The nonlinearity induced by light-emitting diode (LED) affects the normal transmission and reception, which significantly limits the performance of visible light communication (VLC) system. To address the problem induced by the nonlinearity of LED, a post-distorter for orthogonal frequency division multiplexing (OFDM) based VLC is proposed in this paper. Radial basis function (RBF) interpolation, known as an excellent function approximation method, is investigated to implement the nonlinearity mitigations. To further remove the memory effect of LED, which could not be ignored, we propose to extend RBF to a high-dimensional interpolation so that it can eliminate both nonlinearity and memory effect simultaneously. The performance of the proposed RBF extension interpolation is verified by numerical simulations, and a better performance in suppressing nonlinearity is obtained in VLC-OFDM systems as compared with the frequency domain nonlinear compensation method. A signalto-noise ratio gain of 2dB in terms of 0 bEN can be obtained when bit error rate is about 6*10 -7 at 6dB power back-off (PBO), while 6dB gain can be achieved when BER is 2*10 -4 at 4dB PBO.

Radio frequency fingerprint identification (RFFI) is a promising physical layer security technique that employs the hardware-introduced features extracted from the received signals for device identification. In this paper, we consider an RFFI problem in the presence of hybrid time-varying distortions (HTVDs) induced by multipath fading channel, carrier frequency offset (CFO), and phase offset. To solve this problem, an HTVDsrobust RFFI framework is proposed. Firstly, we derive that the residual HTVDs after CFO correction can be approximated as multiplicative interference in the frequency domain. Secondly, we define a novel signal analysis dimension named spectral quotient (SQ) representation and then present the spectral circular shift division (SCSD) method to generate the HTVDsrobust SQ signals, where the multiplicative interference can be suppressed. Thereafter, the statistics including root mean square (RMS), variance (VAR), skewness (SKE), and kurtosis (KUR) are extracted from the real and imaginary components of the SQ signals, respectively. Finally, the statistical features are used for the training and testing of the support vector machine (SVM) classifiers. To further enhance the performance of the proposed RFFI scheme, we also present the spectral circular multi-shift division (SCMSD) method, which increases the flexibility in the generation of the HTVDs-robust SQ signals.Given what we knew, this is the first time attempting to mitigate the HTVDs by leveraging the strong frequency correlation at the neighboring subcarriers in the multivariate hypothesis tasks. Compared to several handcraft feature-based RFFI methods, the proposed method exhibits superior identification accuracy and strong robustness. Experimental results show that the proposed RFFI scheme can achieve the accuracy of 91.3%with five devices and 86.4% with sixteen devices when the classifiers are trained with the additive white Gaussian noise but are tested with the Rayleigh channel.

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. In visible light communication systems, the inherent nonlinear characteristics of light-emitting diodes (LEDs) are the primary source of signal distortion. To alleviate the nonlinear effect of the LED and improve the system reliability, the polynomial method is used to construct the postdistorter. With strong processing ability and high solution accuracy, radial basis function (RBF) interpolation can also be used to alleviate the nonlinear effect. In terms of the iterative implementations, recursive least squares (RLS) algorithm has been widely adopted, but performance loss exists with the solution since RLS is initially proposed for linear regressions. Aiming at the nonlinear characteristics of post distortions, Gauss–Newton (GN) method is investigated to accomplish the iterations in this paper, where Taylor series expansion is used to approximate the nonlinear regression model. Simulation results show that the RBF can obtain better bit error ratio performance than polynomial, and the proposed GN iterations have significantly better performance compared to RLS in mitigating the nonlinear effect of LED.