Joint Blind Identification of the Number of Transmit Antennas and MIMO Schemes Using Gerschgorin Radii and FNN

TL;DR

This paper presents a joint blind identification method for determining the number of transmit antennas and MIMO schemes simultaneously, using Gerschgorin radii and neural networks, applicable to both single-carrier and OFDM systems.

Contribution

The proposed approach uniquely combines subspace-rank features, Gerschgorin radii, and neural networks for joint identification, including new MIMO schemes and short observation periods.

Findings

Effective identification in both single-carrier and OFDM systems

Good performance with short observation periods

Acceptable computational complexity

Abstract

Blind enumeration of the number of transmit antennas and blind identification of multiple-input multiple-output (MIMO) schemes are two pivotal steps in MIMO signal identification for both military and commercial applications. Conventional approaches treat them as two independent problems, namely the source number enumeration and the presence detection of space-time redundancy, respectively. In this paper, we develop a joint blind identification algorithm to determine the number of transmit antennas and MIMO scheme simultaneously. By restructuring the received signals, we derive three subspace-rank features based on the signal subspace-rank to determine the number of transmit antennas and identify space-time redundancy. Then, a Gerschgorin radii-based method and a feed-forward neural network are employed to calculate these three features, and a minimal weighted norm-1 distance metric is utilized for decision making. In particular, our approach can identify additional MIMO schemes, which most previous works have not considered, and is compatible with both single-carrier and orthogonal frequency division multiplexing (OFDM) systems. Simulation results verify the viability of our proposed approach for single-carrier and OFDM systems and demonstrate its favorable identification performance for a short observation period with acceptable complexity.