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SVM Detection for Superposed Pulse Amplitude Modulation in Visible Light Communications

Youli Yuan1, Min Zhang1*, Pengfei Luo , Zabih Ghassemlooy , Danshi Wang1, Xiongyan Tang1, Dahai Han1 1State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China

2Research Department of HiSilicon, Huawei Technologies Co., Ltd, Beijing 100085, P. R. China 3Optical Communications Research Group, NCRLab, Faculty of Engineering and Environment,

Northumbria University, Newcastle upon Tyne NE1 8ST, U.K. *

Abstract A support vector machine (SVM)-based data

detection for 8-superposed pulse amplitude modulation in visible light communication is proposed and experimentally demonstrated. In this work, the SVM detector contains three binary classifiers with different classification strategies. And the separating hyperplane of each SVM is constructed by training data. The experiment results show that the SVM detection offers 35% higher data rates when compared with the traditional direct decision method.

Keywordssupport vector machine; superposed pulse amplitude modulation; visible light communiction; direct decision.

I. INTRODUCTION With the increasing popularity of smart mobile devices, the

wireless data traffic is growing exponentially [1]. This has resulted in shortage of the radio frequency (RF) spectrum and made the RF spectrum become a precious commodity. Since the RF spectrum is limited and expensive due to the license fees, the optical wireless communications (OWC) has evolved to become a high-capacity complementary technology to the RF communications, which can alleviate the spectrum shortage. In addition, the RF wireless technologies are not allowed to be used in electromagnetic sensitive areas such as hospitals, aircraft cabins, mines and others because of the electromagnetic interference [2]. As a new lighting technology, the white light-emitting diodes (LEDs) are being installed at a fast rate globally, thus creating a golden opportunity for full scale practical realization of visible light communications (VLC) (or LiFi) technology.

In VLC systems, the information signal is used to intensity modulate (IM) LEDs. On-off-keying (OOK) is one of the most widely used modulation format, which is relatively easily to implement in hardware [3]. However, the transmission data rate Rd is limited by the low bandwidth of white LEDs, which is only several MHz due to the slow time constant of the yellow phosphor [4]. Hence, Rd of conventional OOK could only reach several Mbps. Consequently, several studies have been performed to increase Rd in VLC systems. Firstly, quadrature amplitude modulation (QAM) with orthogonal frequency division multiplexing (OFDM) including direct-current-biased optical OFDM (DCO-OFDM) and asymmetrically clipped optical OFDM (ACO-OFDM) are widely used in VLC systems [5]. But OFDM requires that the transmitter (Tx) (including LED and the driven circuit) has a

wide dynamic range and reasonably good linearity characteristics. Secondly, OFDM based on employing a discrete power level stepping technology was proposed in [6] to address the shortcoming of conventional optical Txs. The core of this technology is the digital to analog conversion (DAC) implemented in the optical domain [7-8]. However, these modulation formats rely on complex digital signal processing (i.e., fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT)). This leads to increase complexity for both Tx and receiver (Rx). To avoid such complexity, spatial modulation techniques were proposed with higher spectral efficiency as in [9-15]. Superposed pulse amplitude modulation (SPAM), which is low-cost, insensitive to non-linearity of LEDs, was proposed to reduce the complexity of the transceiver and enhance the frequency efficiency [12]. In SPAM, an N parallel NRZ-OOK signals with different amplitudes are used for IM of N-LED, whose optical power are then superposed in a free space channel to produce a 2N-level signal. Following this principle, the Tx can provide a larger dynamic range than traditional one of LED system.

Machine learning has been widely employed to solve problems in different areas such as big data processing, personalized recommendation, pattern recognition, data mining and artificial intelligence, etc. [16]. Whats more, this technique has been used in VLC systems to reduce the effect of fluorescent light interference, inter-symbol interference (ISI) and artificial light interference [17-19]. In VLC systems, the background noise, electrical noise from transceiver and bandwidth limitation of LED contribute to the distortion of the received waveform. Amplitude modulation schemes such as pulse amplitude modulation (PAM) are especially sensitive to the signal distortion, which leads to poor performance in IM/direct decision (DD) based systems.

In this paper, we introduce a support vector machine (SVM) algorithm in 8-SPAM VLC system to improve systemperformance. After being trained with a training data, SVM is able to learn the properties of received signal and generate an improved decision hyperplane for more precise data detection. In order to test the bit-error rate (BER) performance of a SVM-based 8-SPAM VLC system, an experimental test-bed has been built. The results show that the SVM detection scheme can achieve improved performance when compared with the traditional DD method.

2016 10th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP)

978-1-5090-2526-8/16/$31.00 2016 IEEE

Fig. 1. A waveform emitted by three LEDs with different amplitudes.

The structure of this paper is as follows. The operation of each technique is detailed in Section II. The experiment setup of the SVM-based detection for 8-SPAM is described in Section III. The experiment result and discussion are presented in Section IV. Finally, Section V concludes this paper.


A. Concept of SPAM The basic concept of SPAM is to use multiple LEDs to

realize DAC in the optical domain [7-8] - multiple optical pulses transmitted in parallel from different spatial positions in a LED array, which eventually produces multi-amplitude light levels to realize PAM in the optical domain. In this scheme, a parallel binary sequences control multiple LEDs (i.e., on or off) with each LED transmitting at a specific intensity (i.e., optical power) level. With the superposition of light intensities in the free space channel, a multi-level light signal is generated. Assuming I is the maximum light intensity among all independent LEDs, in order to generate an equally spaced PAM signal, the corresponding LED light intensities are set as: I1 = I / 2N - 1), I2 = I / 2N - 2), ..., Ik = I / 2N - k), and IN = I, where Ik is the kth LEDs output optical power, and N is the number of LEDs. Therefore, the equally spaced PAM signal could be generated by combining different optical power levels in the free space channel, which can also be considered as DAC of signal. Finally, the 2N-level optical PAM signal can be detected at the Rx using only a single photo-detector (PD). A standard PAM demodulator can then be employed for de-mapping the received multi-amplitude signal back to the transmitted serial data stream. Therefore, by employing N-LED and a single PD, the SPAM scheme could archive N times that of the one-LED and one-PD OOK system. Unlike OFDM or MIMO scheme which have high spectral efficiency, the SPAM scheme is a relatively simple scheme not requiring complex data processing and DAC.

It is known that the power versus current characteristics of LEDs are linear over a certain range, beyond which the characteristics becomes non-linear. In this paper, we generated three OOK signals for IM of 3 LEDs with different intensities to realize the 8-SPAM scheme. Fig. 1 shows an example of



Fig. 2. 8-SPAM modulation scheme: (a) three waveforms that drive three LEDs, and (b) the corresponding combined waveforms and signal mapping strategy.

Fig. 3. SVM schematic diagram: (a) for linearly separable data, (b) for

linearly inseparable data, and (c) using kernel function to realize linear classfiction for linearly inseparable data.

a normalized amplitude waveform for LEDs A, B and C having the same frequency. To achieve 8-level of light intensities, the normalized amplitude of LEDs A, B, and C are set to 0.25, 0.5 and 1.0, respectively.

Fig. 2(a) depicts the waveforms that drive the three LEDs, whereas Fig. 2(b) illustrates the corresponding combined 8-SPAM waveform and the signal mapping strategy. It is notable that the latter is the same as the traditional 8-PAM signal, therefore, an 8-PAM demodulator can be adopted in the proposed 8-SPAM to recover the information.

2016 10th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP)

Fig. 4. Classification strategy based on SVM for 8-SPAM.

B. SVM for 8-SPAM Detection SVM is a well-known statistical classification and regression technique, which has been used to solve a range of engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and digital communication systems. In the latter application SVM has been used as a new method for channel equalization [20] and for combating ISI and the co-channel interference [21]. A SVM is fundamentally a two-class classifier, which could generate boundary to classify two segments of data. Based on the statistical theory, SVM aims to find an optimal (maximize) margin, which is defined by the smallest distance between the decision boundary and any point of the samples, as is shown