Nowadays, Visible Light Communication (VLC) is an attractive alternative technology for wireless communication because it can use some simple Light Emitting Diodes (LEDs) instead of antennas. Typically, indoor VLC is designed to transmit only one dataset through multiple LED beams at a time. As a result, the number of users per unit of time (throughput) is relatively low. Therefore, this paper proposes the design of an indoor VLC system using switched-beam technique through computer simulation. The LED lamps are designed to be arranged in a circular array and the signal can be transmitted through the beam of each LED lamp with the method of separating the dataset to increase the number of simultaneous users for enhancing the indoor VLC. The coverage area is determined from the area where the communication can be performed at a location on the receiving plane with a Bit Error Rate less than or equal to the specified value based on coverage illuminance according to International Commission on Illumination (CIE) standards. In this paper, Genetic Algorithm is used to find the suitable solution for designing parameters to achieve maximum coverage area. The results show that a Genetic Algorithm can be used to find a suitable solution and reduce the computational time approximately 382 min in proposed scenarios.
Visible Light Communication (VLC) is an interesting alternative technology because the current radio frequency communication system has limitations in data rate, insufficient bandwidth to increase the number of users, and interference among electromagnetic wave sources. So far, VLC technology demonstrates the next level of potential wireless communication in the near future because light can be easily used instead of using radio frequencies [
The VLC is one type of communication in which the data is transmitted by the modulation of light waves from the visible light spectrum with the wavelengths in the range of 380 nm–780 nm using LEDs. The reason for using LED to transmit data is because the LED is an electronic device that provides brightness, saves energy, and has a lifespan longer than traditional bulbs. This plays a significant role in smart lighting being able to provide both illuminations and also communication at the same time [
The structure of VLC consists of 3 main parts: transmitter, channel, and receiver, as shown in
Light has been used for communication from the past to the present. For example, a photophone was invented in the late 19th century by Alexander Graham Bell. The photophone has been considered as the beginning era of optical communication. The work started from sunlight reflection on the glass surface of the transmission sector, in which the light waves vibrate according to the user's voice. After that, light is sent to the lens which is a reflective curved glass installed in the receiver [
The VLC can be categorized as indoor VLC, such as communication in the office room, on the plane, or even in the hospital, etc., and outdoor VLC such as Vehicle-to-Vehicle communications (V2V) or Vehicle-to-Infrastructure communication (V2I) [
The optimal circular-array LED arrangement was presented to improve the uniformity of Illumination for the VLC system [
In addition, the optical beamforming technique can be used for enhancing the VLC system. The Spatial Light Modulator (SLM) uses optical beamforming to control the LED beam to be directly focused on the desired target device at the receiver [
The optical beamforming can be applied to multiple access techniques such as Space Division Multiple Access (SDMA) or Time Division Multiple Access (TDMA) to support multiple users in real situations [
Apparently, the use of optical beamforming techniques using SLM can significantly enhance communication performance. This can improve the quality of the received signal, but SLM devices are quite expensive, which is a limitation in terms of cost.
From the literatures, most of the traditional indoor VLC system transmit only one dataset through the LED beam per user at a time. As a result, the number of users per unit of time (throughput) is low. Therefore, this paper proposes the design of an indoor VLC system using switched-beam technique through MATLAB programing. The LED lamps are designed to be arranged in a circular array. The signal can be transmitted through the beam of each LED lamp with the method of separating the dataset to increase the number of simultaneous users enhancing the indoor VLC as shown in
The most popular method to optimize the uniform illuminance distribution in VLC system is based on specific location orientation of LED or the power allocation [
The calculation in this paper is considerably complex because the system has multiobjective optimization adjusting the designing parameters depending on both coverage area and condition of coverage illuminance. Therefore, heuristic optimization technique is applied to reduce complex calculation for finding the optimal path. Two types of optimization techniques are proposed; exhaustive search method and heuristic method. The exhaustive search examines every search point within the search space in order to get accurate optimal results but it takes a long time to calculate. So far, Genetic Algorithm is the most popular heuristic method. It is adopted to find the suitable solution for designing parameters to achieve maximum coverage area as it can effectively solve problems in large and complex data sets. Also, it can be used in VLC optimization problem [
The remainder of this paper is as follows. Firstly, the section of materials and methods discusses an array LED design, a proposed algorithm, and the simulation setup. The following section is results and discussion. Finally, the conclusion is given.
In this subsection, the calculation of the horizontal illuminance, the received optical power of directed light, Signal to Interference plus Noise Ratio (SINR), Bit Error Rate (BER), and the proposed LED-array design are discussed.
The horizontal illuminance: The illuminance expresses the distribution of an illuminated surface. A horizontal illuminance
The received optical power: In this paper, the only effect of directed light is considered, excluding the reflective light by walls. The received optical power
Signal to Interference Plus Noise Ratio: We design an array of LEDs to transmit different data for increasing the number of simultaneous users who can access the system at the same time. It results in interference among LED beams. The SINR can be calculated as [
Finally, The Bit Error Rate: BER can be calculated as [
The function
The accurate BER performance for wireless optical communication and indoor VLC system requires a BER of 10−3 [
LED-Array design: In the design of the LED installation, it is necessary to consider for the sufficient illumination, which must have the illuminance at least 300--2000 lux according to CIE [
The center LED: One LED is designed to be placed in the center of the area, as shown in
The surrounding LED: The remaining LEDs are designed to be tilted out from the center LED and arranged in a circular LED array. As a result, the angle of irradiance is changed as shown in
The beam coordinate is the coordinate in which the center beam of the LED falls on the receiving plane, as shown in
After designing the LED array, it is necessary to find the solution of designing parameters to achieve maximum possible communication area, also known as maximum coverage area. This paper adopts Genetic Algorithm to find the suitable solution for designing parameters with various simulation scenarios.
The simulation parameters in MATLAB programming are shown in
Parameters | Values |
---|---|
The number of LED array | 8, 9, 12 |
Single LED power (Watt) | 7, 10 |
Area size (m3) | 5 × 5 × 3, 4 × 5×3, 6 × 6 × 3 |
Height between source and receiver plane (m) | 2.15 |
Photodiode area (cm2) | 1 |
Refractive index at PD | 1.5 |
Photodiode responsivity (R) | 0.55 |
Field of view (FOV) (degree) | 60 |
This paper adopts a Genetic Algorithm to find the suitable solution for designing parameters mentioned above of each simulation scenario to achieve maximum coverage area. This is determined from the area where communication can be performed at BER less than or equal to 10−3 based on coverage illuminance that must cover greater than or equal 300 lux according to CIE standards. Moreover, Genetic Algorithm performance is analyzed by comparing the results with an exhaustive search.
The exhaustive search is an algorithm that examines every search point inside the search region. However, a large amount of computation is required. As a result, the algorithm has low efficiency because of a very large search space [
A Genetic Algorithm (GA) is a method for solving optimization problems based on a natural selection process that mimics biological evolution. GA was developed by John Holland and his assistant in 1975 [
The GA can be divided into five processes as follows: 1. Initiation 2. Fitness Function 3. Selection 4. Crossover and 5. Mutation. Initiation: The process begins with determining search space for the solution of designed parameters, which consists of
z-angle is the inclination angle in which the surrounding LEDs are inclined from the center LED. It is in the range of 1–70 degrees in the increments of 1 degree. semi-angle at half power is the angle of a diffusing lens covering an LED. It is in the range of 10–90 degrees in the increments of 5 degrees. radius of LED array. It is in the range of 0.1–1 meters in the increments of 0.1 meters.
The range of z-angle and radius of LED array are determined to be appropriate for the size of the room. It is considered that the LED beam can cover the entire area of the room. For semi-angle at half power range, the possible value of this angle is described in many scenarios [
Then, GA performs an individual element randomization which is called a population from the search space. Each individual element (chromosome) contains the solution of designed parameters (genes) as shown in
2. Fitness Function: This process begins with determining the ability of an individual element to compete with other individual elements. Each individual element (chromosome) is randomized by GA which is substituted in the Fitness Function to find the coverage area based on coverage illuminance according to CIE standards.
3. Selection: This process begins with selecting the fittest individual elements and passing their genes to the next generation. Individual elements with high fitness have more chances to be selected for reproduction. This step selects the individual element with the maximum coverage area to match, and prepare for crossover by determining the heavy weight for an individual element with a large coverage area to increase the chances of finding a better coverage area. Then, two pairs of individual elements (parents) are randomized based on weight for crossover to find the next generation.
4. Crossover: When each pair of parents is matched, a crossover point is randomized within the Genes by determining the crossover point in point 1 or 2. For example, at crossover point 1, two offspring appeared by the exchange of their parent genes becomes the next generation. The population has a fixed size. When the new generations are formed, individual elements with the least fitness are eliminated, providing space for a new generation.
5. Mutation: Some of genes can be subjected to a mutation with a low random probability in certain new offspring. This increases the chances of finding new and better values by randomizing the chance of mutation at every gene. If any gene has a chance of mutation, the values in that range of parameters will be randomized again. Moreover, each new generation is better than the previous generation, as shown in
The final process of GA brings a new generation back to Fitness Function for calculating the coverage area. Then, it checks if the maximum of duplicated coverage area is greater than or equal to five times. Also, if the accuracy is greater than or equal to 30%, the algorithm will terminate. The result is the suitable solution of designed parameters.
The simulation is designed for four scenarios as shown in The results from the adopted Genetic Algorithm which is used to find the solution for designing parameters mentioned above to achieve maximum coverage area. This is determined from the area where communication can be performed with a BER less than or equal to 10−3 based on coverage illuminance which must cover greater than or equal to 300 lux according to CIE standards. Comparison of the results between the Genetic Algorithm and an exhaustive search, which are used to find every search point inside the search space.
Scenario | Area size (m3) | The number of LEDs | Power per LED (Watt) |
---|---|---|---|
1 | 5 × 5 × 3 | 9 | 10 |
2 | 5 × 5 × 3 | 12 | 7 |
3 | 4 × 5 × 3 | 8 | 7 |
4 | 6 × 6 × 3 | 12 | 10 |
In this subsection, we run a number of simulation using the Genetic Algorithm and select the best solution after five-time running for each scenario as follows:
From 4 scenarios, for GA cases, each parameter in the figure is adjusted because GA is trying to adapt itself to the suitable solution of designed parameters, which achieves the maximum coverage area. Also, we can see that ever-increasing coverage area is obtained in each iteration until reaching the highest value. So, GA can find a suitable solution of designed parameters to achieve maximum coverage area.
Solution | Algorithm | |
---|---|---|
Exhaustive search | Genetic Algorithm | |
z-angle (°) | 35 | 35 |
Semi-angle (°) | 15 | 15 |
Radius (m) | 0.8 | 0.8 |
Coverage area (%) | 83.922 | 83.922 |
Time (minute) | 480 | 87 |
Solution | Algorithm | |
---|---|---|
Exhaustive search | Genetic Algorithm | |
z-angle (°) | 32 | 32 |
Semi-angle (°) | 15 | 15 |
Radius (m) | 1 | 1 |
Coverage area (%) | 73.622 | 73.622 |
Time (minute) | 480 | 87 |
Solution | Algorithm | |
---|---|---|
Exhaustive search | Genetic Algorithm | |
z-angle (°) | 41 | 41 |
Semi-angle (°) | 20 | 20 |
Radius (m) | 1 | 1 |
Coverage area (%) | 78.181 | 78.181 |
Time (minute) | 480 | 61 |
Solution | Algorithm | |
---|---|---|
Exhaustive search | Genetic Algorithm | |
z-angle (°) | 44 | 44 |
Semi-angle (°) | 15 | 15 |
Radius (m) | 0.6 | 0.6 |
Coverage area (%) | 73.677 | 73.677 |
Time (minute) | 480 | 157 |
Also, both algorithms provide coverage illuminance equal to 100%, which has the distribution of illuminance greater than or equal to 300 lux according to the CIE standards in every scenario, as shown in
The coverage area is determined from the area where the communication can be performed on the receiving plane with a BER less than or equal to 10−3 as shown in
Therefore, this paper proposes a method to increase the coverage area and reduce interference using the switched-beam technique. For example, Scenario 1 can support up to 9 simultaneous users, as shown in
The coverage area increases from 83.922% to 86.496% and 88.027%, as shown in
This paper has presented the design of an indoor VLC system using the switched-beam technique through MATLAB programing, which has designed the LEDs to be arranged in a circular array. The communication is performed through the beam of each LED. The Genetic Algorithm is adopted to find the suitable solution for designing parameters. From the simulation results, a Genetic Algorithm can be used to find the suitable solution for designing parameters of each simulation scenario with less computational time than an exhaustive search approximately 382 min in proposed scenarios. Also, the maximum coverage area based on coverage illuminance according to the CIE standards can be achieved. Moreover, the switched-beam technique can enhance indoor VLC by increasing the number of simultaneous users and the coverage area.