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

TL;DR

This paper explores the use of complex-valued neural networks for channel estimation and equalization in OFDM systems without cyclic prefixes, aiming to improve spectral efficiency and reduce energy consumption.

Contribution

It introduces a CVNN-based approach that outperforms classical algorithms in CP-free OFDM channel estimation, enhancing spectral efficiency.

Findings

CVNN-based method requires less energy per bit.

Outperforms MMSE and LS algorithms without cyclic prefix.

Improves spectral efficiency in OFDM systems.

Abstract

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.