Joint Blind Identification of the Number of Transmit Antennas and MIMO Schemes Using Gerschgorin Radii and FNN
arXiv:1803.10849 · doi:10.1109/TWC.2018.2879941
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.
References in corpus (5)
- Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensors
- Identification of SM-OFDM and AL-OFDM Signals Based on Their Second-Order Cyclostationarity
- Blind Identification of SM and Alamouti STBC-OFDM Signals
- Blind Identification of SFBC-OFDM Signals Using Subspace Decompositions and Random Matrix Theory
- Blind Identification of SFBC-OFDM Signals Based on the Central Limit Theorem