The DCT Neuron for Estimation and Compensation of Amplitude Distortions in OFDM Systems

TL;DR

This paper introduces the DCT Neuron, a novel adaptive processor for efficiently identifying and compensating amplitude distortions in OFDM systems, enabling real-time adaptive correction.

Contribution

The core novelty is the DCT Neuron, a compact adaptive processor based on DCT, for nonlinear channel response estimation directly in the time domain.

Findings

Achieves reliable identification with as few as two OFDM symbols.

Enables low-complexity, real-time adaptive compensation.

Leverages properties of DCT for efficient nonlinear response characterization.

Abstract

We present a receiver-side framework for identifying amplitude distortions in frequency-selective OFDM channels. The core novelty is the use of the DCT Neuron, a compact adaptive processor based on the discrete cosine transform (DCT), to characterize the channel's nonlinear response, leveraging its properties for highly efficient estimation. Operating directly in the time domain, the method builds an accurate signal model and tracks channel variations adaptively, achieving reliable identification with as few as two OFDM symbols. The learned nonlinear response can then be exploited for predistortion and iterative decoding, enabling low-complexity, real-time adaptive compensation of complex responses in multicarrier systems.