эффективности модели по частоте битовых ошибок Bit Error Rate (BER), отношению сигнал/шум и среднеквадратической ошибки. С учетом величины BER представленная модель на основе искусственного интеллекта обеспечивает лучшие результаты по сравнению с MMSE на 2,4 дБ и с LS – на 5,4 дБ.

Precise modelling and accurate estimation of long-term evolution (LTE) channels are essential for numerous applications like video streaming, efficient use of bandwidth and utilization of power. This deals with the fact that data traffic is increasing continuously with advances in Internet of things. Previous works were focused mainly on designing models to estimate channel using traditional minimum mean square error (MMSE) and least squares (LS) algorithms. The proposed model enhances LTE channel estimation. The designed model combines LS and MMSE methods using Taguchi genetic (GE) and Particle Swarm Intelligence (PSO) algorithms. We consider LTE operating in 5.8 GHz range. Pilot signals are sent randomly along with data to obtain information about the channel. They help to decode a signal in a receiver and estimate LS and MMSE combined with Taguchi GA and PSO, respectively. CI-based model performance was calculated according to the bit error rate (BER), signal-to-noise ratio and mean square error. The proposed model achieved the desired gain of 2.4 dB and 5.4 dB according to BER as compared to MMSE and LS algorithms, respectively

генетический алгоритмGAалгоритм оптимизации роя частицPSOмобильная сеть LTEминимальная среднеквадратическая ошибкаMMSEметод наименьших квадратовLSgenetic algorithm (GA)particle swarm intelligence (PSO)long term evolution (LTE)minimum mean square error (MMSE)least squares (LS)Авторы благодарят Школу инженерии и технологий Университета Амити в Джайпуре, Индия, за предоставление учебных материалов и ресурсов. Также благодарны за предоставление лаборатории для проведения исследований Техническому кампусу Анджуман-и-Ислам в Калсекаре, Университет Мумбаи, Нави Мумбаи, ИндияWe thank Amity School of Engineering & Technology of Amity University, Jaipur, India for providing material and resources for our study, we also thank Anjuman-I-Islam’s Kalsekar Technical Campus of Mumbai University Navi Mumbai, India for providing laboratory to do research.ReferencesBobde S., Phalnikar R. 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The authors declare that there are no conflicts of interest present.