10 research outputs found

    GWO-BP neural network based OP performance prediction for mobile multiuser communication networks

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    The complexity and variability of wireless channels makes reliable mobile multiuser communications challenging. As a consequence, research on mobile multiuser communication networks has increased significantly in recent years. The outage probability (OP) is commonly employed to evaluate the performance of these networks. In this paper, exact closed-form OP expressions are derived and an OP prediction algorithm is presented. Monte-Carlo simulation is used to evaluate the OP performance and verify the analysis. Then, a grey wolf optimization back-propagation (GWO-BP) neural network based OP performance prediction algorithm is proposed. Theoretical results are used to generate training data. We also examine the extreme learning machine (ELM), locally weighted linear regression (LWLR), support vector machine (SVM), BP neural network, and wavelet neural network methods. Compared to the wavelet neural network, LWLR, SVM, BP, and ELM methods, the results obtained show that the GWO-BP method provides the best OP performance prediction

    Performance Analysis of Energy Harvesting-Assisted Overlay Cognitive NOMA Systems With Incremental Relaying

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    In this paper, we analyze the performance of an energy harvesting (EH)-assisted overlay cognitive non-orthogonal multiple access (NOMA) system. The underlying system consists of a primary transmitter-receiver pair accompanied by an energy-constrained secondary transmitter (ST) with its intended receiver. Accordingly, ST employs a time switching (TS) based receiver architecture to harvest energy from radio-frequency signals of the primary transmissions, and thereby uses this energy to relay the primary information and to transmit its own information simultaneously using the NOMA principle. For this, we propose two cooperative spectrum sharing (CSS) schemes based on incremental relaying (IR) protocol using amplify-and-forward (AF) and decode-and-forward (DF) strategies, viz., CSS-IAF and CSS-IDF, and compare their performance with the competitive fixed relaying based schemes. The proposed IR-based schemes adeptly avail the degrees-of-freedom to boost the system performance. Thereby, considering the realistic assumption of the NOMA-based imperfect successive interference cancellation, we derive the expressions of outage probability for the primary and secondary networks under both CSS-IAF and CSS-IDF schemes subject to the Nakagami-m fading. In addition, we quantify the throughput and energy efficiency for the considered system. The obtained theoretical findings are finally validated through numerous analytical and simulation results to reveal the advantages of the proposed CSS schemes over the baseline direct link transmission and orthogonal multiple access schemes. © 2020 IEEE

    Probabilidade de detecção em AOA convencional usando arranjos ULA e URA

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    Orientador: Gustavo FraidenraichDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Em sistemas de MIMO massivo, onde um grande número de antenas é usado, a dependência do espectro de energia na direção do sinal permite estimar o AoA com uma precisão muito alta. Neste trabalho, derivamos a probabilidade de detectar corretamente o ângulo de chegada usando o beamformer convencional. A derivação é apresentada de uma maneira exata para as geometrias ULA e URA. As expressões resultantes dependem da potência do ruído, a linha de visada (modelada como a média de desvanecimento), variância de desvanecimento, número de amostras do sinal, número de ângulos de orientação e o número de antenas. Avaliamos numericamente a probabilidade de detecção simulada e analítica e elas mostraram uma concordância perfeita para cenários distintos enquadrados na condição de MIMO massivoAbstract: In Massive Multiple Input Multiple Output (MIMO) systems, where a large number of antennas is used, the dependency of the power spectrum on the signal direction allows Angle of Arrival (AoA) estimation with a very high precision. In this work, we derive the probability to correctly detect the angle of arrival using the conventional beamformer. The derivation is presented in an exact manner for Uniform Linear Array (ULA) and Uniform Rectangular Array (URA) geometries. The resulting expressions depend on the noise power, Line of Sight Component (LoS) (modeled as the fading mean), fading variance, number of signal snapshots, number of steering angles, and the number of antennas. We have numerically evaluated the simulated and analytical probability of detection and they have shown a perfect agreement for distinct scenarios framed in the Massive MIMO conditionMestradoTelecomunicações e TelemáticaMestre em Engenharia ElétricaCAPE
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