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Abstract
We demonstrated a diversity-reception lens-free underwater wireless optical communication system employing a 2×2 solar panel array as detectors. The respective relationships between solar panel sizes and photocurrents, output voltages, system bandwidths were studied theoretically and experimentally. The signals output from the array were combined via maximum ratio combining in order to improve the signal quality. A 450-nm blue laser was used in the transmitter and a light spot with a size of 20mm×35mm was formed on the receiving plane after a 7-m transmission in tap water. With the solar panel array, a data rate of 84 Mbps was achieved with a bit error rate of 2.17 × 10−3 using 16-QAM OFDM signal. Meanwhile, a single solar panel with the same size as the array only achieved 60 Mbps. Solar panel array can bring about 40% data rate improvement. With the same detection area, the horizontal detection range of detectors increased from 37 mm for single solar panel to 55 mm for solar panel array thanks to the diversity reception. The results show that solar panel array using maximum ratio combining can enhance the transmission data rate as well as the detection range. The impacts of air bubbles, water fluctuation and microscopic particulates suspension on the proposed solar panel array-based system were also investigated. The results show that the received power sensitivity of solar panel array is 5.22 dB higher than single solar panel with the same detection area with a data rate of 60 Mbps and a BER of 10−3.
© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
1. Introduction
Since around 71 percent of the Earth's surface is covered by ocean, underwater exploration has always been a popular topic worldwide. Establishing an underwater communication network is of great importance for underwater exploration. In 1963, S. Q. Duntley discovered the existence of a blue-green window, showing that light with a wavelength near 480 nm suffers less attenuation in the sea [1] and started underwater wireless communications using visible light. Previously, acoustic waves have been used to build underwater communication links of a range up to tens of kilometers. Due to the bandwidth limitation of acoustic channels and low propagation speed of acoustic waves, emerging underwater wireless optical communication (UWOC) became a more promising alternative in the near-range high-capacity scenarios [2–5].
At present, in the field of UWOC, researchers mainly aim to increase the data rate or to extend the transmission distance [6–8]. Photodiodes like positive–intrinsic–negative (PIN) diodes and avalanche photodiodes (APDs) with large bandwidth are the most commonly used detectors in UWOC. However, in underwater environments, when using traditional photon detectors, the precise alignments and the retention of the communication link are extraordinary difficult due to the water fluctuation and vehicle shaking. In addition, photodiodes require external power supply to provide reverse voltage, which may arouse some problems in the scenarios where the energy supply is limited and battery maintenance is very inconvenient. Adopting solar panel as the detector can help overcome the two problems by leveraging its large detection area and its ability of energy harvesting. Moreover, the large detection area of solar panel can potentially alleviate the problem of light spot spread caused by scattering and turbulence. One practical application scenario is the data transmission between underwater sensor nodes and small autonomous underwater vehicles (AUVs), in which precise link alignment cannot be easily established and the transmission data rate is not necessarily very high (about tens of Mbps). Another issue that UWOC may need to deal with is the mobility due to the movement of AUV. The mobility induced penalty was studied in [9], and it was shown that with precoding schemes, the robustness the mobile UWOC systems can be enhanced. It is then appropriate to employ solar panel to relax the requirement for link alignment and to prolong the service life of batteries.
In [10], we have investigated the benefits of a lens-free solar panel with a large receiving area of 5 cm2 and a receiving angle of around 20°. A data rate of 22.56 Mbps has been achieved by using 64-QAM OFDM signal over a 7-m underwater channel. Spatial diversity techniques such as array structure and multiple-input multiple-output (MIMO) techniques can provide an attractive performance enhancement [11–13]. Employing diversity reception and maximum ratio combining (MRC) algorithm on a solar panel array can help improve the system performance effectively. Diversity techniques are generally used to mitigate the deep fading and intensity fluctuation caused by optical turbulence either in atmosphere or underwater [14–16]. Thus, solar panel array can further enhance the reliability of the communication system.
In this paper, we propose a diversity-reception underwater wireless optical communication system with a 2×2 solar panel array consisting of four solar panels with a size of 10mm×10 mm each as the detectors. The solar panels used in this paper have a small detection area, which leads to a larger output voltage when receiving the same optical power. Besides, diversity reception is used to further improve the SNR. We employ a single solar panel with the same detection area as the array for a fair comparison. The four output signals of the array are combined using the MRC algorithm to improve signal quality. For all we know, it is the first time that solar panel array is employed for underwater optical wireless communication. Over a 7-m tap-water channel, a data rate of 84 Mbps is achieved with a bit error rate (BER) of $2.17 \times {10^{ - 3}}$ using 16-QAM OFDM modulation format. A data rate of 75 Mbps is achieved with a BER of $3.63 \times {10^{ - 3}}$ using 32-QAM OFDM signal. Then, a 16-QAM OFDM signal with a bandwidth of 15 MHz is used to test the horizontal detection ranges of the detectors. For a light spot size of about 20mm×35 mm, with the BER kept under $3.80 \times {10^{ - 3}}$, the horizontal detection range of the solar panel array could reach about 55 mm, while the single solar panel with a size the same as the array only reaches 37 mm. We confirm that the proposed system is particularly robust to air bubbles and water fluctuation. Magnesium hydroxide powder is gradually added to water to investigate the effect of microscopic particulates suspension to the system.
The rest of the paper is organized as follows. Section 2 introduces the principles of solar panel and maximum ratio combining. In Section 3, we describe the proposed diversity-reception communication system. The experimental results of the proposed system are presented and analyzed in Section 4. Finally, Section 5 concludes the paper.
2. Principles of solar panel and maximum ratio combing
In this proposed system, a 2×2 solar panel array was employed as the detector, while a single solar panel with the same structure and the same size as the array was used for comparison. The four output electrical signals of the array were combined using the MRC algorithm.
2.1 Principle of solar panel
Solar panel is not commonly used as a detector in underwater wireless optical communication systems due to its low bandwidth. However, it can greatly reduce the alignment problem in some scenarios requiring medium transmission rate because of its large detection area.
Generally, a solar panel is defined as a large-area p-n junction made from silicon. When the input optical power is not saturated, the photocurrent of solar panel can be obtained using the formula of a photodiode as follows:
According to Eq. (1), for solar panels with the same doping concentration and the same depth but different sizes, the output photocurrents should be theoretically the same for a given input optical power.
The equivalent single potential model of solar panel for energy harvesting had been put forward for a long time [17], as shown in Fig. 1. Using such model, the relationship between output current and voltage can be denoted by Eq. (2):
The short-circuit current ${I_{\textrm{sc}}}$ can be obtained by setting the output voltage V to zero. Assume that ${R_\textrm{s}}$ is negligibly small, thus:
Similarly, we can find the open-circuit voltage ${V_{\textrm{oc}}}$:A solar panel model that collects AC signals was given in [22], as shown in Fig. 2. A capacitor C is introduced to represent the internal capacitive effects of the solar cell. The equivalent resistor r is used to replace diode D. The inductance of any wire connections to the solar panel is modelled using the inductor L. Since only the AC component of the signal is required for communication, a capacitor ${C_0}$ is inserted to block the DC component of the signal. The corresponding frequency response of the solar panel for communication is given by Eq. (9) [22]:
For identical solar panels, ${R_{\textrm{sh}}}$, ${R_\textrm{s}}$ and r are inversely proportional to the size of the solar panel, while C is proportional to the size [21]. The parameters of electronic components obtained in [22] are used to simulate the frequency responses of solar panels with different sizes and load resistors, as shown in Fig. 3. m represents the multiple of the size of a solar panel. Figure 3 shows that when applied a small load resistor, solar panel with a larger size has a smaller bandwidth. When the load resistor increases, the bandwidth becomes smaller. After it reaches a sufficiently large value, the size of solar panel is no longer a factor that effects the bandwidth.
Fig. 3. Simulation result on normalized frequency response of solar panels with different sizes (m) and load resistors (RL).
2.2 Principle of maximum ratio combining
The system with a solar panel array as the detector can be regarded as a single-input multiple-output (SIMO) system. In this system, noises in the received signals include thermal, shot, background and dark current noise. When there is a misalignment between the detectors and the transmitter, there will be a large difference in SNR among different solar panels. MRC is a diversity combiner that combines the received signals linearly in order to improve the SNR of the output signal [23]. The received signal of the $i\textrm{ - th}$ detector is:
where ${h_i}$ is the channel attenuation, x is the signal and ${n_i}$ is the noise.The combined signal using MRC is given by:
where N is the number of the receivers, and ${w_i}$ is the weight of the $i\textrm{ - th}$ received signal, which is determined according to its SNR.3. Experimental setup
In this section, the experimental setup of the proposed diversity-reception communication system is described, as shown in Fig. 4, with the water tank, the received spot and the detectors depicted. The QAM-OFDM signals generated by MATLAB were loaded to an arbitrary waveform generator (AWG) (Tektronix AWG70002A). The sample rate of the AWG was 62.5 MSample/s and the amplitude of the output signal was 0.5 Vpp. A 25-dB amplifier (AMP) (Mini-Circuits ZHL-6A-S+) and a variable electrical attenuator (VEA) were used to adjust the amplitude of the AWG output. After that, the signal was combined with a direct current (DC) bias via a bias tee and then coupled to a 450-nm blue laser (NDB7875). After transmitting through a 7-m water tank filled with tap water, a 20mm×35mm light spot was formed on the receiving plane. The light spot was not uniform, which can be seen from Fig. 4(ii). As the light spot had a wide area, a solar panel array with a large detection area was used to effectively receive the optical signal without a lens. A 2×2 solar panel array was employed, with a detection area of 10mm×10mm for each solar panel. For comparison, the solar panel array was replaced by a single solar panel with the same size (20mm×20mm). After passing through low pass filters (LPFs) (Mini-Circuits SLP-600+) and 25-dB AMPs (Mini-Circuits ZHL-6A-S+), the output signals of solar panel array were recorded by a mixed-signal oscilloscopes (MSO) (Tektronix MSO 71254C) with a sampling rate of 625 MSample/s. The recorded four data streams were further sent to a computer for MRC processing, demodulation and BER calculation.
Fig. 4. Experimental setup of the proposed diversity reception communication system. Insets: (i) the 7-m water tank, (ii) the received spot, (iii) the solar panel array and (iv) the single solar panel.
The responsivity curves of the solar panels in the experiment are depicted in Fig. 5. S1 to S4 are solar panels, with a size of 10mm×10mm each, and S5 is a solar panel with a size of 20mm×20mm, as shown in the insets of Fig. 4. The five curves in Fig. 5 are almost overlapped, implying that for the same input optical power, the short-circuit current of the solar panels with different sizes are almost the same. This agrees well with Eqs. (1) and (3). Figure 6 shows the relationship between the open-circuit voltage and short-circuit current. It can be seen that the solar panel with a larger detection area has a smaller open-circuit voltage, which is consistent with Eq. (7). Optical signals can be directly converted into voltage signals using solar panels. With solar panels, the receiver can be further simplified by removing the transimpedance amplifier (TIA) [22]. However, Fig. 6 shows a nonlinear relationship between the open-circuit voltage and the short-circuit current. Therefore, removing the TIA simplifies the system while introducing nonlinearity. The frequency response of each solar panel is shown in Fig. 7. Because of the low frequency noise, the measured frequency starts from 1.5 MHz. As shown in Fig. 4, the solar panels are connected to LPFs with an input impedance of 50 $\varOmega $. As discussed in Section 2.1, in this case, the size of solar panel is not the dominant factor of bandwidth. Thus, the bandwidth of each solar panel channel is almost the same, which can be confirmed by the experimental data in Fig. 7.
Fig. 6. Open-circuit voltage versus short-circuit current of different solar panels. Marker: Measurement; Dotted line: Fitting.
4. Results and discussion
To determine the optimal operation condition of the laser, we measured the error vector magnitudes (EVMs) under different bias currents and attenuation values of the VEA using a single solar panel S1 as the detector. Figure 8 shows the EVM performance of a 16-QAM OFDM signal with a bandwidth of 10 MHz. As illustrated in Fig. 8, the best EVM can be achieved with an attenuation of 0 dB and a bias current of 0.22 A. In the following experiments, the bias current and attenuation of VEA were fixed at 0.22 A and 0 dB, respectively. The measured transmitted and received optical power were 19.76 dBm and 15.03 dBm.
To find the highest data rate of the proposed diversity-reception communication system, we depicted the BER curves versus signal bandwidth. The beam spot was always in the center of the whole detection area during this experiment. Figure 9 shows the experimental results for Fig. 9(a) 16-QAM OFDM and Fig. 9(b) 32-QAM OFDM. Since the intensity distribution of light spot is uneven, the received optical power of the four solar panels S1 to S4 are not the same, leading to different BERs. It is shown that MRC can reduce the BER by around an order of magnitude, which increases the achievable data rate. By applying MRC, for 16-QAM OFDM signal, a data rate of 84 Mbps (corresponding to the signal bandwidth of 21 MHz) was achieved with a BER of $2.17 \times {10^{ - 3}}$. The data rate was about four times of that in the previous work [10]. Figure 10 shows the EVM of each subcarriers of MRC 84-Mbps 16-QAM OFDM signal. Due to the bandwidth limitation, EVM increases as the subcarrier number increases. A data rate of 75 Mbps (corresponding to the signal bandwidth of 15 MHz) was achieved using 32-QAM OFDM with a BER of $3.63 \times {10^{ - 3}}$. Meanwhile, the single solar panel with a size of 20mm×20mm can only achieve a data rate of about 60 Mbps and 55 Mbps with a BER of $2.29 \times {10^{ - 3}}$ using 16-QAM OFDM and $3.04 \times {10^{ - 3}}$ using 32-QAM OFDM.
Due to the strong directionality of the laser light, the establishment of a communication link is always a challenging issue in UWOC. A solar panel with a large detection area can greatly relieve the alignment problem. In addition, the implementation of diversity reception can further enhance the system performance compared with a single solar panel with the same detection area. We measured the tolerance of the system for misalignment by changing the horizontal position of the detector with a 20mm×35mm nonuniform light spot. Firstly, the center of the detection surface was covered by the light spot (corresponding to a horizontal offset of 0 mm in Fig. 11), and the horizontal position of detector was gradually moved with a step of 5 mm until the light spot was completely out of the surface of the detector. The BER curves of the 60-Mbps 16-QAM OFDM signal versus the horizontal offset are illustrated in Fig. 11. As the optical power received by the detector was gradually reduced, the system BER increased correspondingly. When a single solar panel was employed, the horizontal detection ranges for single solar panels S1 to S4 were not more than 41 mm, whereas the range for S5 was about 37 mm. The range for the solar panel array increased to about 55 mm after using MRC algorithm. Compared to the single solar panels with a size of 10mm×10mm, the solar panel array has an advantage of larger detection area. The solar panel array also has higher output voltage than the solar panels with a size of 20mm×20mm. By using the solar panel array with MRC algorithm, both the advantages of larger detection area and higher output voltage can be attained simultaneously. Therefore, we can enhance the transmission data rate as well as the detection range.
Fig. 11. BERs of the 60-Mbps 16-QAM OFDM signal when the solar panel is at different horizontal offset.
Previous experiments demonstrated the underwater wireless optical transmission through still tap water. In realistic environments, air bubbles, water fluctuation and optical turbulence can cause random fluctuations on the received optical signals [24]. In the presence of microscopic particulates suspension and dissolve organic matters in ocean waters, absorption and scatterings will bring intensity loss and pulse broadening of optical signal [25]. Therefore, further experiments were conducted in order to test the tolerance of the proposed system to air bubbles, water fluctuation and microscopic particulates suspension.
An off-the-shelf tunable air blower with the maximum capacity of 2 L/min was employed in the middle of the water tank to generate random air bubbles. In order to obtain the effect of air bubbles on system performance, we ran several experiments with different air bubble densities. A CCD camera beam profiler (Thorlabs BC106N-VIS/M) with an effective detection area of 8.77mm×6.6mm was placed in the center of the light spot to measure the light intensity distribution. The scintillation index of the received light measured by the beam profiler was used to characterize the density of air bubbles [24]. Figure 12(a) is the instantaneous light intensity distribution without air bubbles and Figs. 12(b)–12(e) correspond to the instantaneous light intensity distribution with the density of air bubbles increasing step by step until the air blower reaches its maximum power. The corresponding average optical power P and scintillation index ${\sigma ^2}$ are listed in Table 1. We can conclude that the presence of air bubbles will reduce the received optical power and increase the light intensity fluctuation. The BER curve of 60-Mbps 16-QAM OFDM signal versus scintillation index is shown in Fig. 13. It is obvious that the power reduction and intensity fluctuation caused by air bubbles can degrade the system performance. However, with the large detection area and the use of MRC, the proposed system can still maintain good performance with a large density of air bubbles. The BER was $7.90 \times {10^{ - 5}}$ when the scintillation index reached $9.69 \times {10^{ - 2}}$. It can be seen from Figs. 13(i) and 13(ii) that as the scintillation index increases, there is an overlap between the constellation points, but they are still clearly distinguishable. Then, we employed a 70 W water pump to generate the water fluctuation. The corresponding BERs of the four cases (a) still water, (b) with air blower, (c) with water pump and (d) with air blower and water pump were 0, $7.90 \times {10^{ - 5}}$, 0 and $4.75 \times {10^{ - 5}}$. The results show that individual water fluctuation generated by water pump has little effect on the proposed system. What's more, water fluctuation can reduce the air bubble density, leading to a lower BER compared with the case employing only air blower. Therefore, the proposed system has strong tolerance to air bubbles and water fluctuation.
Fig. 12. Instantaneous light intensity distribution of (a) still water with ${\sigma ^2}\textrm{ = }1.48 \times {10^{ - 3}}$; (b) bubbly water with ${\sigma ^2}\textrm{ = }5.20 \times {10^{ - 3}}$; (c) bubbly water with ${\sigma ^2}\textrm{ = }1.11 \times {10^{ - 2}}$; (d) bubbly water with ${\sigma ^2}\textrm{ = }2.66 \times {10^{ - 2}}$; (e) bubbly water with ${\sigma ^2}\textrm{ = }9.69 \times {10^{ - 2}}$.
Fig. 13. BERs of 60-Mbps 16-QAM OFDM signal when the scintillation index is affected by the air bubble density. Insets: constellation diagrams when (i) ${\sigma ^2}\textrm{ = }1.11 \times {10^{ - 2}}$, (ii) ${\sigma ^2}\textrm{ = }9.69 \times {10^{ - 2}}$.
Table 1. Mean and variance of optical power at different air bubble densities.
Finally, magnesium hydroxide powder was added to the water to imitate the suspended particles in ocean waters. Table 2 lists the correspondence between magnesium hydroxide powder concentration, received optical power and the attenuation coefficient. We measured the BERs of the 60-Mbps 16-QAM OFDM signal versus magnesium hydroxide powder concentration, as shown in Fig. 14. With the magnesium hydroxide powder concentration reached 3.015 mg/L (corresponding to the attenuation coefficient of 1.42 dB/m), the BER was $2.12 \times {10^{ - 3}}$, which was below the FEC threshold of $3.80 \times {10^{ - 3}}$. The BER was above the FEC threshold when the magnesium hydroxide powder concentration reached 3.397 mg/L (corresponding to the attenuation coefficient of 1.57 dB/m). As can be seen from Figs. 14(i) and 14(ii), the quality of constellation diagram decreases as the magnesium hydroxide powder concentration increases. The increasing optical power loss caused by absorption and scattering of the magnesium hydroxide powder results in the performance degradation of the proposed system. Note that the single solar panel with a size of 20mm×20mm achieved a BER of $2.29 \times {10^{ - 3}}$ using 60-Mbps 16-QAM OFDM signal with a received optical power of 15.03 dBm. Compared to that, the received power sensitivity of the proposed system with solar panel array and MRC can be improved by around 5.22 dB at the BER of ${10^{ - 3}}$.
Fig. 14. BERs of 60-Mbps 16-QAM OFDM signal versus magnesium hydroxide powder concentration. Insets: constellation diagrams when magnesium hydroxide powder concentration equal to (i) 0.643 mg/L, (ii) 3.397 mg/L.
Table 2. Received optical power and attenuation coefficient at different magnesium hydroxide powder concentrations.
5. Conclusion
In this paper, we propose and experimentally demonstrate a diversity-reception underwater optical communication system using solar panel array and MRC algorithm. A 2×2 solar panel array with a total detection area of 20mm×20mm is used as a detector that can enhance the data rate and alleviate the alignment problem simultaneously. Over a 7-m tap water channel, the highest data rate of 84 Mbps is achieved using 16-QAM OFDM with a BER of $2.17 \times {10^{ - 3}}$. For 32-QAM OFDM, a data rate of 75 Mbps is obtained due to limited SNR. When the data rate of the 16-QAM OFDM signal is set to 60 Mbps, we can achieve a horizontal detection range of about 55 mm using the solar panel array, when the spot size is about 20mm×35mm. For comparison, using a single solar panel of the same size gives a detection range of 37 mm only. The system maintains a fairly good performance after adding air bubbles and water fluctuation. By adding the magnesium hydroxide powder, we can find that the BER is under the FEC threshold of $3.80 \times {10^{ - 3}}$ when the attenuation coefficient reaches 1.42 dB/m. Using 60-Mbps 16-QAM OFDM signal, the received power sensitivity of the proposed system with solar panel array can be improved by 5.22 dB at the BER of ${10^{ - 3}}$ compared to single solar panel. This study demonstrates that solar panel arrays are highly desirable in the scenarios that require moderate data rate and low alignment complexity.
Funding
National Natural Science Foundation of China (61971378, 61671409, 61705190); Research Grants Council, University Grants Committee (GRF 14201818); National Key Research and Development Program of China (2016YFC1401202, 2017YFC0306100, 2017YFC0306601); Fundamental Research Funds for the Central Universities.
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