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




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Institute of Electrical Electronics Engineers Inc


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. In this paper, we develop a joint blind identification algorithm to determine the number of transmit antennas and MIMO schemes 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. The 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.


This work was supported in part by the National Natural Science Foundation of China under Grant 61771365, in part by the Natural Science Foundation of Shaanxi Province under Grant 2017JZ022, in part by the 111 Project under Grant B08038, in part by the National Key Research and Development Program of China under Grant 2016YFB1200202,
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Orthogonal frequency division multiplexing, MIMO systems, Transmitting antennas, Antennas (Electronics), Antenna arrays, Redundancy (Engineering), Wireless communication systems, Neural networks (Computer science), Orthogonal frequency division multiplexing, Signal detection


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