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Visible light communication (VLC) has emerged as an eco-friendly and low-cost technology for the next-generation communication systems. However, for VLC systems, existing works report performance degradation due to light-emitting-diode (LED) non-linearity, multipath and relative mobility between the transmitter and the receiver. Additionally, multipath and user-mobility introduce inter-symbol-interference (ISI) and frequency-domain spreading which degrades the performance of VLC systems. For such scenarios, orthogonal time frequency space (OTFS) modulation is well-known to jointly addresses impairments due to multipath and user-mobility. To mitigate the distortions due to LED non-linearity, recently reproducing kernel Hilbert space (RKHS)-based random Fourier feature (RFF) techniques have emerged which alleviate the dependence on learning a dictionary and, outperforms classical polynomial based techniques. However, performance of these techniques is sensitive to the choice of kernel-width. Thus, for the OTFS systems impaired by LED non-linearity, this manuscript proposes a hyperparameter-free RFF-based post-distorter, under finite-memory budget without the need of explicitly tuning kernel-width for best performance. Analytical bounds for the bit-error-rate (BER) performance of the proposed post-distorter are presented, and validated via simulations over realistic VLC channels. From the results it is verified that the proposed receiver achieves better BER performance over uncompensated scenario and classical baseline polynomial based technique.

Visible light communication (VLC) has emerged as a potential candidate for next generation wireless communication systems. However, nonlinear characteristics of light emitting diode (LED), user-mobility, and DC-bias fluctuations are the major factors that limit the throughput of a VLC link, and makes the overall additive distortion as non-Gaussian distributed. To mitigate this non-Gaussian noise processes encountered in VLC systems due to LED nonlinearity, and user-mobility, recently a random Fourier features (RFF) based kernel recursive maximum Versoria criterion (KRMVC) based post-distortion algorithm is proposed, which delivers better performance as compared to the classical polynomial series, and kernel recursive least squares (KRLS) algorithms due to the incorporation of higher order statistics of error. However, the performance of RFF-KRMVC algorithm is sensitive to the choice of kernel-width, and results in approximation errors due to imperfect choice of kernel-width. This paper proposes a novel RFF-KRMVC algorithm using a kernel-width sampling (KWS) technique called as RFF-KWS-KRMVC, which implements the post-distortion under a hyperparameter-free finite memory budget. Furthermore, analytical expressions for mean square error, and error rate are quantified for the proposed RFF-KWS-KRMVC post-distorter, and corroborated by Monte-Carlo simulations performed over standard VLC channel models.

In this paper, a kernel minimum error entropy (KMEE) based estimator is proposed for the estimation of multiple targets’ direction of departure (DOD), the direction of arrival (DOA), and the Doppler shift with multiple input multiple output radar in the presence of non-Gaussian clutter. Most existing estimation approaches are based on optimization of a complex cost function which often leads to a sub-optimum solution. Therefore, for the accurate estimation of DOD, DOA and Doppler shift, an efficient, kernel adaptive filter (KAF) based estimation approach is proposed. The proposed estimator utilizes the minimum error entropy (MEE) criterion and minimizes the error entropy function. The MEE, being an information-theoretic criterion, optimizes the higher-order statistics of error and thus makes the proposed estimator robust against the effects of outliers like clutter. The KMEE based estimator without any sparsification suffers from a linear increase in computational complexity. Thus, subsequently, the computational complexity of the proposed KMEE based estimator is reduced by incorporation of novelty criterion (NC) based sparsification technique, and the resulting estimator is called KMEE-NC. The performance of the proposed KMEE-NC based estimator is compared with the recently introduced sparse estimators based on kernel maximum correntropy criterion, and kernel minimum mean square error criterion. Additionally, KMEE-NC based estimator is also compared with other existing conventional estimators. Further, for assessing the accuracy of the proposed estimator, the modified Cramer-Rao lower bound is derived using the modified Fisher information matrix.

Optical Wireless Communication (OWC) refers to transmission in unguided propagation media through the use of optical carriers, i.e., visible, Infrared (IR), and Ultraviolet (UV) bands. In this paper, we focus on indoor Visible Light Communication (VLC)-based Medical Body Sensor Networks (MBSNs) which allow the Light Emitting Diodes (LEDs) to communicate between on-body sensors/subdermal implants and on-body central hubs/monitoring devices while also serving as a luminaire. Since the Quality-of-Service (QoS) of the communication systems depends heavily on realistic channel modeling and characterization, this paper aims at presenting an up-to-date survey of works on channel modeling activities for MBSNs. The first part reviews existing IR-based MBSNs channel models based on which VLC channel models are derived. The second part of this review provides details on existing VLC-based MBSNs channel models according to the mobility of the MBSNs on the patient’s body. We also present a realistic channel modeling approach called site-specific ray tracing that considers the skin tissue for the MBSNs channel modeling for realistic hospital scenarios.

Non-orthogonal multiple access (NOMA) has emerged as a promising technology that allows for multiplexing several users over limited time-frequency resources. Among existing NOMA methods, sparse code multiple access (SCMA) is especially attractive; not only for its coding gain using suitable codebook design methodologies, but also for the guarantee of optimal detection using message passing algorithm (MPA). Despite SCMA’s benefits, the bit error rate (BER) performance of SCMA systems is known to degrade due to nonlinear power amplifiers at the transmitter. To mitigate this degradation, two types of detectors have recently emerged, namely, the Bussgang-based approaches and the reproducing kernel Hilbert space (RKHS)-based approaches. This paper presents analytical results on the error-floor of the Bussgang-based MPA, and compares it with a universally optimal RKHS-based MPA using random Fourier features (RFF). Although the Bussgang-based MPA is computationally simpler, it attains a higher BER floor compared to its RKHS-based counterpart. This error floor and the BER’s performance gap are quantified analytically and validated via computer simulations.

Recently, there has been a huge interest in research toward visible light communication (VLC) targeted toward fifth generation (5G) and beyond standards. One of the performance limiting factors in visible light communications is the nonlinear characteristics of the light-emitting diode (LED), which is used as a transmitter in VLC. Among seminal works for mitigating performance-limiting nonlinearity, the modified cascaded multimodulus algorithm (MCMMA) based Volterra filtering approaches have been proposed recently for blind LED postdistortion. This work proposes an unsupervised normalized Hammerstein improved multistage clustering (HIMSC) based postdistorter that is formulated as a more suitable cost function, as compared with the Volterra-MCMMA-based approaches. Theoretical bounds on step size are found to ensure convergence of the proposed postdistorter. Simulations over IEEE 802.15 personal area network (PAN) channels indicate that the proposed normalized HIMSC delivers much better performance as compared with the Volterra-MCMMA in terms of mean square error (MSE) and bit error rate (BER) performance.