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Exploiting Equalization Techniques for Improving Data Rates in Organic Optoelectronic Devices for Visible Light Communications Paul Anthony Haigh†, Zabih Ghassemlooy†, Hoa Le Minh†, Sujan Rajbhandari†, Francesco Arca‡, Sandro Francesco Tedde‡, Oliver Hayden‡ and Ioannis Papakonstantinou†† Abstract—This paper presents the use of equalization techniques in visible light communication (VLC) systems in order to increase the data rate. Here we investigate two VLC links a silicon (Si) light emitting diode (LED) and an organic photodetector (OPD), and an organic LED (OLED) plus an Si photodetector (PD), together with three equalization schemes of an RC high pass equalizer, a fractionally spaced zero-forcing equalizer (ZF) and an artificial neural network (ANN). In addition we utilize a pre-distortion scheme to enhance the performance of the digital equalizers. For both systems the bit rate achieved are 750 kb/s from a raw bandwidth (BW) of 30 kHz and 550 kb/s from a raw BW of 93 kHz. Index Terms— Visible light communications, photodetector, organic light emitting diodes, equalizers
O
organic
I. INTRODUCTION
RGANIC semiconductors are becoming very popular in the research community, not only in white VLC, due to the rapid development of OLEDs, but also for analogue electronics where organic thin-film transistors (OTFTs) and organic field effect transistors (OFETs) have been fabricated to switch OLEDs [1]. The advances in organic devices are due to the development of the manufacturing processes that produce them – accurate three dimensional printers allow rapid prototype development and flexibility in variety of materials that can be loaded and printed [2]. Organic semiconductors, in particular small molecules, can be coated by means of the thermal evaporation process. However, the real cost reduction for the organic based devices lies in their solution processability; polymers and functionalized small molecules are thus soluble in common solvents. The most important coating techniques are spincoating, doctor blading, inkjet printing, and spray-coating. It is worth noting that organic semiconductors behave identically to silicon semiconductors; except the electron and hole transport layers (ETL and HTL) have lower mobility and © © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Paul Anthony Haigh acknowledges support from Northumbria University. Authors† are with the Optical Communications Research Group, Northumbria University, Newcastle-upon-Tyne, NE1 8ST, UK. Authors‡ are with Siemens AG, Corporate Technology, CT T DE HW3, Erlangen, Deutschland. Author†† is with the Department of Electronic & Electrical Engineering, University College London, Torrington Place, London, WC1E 7JE, UK. email:
[email protected].
therefore lower current densities. Therefore sub-atomic phenomena commonly performed in the silicon domain transfer directly to the organic domain; as demonstrated by the organic non-volatile memory achieved in [3]; meaning that organic memory is palpable and can plausibly be achieved. In VLC interest is starting to intensify in organic devices, with new materials research presented in [4] that promises high luminous efficacies of 100 lm/W, compared with 40–60 lm/W in typical OLEDs [5]. Thus bringing the devices more into line with white LEDs [6], which also typically have luminous efficacies of 120 lm/W, making them more suitable for general purpose use and enhancing OLED’s capabilities for candidature in future VLC systems. OPDs are also a promising technology for future VLC systems. The OPDs reported in this paper have been processed by the spray deposition technique as reported in [7] and are based on the bulk heterojunction (BHJ) concept [8]; an interpenetrated network (blend) of an electron donor and an electron acceptor. The OPD dark current is about two orders of magnitude larger than a high end monocrystalline Si PD, however it can compete with Si avalanche PDs (APDs) as well as with low end Si PDs. Advantages are in the low material costs and the solution processability, which makes fabrication via spray coating of the semiconducting layers extremely cost effective. The material costs for the poly(3-hexylthiophene):[6,6]-phenyl C61-butyric acid methylester (P3HT:PCBM) system implemented here are ~ 0.20 €/cm2. If the quantity of material is scaled up into the kilograms region, the cost will fall further. Contrary to Si PDs for visible light, OPDs with P3HT as the absorber do not require infrared (IR) filtering as the bandgap is too large to absorb near IR (NIR) wavelengths. The OPD external quantum efficiency (EQE) spectra can be seen in Fig. 1. The responsivity of the device is comparable to a generic Si PD, however it has the advantage of being IR blind as mentioned. The responsivity spectra can be seen in Fig. 2. Current work in organic devices is mostly restricted to the development of the chemical and physical characteristics of the devices, which this paper does not focus on. An analogue OLED equalization scheme employing a resistor-capacitor (RC) post-equalizer that offers a BW increase of six times is presented in [9]. In this work the effect of baseline wander (BLW) induced from the RC filter [10] is not considered, as the focus was on the BW extension analysis, which is done by recording impulse responses, rather than data transmission and error estimation.
2
Fig. 1. OPD EQE showing no IR absorption
(1) where ε0 and εr are the permittivity of a vacuum and the relative dielectric constant of the organic layer, A is the photoactive area of the diode. As area increases, materials become much cheaper than silicon devices; hence organic devices typically have large active areas. The organic layer thickness is given by d – typically ~ 1-200 nm and ~500 nm for OLEDs and OPD, respectively. The thickness is constant for the whole of the device and therefore the capacitance is linearly proportional to the active surface area. The capacitance of the organic device is the limiting factor in determining the available modulation BW; which can be proved when considering the equivalent circuits shown in Fig. 3 [12], where Rd is the contact resistance of the OLED and Rp is the OLED leakage resistor. The applied current is split in both cases between the respective parallel R and C combinations; here only the proof for the OPD is shown; but however both devices can be thought of as low pass filters (LPF) therefore it is true for both [13]. The time-dependent photocurrent iphoto in response to a unity step function is given by [12]: ( ) ( ) [ ] (2) where i0 is the start current; assumed to be equal to the OPD dark current, t is the instantaneous time and . It can be shown the rise time δtr is given by: (3)
Fig. 2. Responsivity spectra of the OPD This paper presents the same RC post-equalizer structure, however rather than employing an OLED; we use an LED and OPD to test the possible BW increase of the OPD. This paper also presents for the first time the implementation of digital equalization techniques for OLED and OPD devices. The OLED is tested with a Si PD as a receiver and the OPD is tested with a Si LED as the transmitter. This is because the respective silicon counterparts have BWs that are orders of magnitude higher than the organic devices; ensuring that the equalizations are extending the operational range of the organic devices. This paper is organized as follows. In Section II the organic device equivalent circuits are outlined, whereas in Section III the devices under test are described. In Section IV the equalizer theory is summarized. In Sections V and VI the results are shown and comparisons are drawn. Finally in Section VII conclusions are made.
where fc is the 3 dB cut-off frequency and the full proof is available in [12]. Hence, the capacitance is one of the dominant factors in the organic device 3 dB BW limitation. Materials can also have an impact on the performance of the device. Careful selection of organic layers helps to improve the fluorescent lifetime (FL) of organic devices. Different OLED materials have been tested in [14] illustrating the influence the material has on FL. As FL contributes to the transmission speed, the faster the device releases the photon the higher the speed that can be achieved, but this is beyond our control for this paper.
II. EQUIVALENT CIRCUITS OF THE ORGANIC DEVICES In VLC, white light is employed to provide both illumination and data communication simultaneously. The channel is the indoor environment and is not frequency selective in the visible region and is therefore represented by the linear DC gain [11]. Therefore the channel is limited by the device with the lowest 3 dB modulation BW. For the OPD and OLED the measured 3 dB BW are 30 kHz and 93 kHz, respectively compared with Si devices with 3 dB BWs that are orders of magnitude higher. The organic devices can be represented by plate capacitors, given by [12]:
Fig. 3. Equivalent circuits for organic photonic devices
3 III. DEVICES UNDER TEST As mentioned above there are two cases to be tested; (a) the Si LED to OPD; (b) the OLED to Si PD. The silicon LED used in case (a) is a commercial white Philips Luxeon Rebel DS64, which has a raw BW of ~4.4 MHz [15] and the OPD is a custom device produced in collaboration with Siemens, details of the device can be found in [7]. In case (b) the OLED used is an Osram ORBEOS CMW-031; which has a raw BW of 93 kHz. The PIN PD used is a Centronics OSD15-5T with a raw BW of ~30 MHz in the visible spectrum. Both the LED and OLED are Lambertian sources [16] where the radiance and channel response are given in [11]. The distance between transmitter and receiver in this paper is defined as where the incident light on the PD is 400 lx, which is the required light level for office conditions [17]. The distances for cases (a) and (b) are 40 cm and 10 cm, respectively, which are different and are due to the different light intensities of the devices. Since the electrical signal is identical in both cases and the distance is adjusted to obtain the same incident light level, the signal-to-noise ratio (SNR) at the receiver is approximately the same. The SNR will never be identical owing to the different noise sources; the OLED has a larger panel than the LED; therefore it is reasonable to suggest that the induced thermal and shot noise will be greater in comparison, whilst these are negligible in comparison to the ambient noise. At the receiver side; PD with a smaller collection area and induces less noise in the same manner as previously, whereas the OPD is typically dominated by the shot noise due to the low BW [12]. The SNR at the point immediately after the transimpedance amplifier (TIA) is given by [12]: √(
)
̃
̃
̃
(4)
where is the photocurrent, is the BW, is the feedback resistor of the TIA, is the PD shunt resistor; typically [12]. ̃ is the TIA thermal noise voltage variance per frequency [12]: ̃ (5) where kB is the Boltzmann constant and T is the room temperature in Kelvin. is the capacitance of the PD under ̃ test and finally is the TIA shot noise current variance per frequency [12]: ̃ ( ) ( )] [ (6) where q is the charge of an electron; idark is the dark current of the PD and iph is the current induced by ambient incident photons. The (V) notation signifies that these values are relative to the bias voltage (25 V and 5V reverse bias for the PD and OPD, respectively). Where applicable, the tilde notation ̃ indicates BW dependency. From (4); it is clear that in order to maximize the SNR it is necessary to select a TIA with low ̃ and ̃ noise characteristics; hence in this paper the Analog Devices AD8015 is used [18] since the electrical noise induced by the TIA is as low as 2.4 pA/√Hz, therefore not impacting the results obtained. It is also important to use a LPF with a cut off frequency equal to the bit rate at the receiver in order to avoid extraneous noise induced from unused BW. Also when
an RC equalizer is implemented; it should be noted that the SNR will be degraded due to the additional extra thermal noise associated with extra components. IV. EQUALIZERS UNDER TEST In order to overcome the limiting BW factor; a number of equalizers are considered. The first equalizer implemented is the same analogue RC equalizer setup as in [9] to find the suboptimal BW increase for the OPD case. A lead-lag circuit could be considered, but since the channel response is the DC gain (i.e. it is flat and fading) and the phase response is linear; there is no need to implement such a circuit as there is no phase distortion effect [11]. Next, digital equalizers are implemented in order to relieve the power penalty that the analog filter induces, since in case a); where the OPD already has lower power output; could degrade the signal power to the point where it drops beneath the noise equivalent power (NEP) or the sensitivity level of the TIA. The RC equalizer could be deployed as a pre-equalizer or post-equalizer. In this paper we present the post-equalizer for two reasons; firstly, the theory is identical for both the pre– and post– cases [9]. Therefore there is no considerable gain in selecting either. However literature shows that the postequalizer typically offers a better BW expansion [19, 20] owing to the larger impedance of the receiving circuit (50 Ω) in comparison to the transmission side (~5-10 Ω); offering a better impedance match and a larger power transfer. The other reason is simply for consistency because other equalizers under test are both post-equalizers. ZF is implemented as a digital increase of the sub-optimal RC equalizer. The RC post-equalizer should offer limited, forced zero inter-symbol interference (ISI) at the cost of a large power penalty. ZF offers no direct power penalty because there are no additional components. Both ZF and ANN equalizers are implemented first without and then with a digital RC pre-distortion scheme in order to diminish the influence of the LPF impulse response and observe the improvement. A. RC high-pass filter equalizer Since the analogue RC equalizer is being used with OLEDs in [9], we propose the same test for the OPD. The theory is widely reported and well established in literature and will be covered briefly here. For further reading see [9, 19, 21]. The OPD can be modeled as a LPF with an impulse response given by [12]: ( ) (7) The impulse response of the LPF frequency domain can be written as: ( )
(8)
⁄( ) is the measured cut-off frequency of where the device; obtained using the time constant. Therefore in the ideal case we want to employ a high pass filter (HPF) with an impulse response defined as [13]: ( ) ( ) Given in the frequency domain by:
(9)
4 (10)
( )
where the cut-on frequency of HPF 1/(REQCEQ) and REQ and CEQ are the equalizer resistor and capacitor, respectively. There are some considerations to make nonetheless, such as the power penalty due to dissipation in the additional components and the fact that values of REQ and CEQ may not be available. On the other hand the RC-HPF equalizer can introduce a significant BLW effect [22], thus limiting the system bit error rate (BER) performance. B. Fractionally spaced – zero forcing equalizer Due to limitations of the sub-optimal RC-HPF equalizer, we propose to employ the finite impulse response (FIR) ZF. ZF operates by inverting the estimated frequency response of the limiting component’s BW defined as [13]: ( ) (11) ( ) and then convoluting it with the received signal. In cases (a) and (b) the limiting factors are the slow response of the OPD and the OLED, respectively. Of course when the low BW is exceeded, a significant amount of ISI is introduced because of insufficient response times. It is possible, however, to achieve high data rates using a linear ZF by oversampling the incoming signal symmetrically around the sampling point and forcing the center point to be non-zero and every other point to be zero. ZFE samples at the rate of μT - nξ to find the discrete link impulse response, where μT is the μth sample of the bit period, T. The sampled response is represented in an n x n matrix, where n is the number of filter taps and ξ is the oversampling rate, typically ξ ≥ T/2 [13], the rate selected for this test is ξ = T/2. This rate was selected for consistency with the ANN sample rate. Taking the low pass impulse response in equation (7), the sample point matrix, with elements μT - nξ is given by [13]: (
)
( ) ( ) (12) Considering that is the filter coefficient array, is the sample matrix and is the output array, the relationship between them is as follows [13]: (13) To eliminate ISI, the equalizer output array must be zero except at the sampling instant. Hence, for the ideal case; = [0 0 … 1 … 0 0]. The matrix X is found by transmission of a short training sequence of pulses (length 5 μs for the case (a) and 500 ns for case (b), with amplitude of 5 V p-p in both cases) to find the system impulse response; the longer the training sequence the better the impulse response is estimated to be. This comes with the disadvantage that X is never perfect as more pulses that are transmitted lead to a better representation of the device impulse response and also the quantity of filter taps implemented also influences the quality of the sampled impulse response. Obviously the more taps are used the better the representation, although this comes at the cost of on-board memory and computational speed; which is a trade-off that is reported in [24]. Once the coefficients c are found, they are convoluted with the X matrix to satisfy [23]: ( ) ∑ ( ) (14)
where q(μT) represents the equalized signal; see [23] for application notes. It is well known that ZF is susceptible to noise since a noisy signal can intercept the training sequence. The reason ZF is selected and not the minimum mean square error (MMSE) equalizer is due to the very high SNR experienced in VLC [25] as well as the ease of operation [26]. In this paper we investigate the trade-off between the number of filter taps used and the achievable bit rate. The computational speed is not necessarily a concern because it is constant for all the data that passes through the equalizer; acting as a buffer with the same length as the number of taps in each case. The ZF is realized in MATLAB for offline processing. It is possible to implement on a field programmable gate array (FPGA) but the decision was taken to implement the filter in MATLAB as this is the first report of such filters with organic devices of such small BW. It is straightforward to convert MATLAB scripts into FPGA code, which is the next step in the future. The number of taps to be used is 21; as this level is reasonably low and offers rapid computational speed in addition to satisfying the odd number of taps requirement in equation (14). 11:10:101 taps were also tested in MATLAB and the equalized BER is found to be approximately the same. C. Artificial neural network equalizer ANN based equalizers with a linear and decision feedback structure have been implemented for channel equalization in digital communications due to their superiority in performance [24, 25]. The multilayer perceptron (MLP) ANN is a popular choice of adaptive equalizer due to capability of non-linear mapping between the input and output as well as the adaptability. MLP equalizers are superior to the conventional transversal and decision feedback (DF) equalizers in terms of the equalizer performance and symbol error rate (SER) [24]. Every MLP consists of (i) an input layer with no processing taking place; thus is not counted as part of the network layers, (ii) hidden layers and (iii) an output layer. Provided there is sufficient number of neurons, a 2-layer ANN can be used as a universal approximator, mapping any input-output data set [26]. The MLP consists of neurons with N inputs [xi : i = 1, . . . , N], a weight wi associating with each inputs and an output y, calculated as: (15) (∑ ) where i =0, . . . , N if there is a bias and i =1, . . . , N otherwise. The ANN can be trained with supervised and unsupervised methods. For channel equalization, supervised training using back-propagation is the most popular [27]. In this algorithm, the ANN adjusts the weight to minimize the cost function ( ): ( ) ‖ ( ) ( )‖ (16) where ( ) is the desired response and ( ) is the actual response of the equalizing ANN network. The backpropagation algorithm performs a gradient descent on ( ) in order to reach a minimum. The weights are updated as: ( ) (17) ( ) ( ) ( )
5 where is the weight from the hidden node i to the node j and is the learning rate parameter. The performance of the algorithm is sensitive to . If is too small, the algorithm takes a long time to converge and if is too large, the system may oscillate causing instability [28]. For these reasons, the adaptive leaning rates outlined in [29, 30] are adopted for faster convergence. The MLP with a single input layer, a hidden layer and one output layer with supervised training is utilized in this work. The feed-forward back-propagation training algorithm is implemented. The ANN is trained to classify the received signal based on the observation vector. The concept of equalization as a classification problem is well studied in literature [31, 32]. The ANN is also implemented in MATLAB. In order to provide real time FPGA implementation, there are several considerations. Since the data rates in this paper are relatively low in comparison with the clock speed of a low end modern FPGA device, the main consideration is the complexity of the equalizer and the memory requirements. Literature shows that for similar systems working at faster frequencies (in the tens of MHz regime) it is possible to implement the MLP (the most computationally intense equalizer) using either pipelined blocks of RAM memory or a flip flop architecture. Since we do not demonstrate FPGA implementation in this work, for further reading of real time implementation readers should refer to [33, 34]. D. Test setup The proposed equalizations are tested with the setup in Fig. 4, for cases (a) and (b). In order to control the instruments automatically and precisely; a custom written LabVIEW virtual instrument (VI) controls each instrument. The AFG is loaded with a pseudorandom binary sequence (length 210-1) and triggers the DSO which records both the transmitted (reference) and received data in order to conduct BER tests. The transmitter circuitry consists of a bias tee and a DC current source which supplies 350 mA. Tektronix AFG 3252
Transmitter Circuitry Si LED OLED
Fig. 5. (a) RC BW increase and (b) power penalty The cut-on frequency fc of the high pass RC-filter is 6 kHz, which gives a normalized cut-on frequency (normalization factor: fc/Rb) of ~ 0.075 at 80 kb/s. The optical power penalty due to BLW increases exponentially with more than 3 dB power penalty observed at fc/Rb = 0.01 [22]. On the other hand, HPF significantly attenuates the frequency components near the DC region; thus the signal attenuation is very high as illustrated in Fig. 6.
Agilent DSO 80604B Real Time Scope
Vbias
PC LabVIEW Transmitter
OPD Si PD
Transimpedance Amplifier
largest BW increase is observed with an R = 4.7 kΩ and C = 4.7 nF where the BW is increased to 170 kHz (~ six times). A significant increase to the 30 kHz BW can now be observed for the case of RC equalizers. The best cases are observed with the relatively large resistors and mid-range capacitors with the largest increase observed when R = 4.7 kΩ and C = 4.7 nF, where the 3 dB BW is extended by just short of six times to 170 kHz. The penalty paid for the increase in the BW is the power penalty as outlined in Fig. 5(b). Fig. 5(b) shows a 54 dB power penalty for the RC combination mentioned above in comparison to the unequalized case. Taking the absolute largest BW increase is not always the optimal case; similar increases of around 140 kHz with R = 4.7 kΩ and C = 10 nF can be observed for a power penalty of 44 dB, i.e. there is a trade-off between power penalty and BW increase. Since a comparable increase can be obtained for less power penalty, the second RC filter will be tested in this paper (R = 4.7 kΩ, C = 10 nF).
Output
Input
Receiver
Fig. 4. Block diagrams for case a) and case b) As is typical in optical wireless communications; the BER target is set at 10-6 [9, 19, 20]. Data is driven through the relative LED/OLED into the channel and incident light is absorbed by the relevant OPD/PD. The AWG triggers the DSO as mentioned; where both devices store the received data for offline processing. V. RESULTS AND DISCUSSION A. RC-HPF equalizer The RC equalizer was tested using the same setup as in Fig. 4 except with a high pass RC network inserted between the TIA and the oscilloscope. A number of RC combinations were tested; with the resulting 3 dB BWs shown in Fig. 5 (a). The
Fig. 6. The origin of BLW with RC-EQ In Fig. 5 (a), the low frequency effect cannot be seen since only the 3 dB point is shown. In Fig. 6, the origin of the BLW effect is outlined. The frequency response is divided into three sections; A, B and C and the 3 dB crossing is highlighted. Section A is the most significant, since the RC equalizer (and/or bias tee) significantly attenuates frequencies below the cut-on frequency. Section B shows the band passed region and C shows the frequencies outside the 3 dB bandwidth.
6 Fig. 7 shows the BER performance for the OPD with no equalization and the RC, ZF and ANN equalizers, of which the ZF and ANN will be analyzed in the following subsections. For OPD with no equalization, significant errors occur when data rates exceed 40 kb/s, which closely matches the measured raw BW of OPD as expected. Considering a 3 dB BW of 140 kHz as shown above, significantly lower data rates (80 kb/s) can be achieved. This can be attributed to two factors: (i) higher power penalty where in fact the received power from OPD (measured to be -70 dBm at 170 kHz) is well below the typical sensitivity of AD8015 TIA, inducing error and (ii) the BLW effect. B. Fractionally spaced – zero forcing equalizer The ZF was tested using two approaches (i) using the raw BW only and (ii) using a digital pre-distorting first order analogue filter to enhance the bandwidth. The pre-distortion scheme is the same as the RC equalizer in principle however it is implemented in software before driving the data through the AWG using LabVIEW. This means that the system will obtain the benefits of the pre-equalizer and avoid the BLW and power penalty considerations. The cut-on frequency of the pre-distortion filter is set equivalent to the cut-off frequency of the device under test for maximum bandwidth increase. 1) Case (a) – LED/OPD Note that the magnitude response of the ZF (given by equation 11) increases linearly with the frequency by 1/ . Using just ZF, the achievable bit rate is ~120 kb/s (~4 times the raw case). Using pre-distortion the achievable error free bit rate is 330 kb/s (~10 times larger than the raw case) for the same SNR at the receiver. The reason for this extreme increase is due to the pre-distortion modifying the transmitted signal to compensate for the impulse response of the OPD before transmission, working the same as the RC equalizer. See Fig. 7 for the BER performance of both ZF schemes.
Fig. 7. OPD BER performance for every scheme 2) Case (b) – OLED/Si PIN PD The BER performance of the unequalized OLED is shown in Fig. 8, where the achievable error free data rate is ~100 kb/s, which also closely matches the raw 93 kHz bandwidth. The ZF can achieve 300 kb/s (~3 times more than unequalized). This increase is actually lower than that of OPD for the same conditions. One likely explanation is the relative difference in SNR between case (a) and case (b) causing an increase in performance of the ZF equalizer. For the pre-distorted ZF with the same SNR at the receiver 420 kb/s of error free data transmission can be achieved, see
Fig. 8. The ZF is based on the concept of channel inversion, which can be difficult to realize depending upon the location of the zeros and poles of the transfer function. On the other hand, it is necessary to have a precise knowledge of the system transfer functions (transmitter, channel and receiver) for the optimum performance. Hence, ANN, which does not require the pre-knowledge of the channel, may be the preferable option in many cases. C. ANN equalizer In order to make like-to-like comparisons, the ANN equalizer is fractionally-spaced with an oversampling rate of ξ. The linear structure is implemented in this experiment with the number of tap-delay lines dynamically adjusted. The number of neurons in the hidden layer is made equal to the input layer. The nonlinear log-sigmoid function is adopted in the hidden layer for the nonlinear mapping and the linear transfer function is used at the output layer. The ANN is in a supervised manner with 300 symbols, the LevenbergMarquardt back-propagation algorithm is implemented for updating the weights. The trained network is used for equalization. The output of the ANN is sliced with a threshold value of 0.5 in order to generate the binary sequence. 1) Case (a) – LED/OPD Using the raw BW of OPD, ANN can provide error free transmission up to 150 kb/s, which is slightly higher than ZFE with no pre-distortion – much improved performance can be achieved using ANN in conjunction with the pre-distorted signal, up to 750 kb/s see Fig. 7. This is due to the fact that with the pre-distortion, the overall system becomes non-linear. Since ANN is more effective than a linear equalizer in nonlinear systems, due to the non-linear mapping capability, a significant performance improvement is observed in comparison with linear equalizers. 2) Case (b) OLED/PIN PD Like the previous case, ANN is trained to classify the received signal based on the received signals. An error free transmission up to 500 kb/s is observed using ANN, which is ~ 8 times improvement to the raw BW. Notice that this is significantly higher than that of ZFE. With pre-distortion, the improvement is marginal with the error-free transmission rate up to 550 kb/s, see Fig. 8.
Fig. 8. OLED BER performance for every scheme The ANN equalizer is based on the classification concept. The classifier is easier to realize than channel inversion and ANN base equalizer tends to offer improved performance compared to the traditional linear equalizers. Without a pre-
7 equalizer, the ANN can offer almost double the data rate of the RC-equalizer. With pre-distortion the ANN offers almost double the date rate of ZF. It is noteworthy that an ANN with a non-linear transfer function can offer non-linear mapping of the input and output, whilst ZFs can only map the linear function. With the introduction of the pre-equalization, the overall channel becomes second order filter, which cannot be approximated using a linear transfer function. As a result the ANN offers significantly improved performance compared to ZF, of 750 kb/s in case a) and 550 kb/s in case b). VI. COMPARISON OF DIFFERENT EQUALIZATION METHODS A number of equalization methods have been presented in this paper. For case (a) it is clear that the RC post-equalizer offers very little data rate increase when considering the raw BW as well as high power penalty. The ZF and ANN offer marginally higher data rates than the RC equalizer. A universal disadvantage of these equalizers is that they require a training sequence and can be difficult to synchronize, which is a problem that is simple to overcome in the MATLAB environment, but requires a substantial signal processing capability in real time. Since FIR equalizers work on the concept of channel inversion, they equalize the ISI far more effectively than the RC equivalent. If pre-distortion is implemented in the digital domain; the BW increase offered by the RC equalizer can be realized but without any BLW considerations. Therefore using digital predistortion in conjunction with the FIR equalizers typically offers highest data rates. The ZF and ANN are similar in terms of required platform for implementation; however ZF offers no feedback or filter tap coefficients updates after the training sequence, unlike ANN which constantly corrects itself. Therefore it is worth the minor additional programming and implementation time in order to achieve the very high bit rates that ANN offers compared to the raw BWs in each case. VII. CONCLUSION In this paper we have employed several equalization techniques for OPD and OLED used in VLC. With a raw modulation BW of (a) ~30 kHz and (b) 93 kHz; we are able to drive a maximum bit rate of a) 750 kb/s and b) 550 kb/s by implementing digital FIR post-equalizers with and without distortion. The extended data rates represent significant increases of (a) 20 times and (b) 9 times, respectively. The achieved results showed that the high potential of organic devices used in optical wireless communications and it is a step forward to practically implement real time applications. REFERENCES [1] [2]
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