Blind Identification of SFBC-OFDM Signals Based on the Central Limit Theorem

TL;DR

This paper introduces two novel algorithms based on hypothesis testing and support vector machines for blind identification of SFBC-OFDM signals, effectively utilizing space-frequency redundancy and performing well with short observation periods.

Contribution

It proposes two new algorithms that exploit space-frequency domain redundancy using the central limit theorem, improving blind SFBC-OFDM signal identification without requiring channel or noise knowledge.

Findings

Both algorithms outperform traditional methods in short observation scenarios.

The SVM-based method does not need timing synchronization.

Algorithms maintain good performance under transmission impairments.

Abstract

Previous approaches for blind identification of space-frequency block codes (SFBC) do not perform well for short observation periods due to their inefficient utilization of frequency-domain redundancy. This paper proposes a hypothesis test (HT)-based algorithm and a support vector machine (SVM)-based algorithm for SFBC signals identification over frequency-selective fading channels to exploit two-dimensional space-frequency domain redundancy. Based on the central limit theorem, space-domain redundancy is exploited to construct the cross-correlation function of the estimator and frequency-domain redundancy is incorporated in the construction of the statistics. The difference between the two proposed algorithms is that the HT-based algorithm constructs a chi-square statistic and employs an HT to make the decision, while the SVM-based algorithm constructs a non-central chi-square statistic with unknown mean as a strongly-distinguishable statistical feature and uses an SVM to make the decision. Both algorithms do not require knowledge of the channel coefficients, modulation type or noise power, and the SVM-based algorithm does not require timing synchronization. Simulation results verify the superior performance of the proposed algorithms for short observation periods with comparable computational complexity to conventional algorithms, as well as their acceptable identification performance in the presence of transmission impairments.