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 M-quadrature amplitude modulation.

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.

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.

In this letter, a visible light communication (VLC) link affected by the light emitting diode (LED) nonlinearity and dispersion due to the IEEE 802.15 PAN channel impulse response is considered. To mitigate severe intersymbol interference and LED nonlinearity, computationally complex classical post-distortion techniques fall short in terms of applicability to massively connected 5G deployments (as in a VLC attocell) due to increased demand on resources and intractability of implementation. To address these issues, this letter proposes a low-complexity reproducing kernel Hilbert space-based post-distorter using a better sparsification technique equipped with an adaptive kernel width optimized by a minimum symbol error rate (MSER) criterion. Use of the proposed MSER metric for kernel width optimization reduces the computational complexity at the receiver, without compromising on the bit error rate. An upper bound on step-size to guarantee convergence of the proposed post-distorter is analytically derived, and the steady-state mean square error is also analyzed theoretically and validated by simulations. Furthermore, analytical upper bounds for bit error rate and steady-state dictionary size are also derived.

Visible light communication (VLC) has emerged as a promising, green, and interference-free supplement to existing radio frequency-based communication systems. To enhance the capacity of existing VLC-based systems, using massive arrays of light emitting diodes (LEDs) at the transmitter and photodiodes at the receiver has been proposed recently, resulting in a massive multiple-input multiple-output (m-MIMO) VLC system. However, by increasing LED array pitch in m-MIMO, the VLC channel matrices are generally ill conditioned which makes them sensitive to small perturbations and degrades the overall bit error rate (BER) performance. To reduce the condition number of the overall channel matrix, a novel precoder using an exponent-based singular value decomposition is proposed for m-MIMO VLC system. Furthermore, a joint optimization problem is formulated to optimize the exponent and to detect symbols iteratively using the minimum symbol error rate criterion. Simulations show that the proposed adaptive precoding technique exhibits superior BER performance over existing techniques. Analytical upper bounds for BER are also derived and validated by simulations.