An indoor visible light imaging communication method combining RIS with light source optimization

Abstract

The present invention belongs to the technical field of visible light imaging communication and relates to an indoor visible light imaging communication method that combines a RIS (Reference Indicator) with light source optimization. A person's handheld mobile phone terminal uploads the model number of the mobile phone's CMOS image sensor to an online data analysis and optimization center (AP Controller) via an uplink wireless communication module for parameter search and analysis and optimization. The AP Controller controls the RIS to change the properties of a metasurface to ensure normal light passage or complete absorption, thereby optimizing the illumination of an indoor LED light source on an indoor receiving surface after passing through the RIS. While avoiding a reduction in the service life of the LED light source due to long-term direct adjustment of the light source, the person's handheld mobile phone can relatively well meet the full well capacity parameters of the CMOS image sensor at various locations indoors, thereby optimizing terminal imaging quality.

Description

Technical Field

[0001] The present invention belongs to the technical field of visible light imaging communication and relates to an indoor visible light imaging communication method for optimizing light sources in combination with RIS. Background Art

[0002] Visible light communication (VLC) technology offers exceptional security because visible light can only travel in straight lines. Therefore, only those in the line of sight can intercept information. Recently, thanks to the rapid development of LED lighting and the widespread adoption of mobile phones, visible light imaging communication (OCC) technology has rapidly developed. Compared to the high prices of visible light communication and infrared equipment, OCC technology is inexpensive and easily deployed in everyday life, utilizing only an LED light on the transmitting end and a mobile phone camera on the receiving end. Advances in semiconductor technology have led to the emergence of CMOS image sensors, which are embedded in many electronic devices. CMOS sensors utilize a rolling shutter mechanism, exposing only one line at a time. This method results in images of rapidly flashing LED lights appearing as alternating light and dark stripes. By decoding these stripes through grayscale values, information can be transmitted, completing the communication function. Consequently, the parameters of CMOS image sensors and image quality have become crucial criteria for measuring communication quality across the entire system. However, in recent years, theoretical research on visible light imaging communications has largely focused on image processing optimization and demodulation at the receiver end. Overlooked is the impact of the transmitting LED light source on the imaging of newly introduced CMOS image sensors. When photons strike an image sensor, those absorbed by the pixel plane are converted into electrons. These charges accumulate in the pixel's potential well. When the accumulation limit is reached, no further photoelectric conversion can occur. The maximum number of accumulated electrons is defined as the pixel's saturated electron capacity, or full well capacity. When pixels are saturated, photogenerated carriers generated by one or more pixels receiving illumination overflow into adjacent pixels, causing image distortion and irregular variations in fringe width, significantly increasing the BER. At the same time, a new challenge arises: OCC operates in real-world scenarios and is closely linked to users. Different users have different sensor parameters in their mobile phones. The entire system needs to analyze the user's image sensor parameters and dynamically adjust the light source, which is a significant challenge. This is because the transmitter LED optical components of traditional systems are non-configurable. Once deployed, their characteristics, such as light source position, field of view, amplification factor, and operating wavelength, cannot be dynamically adjusted.

[0003] Currently, there is a technological gap in optimizing the light source at the origin of indoor visible light imaging communication systems for mobile phone CMOS image sensors. Therefore, it is crucial to propose a bidirectional visible light imaging secure communication system that combines wireless communication and visible light imaging communication technologies for secure indoor information transmission. This system leverages the reported ability of reconfigurable intelligent surfaces (RIS) to control visible light propagation, providing either normal light transmission or complete absorption, and altering the state of the LED light source after it passes through the RIS, transforming the solid-state into a dynamic one to optimize indoor light sources. Summary of the Invention

[0004] To overcome the above-mentioned problems, the present invention provides an indoor visible light imaging communication method that combines RIS with light source optimization. This method is a method for indoor visible light imaging bidirectional secure communication that supports user mobility and combines RIS with light source optimization. It provides a new approach to the light source optimization standards and methods of current mobile phone terminal visible light imaging communication systems, utilizing the mobile phone terminal CMOS image sensor parameters as the standard for light source optimization and integrating the reconfigurable intelligent surface RIS into the OCC indoor scene. The entire system can adjust the light source parameters according to the different indoor mobile phone terminals, so that the user can obtain the best image with the least photon spillover phenomenon no matter where the handheld terminal moves to in the room, thereby realizing the adaptation of the indoor light source system to the mobile phone terminal.

[0005] An indoor visible light imaging communication method combining RIS with light source optimization includes the following:

[0006] Step 1: A person enters the room holding a mobile phone terminal and uploads the CMOS image sensor model in the mobile phone terminal to the AP Controller of the network data analysis and optimization center through the uplink wireless communication module;

[0007] Step 2: The AP Controller, a networked data analysis and optimization center, performs light source optimization based on the searched CMOS image sensor parameters. It calculates the Lambert coefficients of each new light source and then determines the optimal half-power angle. Ultimately, it determines the radius of the circular area on the RIS surface that allows the light source to pass normally. The details are as follows:

[0008] Step 2.1: In an indoor visible light communication system, for a LOS link, the illuminance at a point on the receiving surface is obtained as follows:

[0009]

[0010] Where I(0) is the central light intensity of the circular LED light source, φ is the radiation angle of the circular LED light source, is the incident angle of the circular LED light source to a certain point on the receiving surface, D is the distance between the circular LED light source and the CMOS image sensor, and the Lambert coefficient of the circular LED light source is

[0011] Among them, φ 1 / 2 is the half-power angle of the circular LED light source; assuming the circular LED i The coordinates of the light source on the ceiling of the room are (x i ,y i ,3), where 0<i≤n, n is the number of circular LED light sources, i Under the action of the light source, the illuminance at the indoor receiving surface position (x, y, g) is:

[0012]

[0013] Under the action of n circular LED light sources, the total illuminance at the indoor receiving surface position (x, y, g) is:

[0014]

[0015] Step 2.2: Convert the illuminance distribution of the circular LED light source at each position (x, y, g) on ​​the indoor receiving surface into the photon number distribution:

[0016] Irradiance and exposure time t exp Two parameters determine how many photons each pixel receives, and then the number of electrons received by the pixel is calculated through photoelectric conversion. Therefore, under the action of all circular LED light sources in the room, the number of pixel photons at each position (x, y, g) on ​​the indoor receiving surface is calculated as follows:

[0017]

[0018] Where h is Planck's constant, c is the speed of light, λ is the wavelength of the light wave, and A is the pixel area;

[0019] Step 2.3: Further, the number of electrons received by each pixel in the photo taken by the mobile terminal is calculated through photoelectric conversion. Under the action of all circular LED light sources in the room, the number of electrons e(x, y, g) at each position (x, y, g) on ​​the indoor receiving surface is obtained by the following formula:

[0020] e(x,y,g)=Qμ p (x,y,g)

[0021] Where Q is the photoelectric conversion efficiency;

[0022] Step 2.4: Under the action of all circular LED light sources in the room, the difference between the average number of electrons that can be received at each position of the indoor receiving surface and the average full well capacity value at each position of the indoor receiving surface is obtained by the following formula, i.e., the objective function:

[0023]

[0024] Where FWC is the full well capacity of the CMOS image sensor, Area is the area of ​​the room, and abs is the absolute value.

[0025] The constraint formula is as follows:

[0026] subject to(E 1los (K / 2,-P / 2,g)+E 2los (-K / 2,-P / 2,g)+E 3los (-K / 2,P / 2,g)

[0027] +E 4los (K / 2,P / 2,g)) / 4≥300

[0028]

[0029] Where K and P are the length and width of the rectangular ground area where the room is located;

[0030] The genetic algorithm is used to optimize the objective function to minimize the objective function, so that the optimized Lambert coefficient can be obtained, that is, the Lambert coefficient when the objective function is the smallest;

[0031] Step 2.5: Obtain the optimized Lambert coefficient of each circular LED light source, and then obtain the half-power angle of each circular LED light source, and finally obtain the new light-transmitting radius of the RIS surface. The details are as follows:

[0032] Reconstructed circular LED i The half-power angle of the light source is:

[0033]

[0034] where m i is the optimized Lambert coefficient of the i-th circular LED light source;

[0035] Then according to the Pythagorean theorem of triangles, we can get:

[0036]

[0037] Therefore, the light-transmitting radius of the RIS reconfigurable smart surface is:

[0038]

[0039] Where R is the radius of the circular LED light source; divided by this radius r i The RIS area outside the circular area completely absorbs light, with a radius of r i Light passes normally through the metasurface area within the circle, thereby controlling the light output of the circular LED light source;

[0040] Step 3: The AP Controller, a networked data analysis and optimization center, transmits the radius coefficient obtained in Step 2 to the control layer of the RIS. This controls the RIS to achieve regional changes in the properties of the metasurface to ensure normal light transmission and complete absorption, thereby optimizing the illumination of the indoor LED light source on the indoor receiving surface after passing through the reconfigurable smart surface.

[0041] Step 4: Two important factors affecting the saturation output of the CMOS image sensor are the radiant illumination of the light source and the exposure time of the image sensor. By using the reconfigurable intelligent surface (RIS) to adjust the LED illumination in real time, the full-well capacity parameters of the CMOS image sensor can be met even when a person holds their phone in any position indoors. This results in an optimal visible light image with minimal photon spillover.

[0042] In step 3, a central control unit is used at the AP Controller, the networked data analysis and optimization center, to transmit the new light-transmitting radius of the RIS surface obtained in step 2 as the optimal design parameter to the RIS control layer. The regional properties of the RIS surface, i.e., the metasurface, are controlled to adjust the illumination of the light source after it passes through the surface, thereby changing the illumination of the light source on the indoor receiving surface, ultimately completing the optimization of the light source.

[0043] Beneficial effects of the present invention:

[0044] This invention incorporates a reconfigurable intelligent surface (RIS) into a visible light imaging communication system and links LED light source parameters with CMOS image sensor parameters to serve as a criterion for light source optimization. Leveraging the previously reported ability of the RIS to control visible light propagation, it ensures either normal light transmission or complete absorption, thereby optimizing the illumination intensity of indoor LED light sources on receiving surfaces. This ensures that users holding mobile phones in various locations indoors can effectively meet the full well capacity parameters of the CMOS image sensor, thereby achieving optimal imaging at the terminal. This invention alleviates the problem of photon overflow during imaging, thereby reducing the difficulty of demodulating the light and dark fringes on rolling curtains at the receiving end. Furthermore, placing a reconfigurable intelligent surface (RIS) in front of the LED addresses the drawback of the unadjustable position and characteristics of LED optical components in indoor visible light imaging systems. By transforming the solid-state structure into a dynamic one, the RIS can be dynamically adjusted in real time based on parameters uploaded by the terminal via the uplink. Compared to frequently applying these changes directly to the LED light source itself, indirect adjustment using the RIS prevents a reduction in light source life. The light source itself requires no special processing; any commercially available LED light source can be used, making it universally applicable. At the same time, the RIS surface at the front end of the light source used in the present invention only has the effect of attenuating the light amplitude to 0 and allowing the light to pass normally. Compared with RIS surfaces that can provide arbitrary amplitude amplification or attenuation of light, the cost is lower. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings used in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the contents of the embodiments of the present invention and these drawings without paying any creative work.

[0046] Figure 1 This is an application scenario diagram of the present invention.

[0047] Figure 2 This is the ceiling position distribution diagram of the transmitting end light source of the present invention (typical rectangular layout).

[0048] Figure 3 This is a diagram showing the effect of optimizing a single light source, i.e., adjusting the state of the light source after it passes through the surface by changing the surface properties of the RIS.

[0049] Figure 4 It is the indoor system operation flow chart.

[0050] Figure 5 This is a model diagram of the visible light communication system.

[0051] Figure 6 This is a schematic diagram of a line-of-sight link.

[0052] Figure 7 Diagram of initial parameters for indoor visible light imaging communication system.

[0053] Figure 8 Simulation diagram of the indoor receiving surface illumination distribution.

[0054] Figure 9 Simulation diagram of the distribution of photon numbers obtained by the receiving surface of the indoor system.

[0055] Figure 10 Simulation diagram of the electron number distribution obtained by the indoor system receiving surface.

[0056] Figure 11 Schematic diagram of regional property adjustment of reconfigurable smart surface RIS.

[0057] Figure 12 Schematic diagram of the reconfigurable smart surface RIS adjusting the transmittance radius.

[0058] Figure 13 Simulation diagram of the illuminance distribution on the indoor receiving surface after optimization.

[0059] Figure 14 Simulation diagram of the photon number distribution obtained on the receiving surface of the indoor system after optimization.

[0060] Figure 15 Simulation diagram of the electron number distribution obtained on the receiving surface of the indoor system after optimization.

[0061] Figure 16 Schematic diagram of personnel mobility supported in indoor systems after optimization. DETAILED DESCRIPTION

[0062] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all structures.

[0063] This system utilizes a bidirectional link and includes a networked data analysis and optimization center (AP Controller), an LED driver circuit, multiple circular LED light sources arranged on the indoor ceiling, a reconfigurable smart surface (RIS) mounted in front of the light sources and matching their shape and size, and a mobile phone terminal with a rolling shutter exposure mode camera function. A person entering the room uses a mobile phone terminal to upload the model of the phone's CMOS image sensor to the networked data analysis and optimization center (AP Controller) via an uplink wireless communication module. The device then performs parameter search and optimization analysis. After the analysis is complete, the AP Controller controls the RIS to modify the metasurface's properties to ensure normal light transmission or complete absorption, thereby optimizing the illumination of the indoor LED light source on the receiving surface after it passes through the RIS. This prevents the lifespan of the light source from being shortened due to long-term direct adjustment of the LED light source, while ensuring that the full well capacity of the CMOS image sensor is met at all locations in the room, thereby optimizing terminal imaging quality. This not only alleviates the problem of photon overflow during imaging, but also reduces the difficulty of demodulating the rolling shutter light and dark fringes at the receiving end. Furthermore, the reconfigurable smart surface (RIS) provides normal light transmission or complete absorption, resolving the drawback of the unadjustable position and characteristics of indoor LED optical components, transforming solid-state into dynamic.

[0064] like Figure 1 As shown in the figure, the entire indoor visible light imaging communication system has a room size of 5m×5m×3m, which includes three parts: the ceiling light source part, the wireless communication module on the wall, and the Android mobile phone terminal on the ground. The position distribution of the ceiling light source is as follows: Figure 2 The figure shows a classic rectangular layout. For the downlink, as in all visible light imaging communication studies, the mobile phone terminal captures the light strip and demodulates it to recover the text information. Figure 3 The ceiling light source shown specifically includes an AP Controller (networked data analysis and optimization center), an LED driver circuit, a circular LED light source arranged on the indoor ceiling, and a reconfigurable smart surface (RIS) installed in front of the light source and consistent in shape and size with the light source. It illustrates the effect of optimizing a single light source, i.e., adjusting the state of the light source after passing through the surface by changing the surface properties of the RIS, using only a single light source as an example.

[0065] In this invention, a method for optimizing indoor visible light imaging communication light source based on the parameters of mobile phone terminal CMOS image sensor and combined with reconfigurable intelligent surface RIS is proposed. The system operation process is as follows: Figure 4 Shown include:

[0066] A person enters the room holding a mobile phone. The phone uploads the sensor model to the networked data optimization and analysis center AP Controller via the wireless communication module. After obtaining the sensor's full well capacity, exposure time, pixel size and other parameter data, a genetic algorithm is used to optimize the optimal light source Lambert coefficient and then the optimal half-power angle parameter. The properties of certain areas on the RIS surface are then adjusted. The steps include:

[0067] Step 1: A person enters the room holding a mobile phone terminal and uploads the CMOS image sensor model in the mobile phone terminal to the AP Controller of the network data analysis and optimization center through the uplink wireless communication module;

[0068] Step 2: The AP Controller, a networked data analysis and optimization center, performs light source optimization based on the searched CMOS image sensor parameters. It calculates the Lambert coefficients of each new light source and then determines the optimal half-power angle. Ultimately, it determines the radius of the circular area on the RIS surface that can normally pass the light source.

[0069] Step 3: The central control unit at the AP Controller, the networked data analysis and optimization center, transmits the radius coefficient obtained in Step 2 to the control layer of the RIS. This controls the RIS to achieve regional changes in the properties of the metasurface to ensure normal light transmission and complete absorption, thereby optimizing the illumination of the indoor LED light source on the indoor receiving surface after passing through the reconfigurable smart surface.

[0070] Step 4: Two important factors in the saturation output of the CMOS image sensor are the radiant illumination of the light source and the exposure time of the image sensor. By using the reconfigurable intelligent surface (RIS) to adjust the LED illumination in real time, the full-well capacity parameters of the CMOS image sensor can be well met when a person holds a mobile phone in any position indoors, thereby obtaining the optimal visible light imaging image with minimal photon spillover.

[0071] In step 2, the method for analyzing AP Controller parameters in the network data analysis optimization center includes the following:

[0072] The method for optimizing the light source according to the CMOS image sensor parameters to obtain the Lambert coefficients of each light source and then obtaining a new half-power angle of the circular LED light source includes the following steps:

[0073] Step 2.1: In an indoor visible light communication system, for the LOS link, such as Figure 5 、 Figure 6 As shown, the illuminance at a certain point on the indoor receiving surface is obtained as follows:

[0074]

[0075] Where I(0) is the central light intensity of the circular LED light source, φ is the radiation angle of the circular LED light source, is the incident angle of the circular LED light source to a certain point on the receiving surface (where the receiving surface refers to the horizontal plane where the receiver is located, which is the horizontal plane where the top surface of the mobile phone terminal is located in this case), D is the distance between the circular LED light source and the CMOS image sensor, and m is the Lambert coefficient of the circular LED light source:

[0076] Among them, φ 1 / 2 is the half-power angle of the circular LED light source; assuming the circular LED i The coordinates of the light source on the ceiling of the room are (x i ,y i ,3), where 0<i≤n, n is the number of circular LED light sources, then the illuminance at each position (x, y, g) of the indoor receiving surface under the action of the circular LED light source can be obtained:

[0077] E los (x,y,g)=I(0)(3-g) m+1 / [(3-g) 2 +(y i -y) 2 +(x i -x) 2 ] (m+3) / 2

[0078] Where g represents the height of a person holding a mobile phone from the ground. Specifically, it is the distance from the top of the phone to the ground. Because the front camera is used, people normally hold the phone at an angle, not flat. Therefore, the distance from the top of the phone should be used here.

[0079] There is more than one lamp in the room (take n lamps as an example, assuming that there are circular LEDs i The coordinates of the light source (0<i≤n) are (x i ,y i ,3), we need multiple lights in the room to coordinate and distribute the light intensity on the indoor receiving surface to be the best. The light intensity of each light on the indoor receiving surface is obtained by the following formulas:

[0080] Therefore, in the round LED i Under the action of the light source, the illuminance at the indoor receiving surface position (x, y, g) is:

[0081]

[0082] Finally, under the action of n circular LED light sources, the total illuminance at the indoor receiving surface position (x, y, g) is:

[0083]

[0084] according to Figure 7 The initial parameters of the indoor visible light imaging communication system are shown in Figure 1. The illumination distribution of the receiving surface of the whole room can be obtained by adding the illumination of n lights. Figure 8 shown.

[0085] Specifically:

[0086] There is more than one lamp in the room (take four lamps as an example, assuming the light source is LED i The coordinates of (i=1, 2, 3, 4) are (x i ,y i ,3), we need multiple lights in the room to coordinate and distribute the light on the indoor receiving surface to achieve the best distribution. The light intensity of each light on the receiving surface can be obtained by the following formulas:

[0087] Under the action of LED1, the illuminance at (x,y,g) in the room is:

[0088]

[0089] Under the action of LED2, the illuminance at (x,y,g) in the room is:

[0090]

[0091] Under the action of LED3, the illuminance at (x,y,g) in the room is:

[0092]

[0093] Under the action of LED3, the illuminance at (x,y,g) in the room is:

[0094]

[0095] Finally, under the action of four lights, the total illuminance at the indoor receiving surface position (x, y, z) is:

[0096] E los (x,y,g)=E 1los (x,y,g)+E 2los (x,y,g)+E 3los (x,y,g)+E 4los (x,y,g)

[0097] according to Figure 7 The initial parameters of the indoor visible light imaging communication system are shown in Figure 1. The illumination distribution of the receiving surface of the whole room can be obtained by adding the illumination of the four lights. Figure 8 shown.

[0098] Step 2.2: Convert the illuminance distribution of the circular LED light source at each position (x, y, g) on ​​the indoor receiving surface into the photon number distribution:

[0099] Irradiance (Irradiance is different from illuminance, but can be calculated based on illuminance E los Get irradiance) and exposure time t exp Two parameters determine how many photons each pixel receives; the number of electrons received by the pixel is then calculated through photoelectric conversion (the sensor's photoelectric conversion coefficient). Therefore, under the action of all circular LED light sources in the room, the number of pixel photons at each position (x, y, g) on ​​the indoor receiving surface is calculated as follows:

[0100]

[0101] Where h is Planck's constant, c is the speed of light, λ is the wavelength of the light wave, and A is the pixel area. The distribution of the number of photons received by the indoor system receiving surface is as follows: Figure 9 shown.

[0102] Step 2.3: Further, the number of electrons received by each pixel in the photo taken by the mobile terminal is calculated through photoelectric conversion (photoelectric conversion coefficient of the sensor). Under the action of all circular LED light sources in the room, the number of electrons e(x, y, g) at each position (x, y, g) on ​​the indoor receiving surface is obtained by the following formula:

[0103] e(x,y,g)=Qμ p (x,y,g)

[0104] Where Q is the photoelectric conversion efficiency. Linking the LED parameters to the CMOS image sensor's full-well capacity (saturated electron capacity) provides a theoretical basis for adjusting the RIS surface properties based on the type of mobile phone terminals entering the room, thereby controlling the illumination of the LED light source on the indoor receiving surface after passing through the RIS.

[0105] The distribution of the number of electrons obtained by the receiving surface of the indoor system is as follows: Figure 10 shown.

[0106] These charges will accumulate in the pixel potential well. When the upper limit of accumulation is reached, photoelectric conversion cannot be performed. The maximum number of accumulated electrons is defined as the saturated electron capacity of the pixel, that is, the full well capacity value, such as Figure 10 The plane shown is the full-well plane.

[0107] It can be seen that the number of electrons received by pixels in most areas in the center of the room is greater than the full well capacity, resulting in photon overflow, which has a significant impact on the imaging quality of mobile phone terminals.

[0108] Step 2.4: Under the action of all circular LED light sources in the room, the difference between the average number of electrons that can be received at each position of the indoor receiving surface and the average full well capacity value at each position of the indoor receiving surface is obtained by the following formula, i.e., the objective function:

[0109]

[0110] Where FWC is the full well capacity of the CMOS image sensor, Area is the area of ​​the room, and abs is the absolute value.

[0111] The constraint formula is as follows:

[0112] subject to(E 1los (K / 2,-P / 2,g)+E 2los (-K / 2,-P / 2,g)+E 3los (-K / 2,P / 2,g)

[0113] +E 4los (K / 2,P / 2,g)) / 4≥300

[0114]

[0115] Where K and P are the length and width of the rectangular ground area where the room is located;

[0116] Typically, when optimizing VLC light sources, the typical room model used is 5m x 5m x 3m (with a square floor). This OCC room model follows that typical model. However, a square floor is also acceptable, in which case K = P. The 4 here refers to the four corners of the room.

[0117] This embodiment uses a square ground, so the constraint formula is as follows:

[0118]

[0119]

[0120] The objective function describes the difference between the average number of electrons that can be received at each position of the indoor receiving surface and the average full well capacity of the indoor receiving surface; at the same time, the application goal of the visible light imaging communication system is integrated lighting. According to the provisions of the International Organization for Standardization (ISO), the room illumination should be limited to more than 300lx to meet the requirements of the human eye and ensure lighting conditions. The lowest indoor illumination is generally in the corner of the room. Therefore, we use the illumination at the corner to meet the lighting conditions as the constraint condition for genetic algorithm optimization. The side length of the square ground area is K meters, so the four corners The coordinates of the points are (K / 2, -K / 2), (-K / 2, -K / 2), (-K / 2, K / 2), (K / 2, K / 2); at the same time, the illumination uniformity (minimum illumination value / average illumination value) is used to describe the illumination distribution. The closer the illumination uniformity is to 1, the more uniform the light distribution is, and the better the visual experience of the human eye. Conversely, the smaller the illumination uniformity is, the more visual fatigue is increased. Therefore, the illumination uniformity of the core area of ​​the room (3K / 4 ≥ x ≥ -3K / 4, 3K / 4 ≥ x ≥ -3K / 4) must be greater than 0.7. Therefore, the two are used as two constraints of the objective function.

[0121] To minimize the above objective function, the objective function has four parameters m1, m2, m3, and m4 that need to be optimized, which are the Lambert coefficients of each circular LED light source. The optimization here is performed using a genetic algorithm; the genetic algorithm will traverse the value range of these four parameters and find the four Lambert coefficient values ​​that can minimize the objective function, thus obtaining the optimized Lambert coefficients of each light source.

[0122] Step 2.5: Obtain the optimized Lambert coefficient of each circular LED light source, and then obtain the half-power angle of each circular LED light source, and finally obtain the new light-transmitting radius of the RIS surface. The details are as follows:

[0123] Lambert coefficient m after optimization by genetic algorithm i (i=1, 2, 3, 4 represent four different lamps respectively), and the Lambert formula is known to be in the following form:

[0124]

[0125] Here we introduce a new reconstructed circular LED i The half-power angle of the light source is:

[0126]

[0127] where m i is the optimized Lambert coefficient of the i-th circular LED light source;

[0128] Then as Figure 11 As shown, according to the Pythagorean theorem of triangles:

[0129]

[0130] Therefore, the light-transmitting radius of the RIS reconfigurable smart surface is:

[0131]

[0132] Where R is the radius of the circular LED light source; Figure 11 As shown, divided by the radius r i The RIS area outside the circular area completely absorbs light, with a radius of r i Light normally passes through the metasurface area within the Figure 12 Schematic diagram of adjusting the light transmittance radius for RIS to control the light output of the circular LED light source;

[0133] Step 2.6: The centralized AP uses a central control unit to transmit the new light-transmitting radius of the RIS surface obtained in Step 2.5 as the optimal design parameter to the RIS control layer. The RIS surface, or metasurface, is controlled to adjust the illuminance of the light source after it passes through the surface (normal light transmission or complete absorption), thereby changing the illuminance of the light source on the indoor receiving surface, ultimately completing the optimization of the light source.

[0134] The front end of the LED light source is covered with a RIS surface. RIS is a new physical material device that can control changes in surface properties. For example, if a beam of light shines on it, the light can be magnified or reduced after passing through it. Therefore, by adjusting the regional properties of the RIS surface, the state of the light after passing through the surface is controlled. The LED light is originally the same as before the adjustment, but by adjusting the properties of the RIS covering the front end of the LED, the light passes through the RIS surface differently than before. Everything is done to determine the light transmission radius. Light within this radius passes normally, while light outside this radius is completely absorbed. Therefore, by covering the front end of the LED with this new material, the state of the LED after passing through the surface can be adjusted according to needs.

[0135] After all steps are completed, you can see the optimized indoor receiving surface illuminance distribution as follows Figure 13 As shown, the number of photons and electrons distributed on the indoor receiving surface is as follows Figure 14 、 15 As shown in the figure, it can be seen that the area of ​​the room that can affect the terminal imaging quality has been greatly reduced.

[0136] Finally, the indoor light source system is adapted to the mobile phone terminal. Figure 16 The person shown can move freely indoors with a mobile phone in hand to complete downlink visible light imaging secure communication.

[0137] The preferred embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the scope of protection of the present invention is not limited to the specific details of the above embodiments. Within the technical concept of the present invention, any technician familiar with the technical field can make equivalent replacements or changes based on the technical solution and inventive concept of the present invention within the technical scope disclosed by the present invention. These simple variations all fall within the scope of protection of the present invention.

[0138] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any appropriate manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0139] In addition, the various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

Claims

1. An indoor visible light imaging communication method combining RIS with light source optimization, characterized in that: Includes the following: Step 1: A person enters the room holding a mobile phone terminal and uploads the CMOS image sensor model in the mobile phone terminal to the AP Controller of the network data analysis and optimization center through the uplink wireless communication module; Step 2: The AP Controller, a networked data analysis and optimization center, performs light source optimization based on the searched CMOS image sensor parameters. It calculates the Lambert coefficients of each new light source and then determines the optimal half-power angle. Ultimately, it determines the radius of the circular area on the RIS surface that allows the light source to pass normally. The details are as follows: Step 2.1: In an indoor visible light communication system, for a LOS link, the illuminance at a point on the receiving surface is obtained as follows: Where I(0) is the central light intensity of the circular LED light source, φ is the radiation angle of the circular LED light source, is the incident angle of the circular LED light source to a certain point on the receiving surface, D is the distance between the circular LED light source and the CMOS image sensor, and m is the Lambert coefficient of the circular LED light source: Among them, φ 1 / 2 is the half-power angle of the circular LED light source; assuming the circular LED i The coordinates of the light source on the ceiling of the room are (x i ,y i ,3), where 0<i≤n, n is the number of circular LED light sources, i Under the action of the light source, the illuminance at the indoor receiving surface position (x, y, g) is: Then, under the action of n circular LED light sources, the total illuminance at the indoor receiving surface position (x, y, g) is: Step 2.2: Convert the illuminance distribution of the circular LED light source at each position (x, y, g) on ​​the indoor receiving surface into the photon number distribution: Irradiance and exposure time t exp Two parameters determine how many photons each pixel receives; the number of electrons received by the pixel is then calculated through photoelectric conversion. Therefore, under the action of all circular LED light sources in the room, the number of pixel photons at each position (x, y, g) on ​​the indoor receiving surface is calculated as follows: Where h is Planck's constant, c is the speed of light, λ is the wavelength of the light wave, and A is the pixel area; Step 2.3: Further, the number of electrons received by each pixel in the photo taken by the mobile terminal is calculated through photoelectric conversion. Under the action of all circular LED light sources in the room, the number of electrons e(x, y, g) at each position (x, y, g) on ​​the indoor receiving surface is obtained by the following formula: e(x,y,g)=Qμ p (x,y,g) Where Q is the photoelectric conversion efficiency; Step 2.4: Under the action of all circular LED light sources in the room, the difference between the average number of electrons that can be received at each position of the indoor receiving surface and the average full well capacity value at each position of the indoor receiving surface is obtained by the following formula, i.e., the objective function: Where FWC is the full well capacity of the CMOS image sensor, Area is the area of ​​the room, and abs is the absolute value. The constraint formula is as follows: subject to(E 1los (K / 2,-P / 2,g)+E 2los (-K / 2,-P / 2,g)+E 3los (-K / 2,P / 2,g) +E 4los (K / 2,P / 2,g)) / 4≥300 Where K and P are the length and width of the rectangular ground area where the room is located; The genetic algorithm is used to optimize the objective function to minimize the objective function, so that the optimized Lambert coefficient can be obtained, that is, the Lambert coefficient when the objective function is the smallest; Step 2.5: Obtain the optimized Lambert coefficient of each circular LED light source, and then obtain the half-power angle of each circular LED light source, and finally obtain the new light-transmitting radius of the RIS surface. The details are as follows: Reconstructed circular LED i The half-power angle of the light source is: where m i is the optimized Lambert coefficient of the i-th circular LED light source; Then according to the Pythagorean theorem of triangles we can get: Therefore, the light-transmitting radius of the RIS reconfigurable smart surface is: Where R is the radius of the circular LED light source; divided by this radius r i The RIS area outside the circular area completely absorbs light, with a radius of r i Light passes normally through the metasurface area within the circle, thereby controlling the light output of the circular LED light source; Step 3: The AP Controller, a networked data analysis and optimization center, transmits the radius coefficient obtained in Step 2 to the control layer of the RIS. This controls the RIS to achieve regional changes in the properties of the metasurface to ensure normal light transmission and complete absorption, thereby optimizing the illumination of the indoor LED light source on the indoor receiving surface after passing through the reconfigurable smart surface. Step 4: Two important factors affecting the saturation output of the CMOS image sensor are the radiant illumination of the light source and the exposure time of the image sensor. By using the reconfigurable intelligent surface (RIS) to adjust the LED illumination in real time, the full-well capacity parameters of the CMOS image sensor can be met even when a person holds their phone in any position indoors. This results in an optimal visible light image with minimal photon spillover.

2. The indoor visible light imaging communication method for light source optimization combined with RIS according to claim 1, characterized in that: In step 3, a central control unit is used at the AP Controller, the networked data analysis and optimization center, to transmit the new light-transmitting radius of the RIS surface obtained in step 2 as the optimal design parameter to the RIS control layer. The regional properties of the RIS surface, i.e., the metasurface, are controlled to adjust the illumination of the light source after it passes through the surface, thereby changing the illumination of the light source on the indoor receiving surface, ultimately completing the optimization of the light source.

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