CVNN-based Channel Estimation and Equalization in OFDM Systems Without Cyclic Prefix

XLI Simp\'osio Brasileiro de Telecomunica\c{c}\~oes e Processamento de Sinais (SBrT 2023) In modern communication systems operating with Orthogonal Frequency-Division Multiplexing (OFDM), channel estimation requires minimal complexity with one-tap equalizers. However, this depends on cyclic pre...

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Bibliographic Details
Main Authors:	Sousa, Heitor dos Santos, Soares, Jonathan Aguiar, Mayer, Kayol Soares, Arantes, Dalton Soares
Format:	Journal Article
Language:	English
Published:	25-08-2023
Subjects:	Computer Science - Information Theory Mathematics - Information Theory
Online Access:	Get full text
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Summary:	XLI Simp\'osio Brasileiro de Telecomunica\c{c}\~oes e Processamento de Sinais (SBrT 2023) In modern communication systems operating with Orthogonal Frequency-Division Multiplexing (OFDM), channel estimation requires minimal complexity with one-tap equalizers. However, this depends on cyclic prefixes, which must be sufficiently large to cover the channel impulse response. Conversely, the use of cyclic prefix (CP) decreases the useful information that can be conveyed in an OFDM frame, thereby degrading the spectral efficiency of the system. In this context, we study the impact of CPs on channel estimation with complex-valued neural networks (CVNNs). We show that the phase-transmittance radial basis function neural network offers superior results, in terms of required energy per bit, compared to classical minimum mean-squared error and least squares algorithms in scenarios without CP.
DOI:	10.48550/arxiv.2308.13623