Rangeet Mitra

Chebyshev Polynomial-Based Adaptive Predistorter for Nonlinear LED Compensation in VLC

In recent times, there has been a surge in research on visible light communications (VLC). In VLC systems, normal light emitting diode (LED) lamps are used as transmitters by modulating the optical power of the LED with the input current. All this is done at a high frequency, so that the fluctuations are not visible to the naked eye. It is a well known fact that the LED characteristics are not linear. Such nonlinearities degrade the performance of VLC. In the literature, a predistorter using a linear adaptive scaling parameter is proposed as a predistorter, which uses the well known normalized least mean squares (NLMSs) algorithm as the learning mechanism. However, to correct a nonlinearity, we need a nonlinear mapping/predistorter. This letter proposes a Chebyshev regression-based nonlinear predistorter to correct the nonlinear characteristics of LED by learning a polynomial expansion of the input electrical signal so as to mitigate the LED nonlinearity. Simulations have been carried out to validate the performance of the algorithm against existing adaptive predistortion techniques, such as NLMS-based predistortion and post-distortion techniques, such as Volterra and Hammerstein filters.

Adaptive Sparse Dictionary-Based Kernel Minimum Symbol Error Rate Post-Distortion for Nonlinear LEDs in Visible Light Communications

Visible light communications (VLC) has emerged as one of the prominent technologies to cater to the ever-increasing high-speed-data demand for proposed fifth-generation (5G) systems. However, two main issues affect the performance of VLC in an indoor environment: a) nonlinearity of light-emitting diode, which renders the overall system nonlinear; and b) intersymbol interference due to the propagation channel, which closes the eye diagram of the transmit constellation and, hence, causes it to be unsuitable for detection. To counter these artifacts, complex post-distortion receivers such as the Volterra-decision feedback equalizer (DFE) have been proposed to recover the transmit symbols. In this paper, the use of a reproducing kernel Hilbert space-based minimum symbol error rate equalizer is proposed that provides performance comparable to a long Volterra-DFE, with much less computational cost. Simulations have been carried over IEEE 802.15 personal area network (PAN) channels, which suggest that the proposed approach gives equivalent performance in an indoor VLC channel, as compared with Volterra-DFE with far fewer computations. An analytical expression for mean square error dynamics over these channels is also derived, and it is observed that the theoretically derived expression matches the simulation results for the considered IEEE 802.15 PAN indoor VLC channels.

Precoded Chebyshev-NLMS-Based Pre-Distorter for Nonlinear LED Compensation in NOMA-VLC

Visible light communication (VLC) is one of the main technologies driving the future 5G communication systems due to its ability to support high data rates with low power consumption, thereby facilitating high speed green communications. To further increase the capacity of VLC systems, a technique called non-orthogonal multiple access (NOMA) has been suggested to cater to increasing demand for bandwidth, whereby users’ signals are superimposed prior to transmission and detected at each user equipment using successive interference cancellation. Some recent results on NOMA exist which greatly enhance the achievable capacity as compared with the orthogonal multiple access techniques. However, one of the performance-limiting factors affecting VLC systems is the nonlinear characteristics of a light emitting diode (LED). This paper considers the nonlinear LED characteristics in the design of pre-distorter for cognitive radio inspired NOMA in VLC, and proposes singular value decomposition-based Chebyshev precoding to improve performance of nonlinear multiple-input multiple-output NOMA-VLC. A novel and generalized power allocation strategy is also derived in this paper, which is valid even in scenarios when users experience similar channels. Additionally, in this paper, analytical upper bounds for the bit error rate of the proposed detector are derived for square

The Diffusion-KLMS Algorithm

M

A kernel based technique for MSER equalisation for non-linear channels

-quadrature amplitude modulation.

Finite dictionary techniques for MSER equalization in RKHS

The diffusion least mean squares (LMS) [1] algorithm gives faster convergence than the original LMS in a distributed network. Also, it outperforms other distributed LMS algorithms like spatial LMS and incremental LMS [2]. However, both LMS and diffusion-LMS are not applicable in non-linear environments where data may not be linearly separable [3]. A variant of LMS called kernel-LMS (KLMS) has been proposed in [3] for such non-linearities. We intend to propose the kernelised version of diffusion-LMS in this paper.

Normalised Multi-Stage Clustering Equaliser For Underwater Acoustic Channels

Adaptive channel equalisation is a signal processing technique to mitigate inter-symbol interference (ISI) in a time dispersive channel. To this end, the use of least mean squares (LMS) algorithm and its variants is widespread since they minimise the minimum mean squared error (MMSE) criteria by online stochastic gradient algorithms and they asymptotically tend to the optimal Weiner solution for linearly separable channels. The kernel least mean squares (KLMS) algorithm and its variants are based on the MMSE based algorithms for non-linear channels. However, as has been pointed out in the literature, the minimum bit/symbol error rate (MBER/MSER) criterion is a better choice for adapting an equaliser as compared to the traditional approaches based on MMSE criterion. In this paper, we propose a novel equaliser that is inspired from the recently proposed MSER adaptation by Gong et al. using the kernel trick for non-linear channel equalisation.

Unsupervised Multistage-Clustering-Based Hammerstein Postdistortion for VLC

Adaptive channel equalization is a signal processing technique to mitigate inter-symbol interference in a time dispersive channel. For adaptive equalization, minimum mean square error (MMSE) criterion-based reproducing kernel Hilbert spaces (RKHS) approaches such as the kernel least mean squares (KLMS) algorithm and its variants have been suggested in the literature for nonlinear channels. Another optimality criterion, based on minimum bit/symbol error rate (MBER/MSER), is a better choice for adapting an equalizer as compared to MMSE criterion. A kernel-based minimum symbol error rate (KMSER) equalization algorithm combines minimum symbol error rate (MSER)-based approaches with RKHS techniques. However, most algorithms in RKHS such as KMSER/KLMS require infinite storage requirement and hence cannot be practically implemented. To curtail the infinite memory requirement, and make adaptive algorithm suitable for implementation with finite memory and processing power, we propose quantized KMSER (QKMSER) and fixed-budget quantized KMSER (FBQKMSER)-based equalizers in this paper. In this paper, we derive the dynamical equation for MSE evolution of the QKMSER and FBQKMSER and find their performance to be asymptotically close to the MSE behavior of the KMSER. Also, it is found via simulations that the tracking performance of FBQKMSER is better than all the compared algorithms in this paper which is particularly useful for non-stationary channels.

Low Complexity Post-Distorter for Visible Light Communications

Underwater communications systems are being increasingly used in defence, security service, oil exploration, ocean science and in many other applications. The underwater acoustic channel is characterised by the large delay spread, Doppler shifts, limited bandwidths and time variability. The channel is also affected by additive impulsive noise, which makes the underwater communication even more challenging. Since the channel and noise characteristics vary immensely, an adaptive equaliser at the communications receiver forms a viable solution for increasing the bit error rate of the communication link. The adaptive multistage clustering based equaliser is one such solution which provides high throughput. However, the performance of the multistage clustering equaliser degrades in the presence of impulsive noise. To improve the throughput and robustness, we propose an adaptive normalised multistage clustering based blind equaliser for underwater acoustic channel. From simulation results, it is observed that the proposed algorithm has better convergence and symbol error rate performance. Convergence analysis of the proposed algorithm is also presented in the paper.

Kernel-based parallel multi-user detector for massive-MIMO ☆

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.