Abstract
Power amplifier (PA) nonlinearity is typically unique at the radio frequency (RF) front-end for particular emitters. It can play a crucial role in the application of specific emitter identification (SEI). In this paper, under the Multi-Input Multi-Output (MIMO) multipath communication scenario, two data-aided approaches are proposed to identify multi-antenna emitters using PA nonlinearity. Built upon a memoryless polynomial model, the first approach formulates a linear least square (LLS) problem and presents the closed-form solution of nonlinear coefficients in a MIMO system by means of singular value decomposition (SVD) operation. Another alternative approach estimates nonlinear coefficients of each individual PA through nonlinear least square (NLS) solved by the regularized Gauss–Newton iterative scheme. Moreover, there are some practical discussions of our proposed approaches about the mismatch of the order of PA model and the rank-deficient condition. Finally, the average misclassification rate is derived based on the minimum error probability (MEP) criterion, and the proposed approaches are validated to be effective through extensively numerical simulations.
Highlights
Specific emitter identification (SEI) is committed to distinguish individual radiation sources by using essential radio frequency fingerprint (RFF) features extracted from different emitters
We extend the method in [27] to the Multi-Input Multi-Output (MIMO) multipath scenario; a closed-form solution of the nonlinear coefficients is obtained by combining the linear least square (LLS) and singular value decomposition (SVD)
We give comparisons among the Modified Liu Algorithm (MLA), linear method in MIMO (LMM), and nonlinear least square (NLS) in Figures 1 and 2, where the third-order and fifth-order coefficients are combined as a classification feature, from which one can see that the performance of the LMM and NLS are apparently better than that of MLA, especially at low signal to noise ratio (SNR) regime
Summary
Specific emitter identification (SEI) is committed to distinguish individual radiation sources by using essential radio frequency fingerprint (RFF) features extracted from different emitters. In [25,26], an estimation of signal parameters via rotational invariance technique (ESPRIT)-based approach, which takes advantage of the multiple antennas at the receiver to separate the RFF from wireless channel, is proposed for RFF estimation in orthogonal frequency division multiplexing (OFDM) systems, whereas it is only suitable for a Single-Input Multiple-Output (SIMO) system rather than the MIMO one. Treating the fact that all PAs of a multiple-antenna emitter are independent from each other and following a memoryless polynomial model, in this paper, we propose two data-aided solutions that are different from the idea of [19,20] in the MIMO multipath scenario. We extend the method in [27] to the MIMO multipath scenario; a closed-form solution of the nonlinear coefficients is obtained by combining the linear least square (LLS) and singular value decomposition (SVD).
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