Detection of Number of Subcarriers of OFDM Systems using Eigen-Spectral Analysis

TL;DR

This paper introduces a novel eigen-spectral analysis method for accurately estimating the number of OFDM subcarriers in non-cooperative scenarios, useful for cognitive radio applications, especially at low SNR.

Contribution

It presents a new eigen-spectral based approach that can detect any number of subcarriers regardless of modulation scheme, outperforming existing methods.

Findings

Accurately detects subcarriers at low SNR

Works for arbitrary number of subcarriers

Independent of modulation scheme

Abstract

Orthogonal Frequency-Division Multiplexing (OFDM) is widely used in modern wireless communication systems due to its robustness against time-dispersive channels. In this work, we consider a non-cooperative scenario where the receiver does not have prior knowledge of the OFDM parameters such as the number of subcarriers and the aim is to estimate them using the received data. Such a setup has applications in cognitive radio networks. For this blind OFDM parameter estimation problem, we provide a novel method based on eigen-spectral analysis of the covariance matrix corresponding to the received data. In particular, we show that the covariance matrix exhibits a distinctive rank property under correct segmentation of the received symbols, reflecting a characteristic behavior in its eigenvalue spectrum that facilitates accurate estimation of the number of subcarriers. The proposed method is more general than existing approaches in the literature, as it can detect an arbitrary number of subcarriers and its performance remains independent of the modulation scheme. The numerical results show that the proposed method accurately detects the number of subcarriers with high probability even at low SNR.