Complex-Valued Phase Transmittance RBF Neural Networks for Massive MIMO-OFDM Receivers

Soares, Jonathan Aguiar; Mayer, Kayol Soares; Castro, Fernando César Comparsi de; Arantes, Dalton Soares

doi:10.3390/s21248200

Cited by 9 publications

(3 citation statements)

References 68 publications

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“…Nevertheless, due to the relaxation of the code orthogonality requirement, decoding complexity increases concerning that of orthogonal codes because only pair-wise decoding is possible. Specifically, decoding by QOSTBC encoded signals is reduced and, to minimize the cost function over [44], [45], and [46], the information is recovered by the QOSTBC system, the binary, using a MIMO-SOSA-N algorithm that is proposed in this work.…”

Section: 2-stbc From Quasi-orthogonal Design (Qostbc)mentioning

confidence: 99%

The Contribution of Artificial Intelligence in Blind Equalization Using SOSA-MIMO Algorithm with QOSTBC Coding

Abderrazak,

Abdessalam

2023

Preprint

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The recent evolution in communications forces us to rely on AI to improve the quality of communications. Artificial intelligence has become one of the necessities in the development of scientific life. In this article, we integrate artificial intelligence into blind equalization using the coding technique QOSTBC with Multi-input Multi-output (MIMO) transmission. The Multi-input Multi-output (MIMO) transmission and the blind equalizer schemes by using the QOSTBC coding have become the techniques of choice for increasing spectral efficiency in bandwidth-congested areas are hit in demand nowadays, especially in mobile applications, where devices with size, weight, and power constraints are common. In this paper, we propose a new blind equalizer using the second-order statistical algorithm in a MIMO system with QOSTBC coding used. QOSTBCs with MIMO-SOSA are used to enhance the energy efficiency of the communication system with 8; 4 and 2 antennas. We obtain the optimal performance that ensures full diversity and maximizes the QOSTBC minimum coding gain distance. Simulation results are presented for MIMO-SOSA under 5g wireless Rayleigh channels so that a fair performance comparison with other reference techniques can be established. The results show that by using MIMO-SOSA along with a coding QOSTBC and with diversity in the fading channel and thus low BER at high SNR can be ensured. More importantly, it is also shown that QOSTBC using MIMO-SOSA achieves a better error performance than those using conventional modulation format.

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Section: 2-stbc From Quasi-orthogonal Design (Qostbc)mentioning

confidence: 99%

The Contribution of Artificial Intelligence in Blind Equalization Using SOSA-MIMO Algorithm with QOSTBC Coding

Abderrazak,

Abdessalam

2023

Preprint

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“…With the ever-increasing demand for wireless network capacity, multiple-input multiple-output (MIMO) communications have become essential in novel technologies to increase spectral efficiency and, consequently, network throughput [1]. Among the MIMO technologies, space-time block coding (STBC) is fundamental to providing space diversity by supplying multiple independently faded replicas of the same information symbol, increasing communication reliability [2].…”

Section: Introductionmentioning

confidence: 99%

PCA-based Channel Estimation for MIMO Communications

Soares¹,

Mayer²,

Valadares³

et al. 2022

Anais Do XL Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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In multiple-input multiple-output communications, channel estimation is paramount to keep base stations and users on track. This paper proposes a novel PCA-based -principal component analysis -channel estimation approach for MIMO orthogonal frequency division multiplexing systems. The channel frequency response is firstly estimated with the least squares method, and then PCA is used to filter only the higher singular components of the channel impulse response, which is then converted back to frequency domain. The proposed approach is compared with the MMSE, the minimum mean square error estimation, in terms of bit error rate versus E b /N0.

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“…In [14], Soares et al present a novel complex-valued radial basis function (RBF) neural network architecture for channel estimation and symbol detection in massive MIMO-OFDM communication systems. They propose a MIMO phase transmittance RBF neural network (MIMO-PTRBF) to implement massive MIMO schemes as an alternative to the classic MIMO-OSTBC systems under maximum likelihood detection and show that it is more computationally efficient with respect to the optimal maximum likelihood decoding.…”

mentioning

confidence: 99%