Direct Reconstruction of Saturated Samples in Band-Limited OFDM Signals

TL;DR

This paper introduces a direct, non-iterative algorithm for reconstructing saturated samples in band-limited OFDM signals, improving decoding accuracy in high-throughput digital communications.

Contribution

It presents a parameter-free, sinc-based reconstruction method that efficiently cancels saturation effects in OFDM signals, aiding in reducing errors in digital communication systems.

Findings

Significantly reduces transmission errors in simulations.

Effective in high peak-to-average power ratio scenarios.

Non-iterative approach suitable for hardware implementation.

Abstract

Given a set of samples, a few of them being possibly saturated, we propose an efficient algorithm in order to cancel saturation while reconstructing band-limited signals. Our method satisfies a minimum-loss constraint and relies on sinc-related bases. It involves matrix inversion and is a direct, non-iterative approach. It consists of two main steps: (i) regression, to estimate the expansion coefficients of the signal model; (ii) interpolation, to restore an estimated value for those samples that are saturated. Because the proposed method is free from tuning parameters, it is hardware-friendly and we expect that it will be particularly useful in the context of orthogonal frequency-division multiplexing. There, the high peak-to-average power ratio of the transmitted signal results in a challenging decoding stage in the presence of saturation, which causes significant decoding errors due to the nonlinearity of amplifiers and receivers, ultimately resulting in band distortion and information loss. Our experiments on realistic simulations confirm that our proposed reconstruction of the saturated samples can significantly reduce transmission errors in modern high-throughput digital-communication receivers.