Blind Identification of SFBC-OFDM Signals Using Subspace Decompositions and Random Matrix Theory

TL;DR

This paper introduces a novel blind identification algorithm for SFBC-OFDM signals that leverages subspace analysis and random matrix theory, enabling detection without prior knowledge of signal parameters.

Contribution

The paper presents a new algorithm for identifying SFBCs using subspace features and hypothesis testing, applicable to various SFBC schemes without prior parameter knowledge.

Findings

Effective identification with reduced observation period

No need for prior knowledge of signal parameters

Acceptable computational complexity

Abstract

Blind signal identification has important applications in both civilian and military communications. Previous investigations on blind identification of space-frequency block codes (SFBCs) only considered identifying Alamouti and spatial multiplexing transmission schemes. In this paper, we propose a novel algorithm to identify SFBCs by analyzing discriminating features for different SFBCs, calculated by separating the signal subspace and noise subspace of the received signals at different adjacent OFDM subcarriers. Relying on random matrix theory, this algorithm utilizes a serial hypothesis test to determine the decision boundary according to the maximum eigenvalue in the noise subspace. Then, a decision tree of a special distance metric is employed for decision making. The proposed algorithm does not require prior knowledge of the signal parameters such as the number of transmit antennas, channel coefficients, modulation mode and noise power. Simulation results verify the viability of the proposed algorithm for a reduced observation period with an acceptable computational complexity.