Multi-access point optical communication energy efficiency optimization methods, systems, equipment, products and media

Abstract

This invention relates to the field of optical communication, providing a method, system, device, product, and medium for optimizing the energy efficiency of multi-access point optical communication. The method includes: calculating the link channel gain to obtain the composite channel gain; obtaining the received energy at the receiver; establishing a nonlinear received energy expression based on the received energy; constructing a battery dynamics model using the nonlinear received energy expression to obtain device energy constraints; establishing a arrival Markov matrix and a service Markov matrix based on the number of participating communication devices and their obstruction states; calculating the arrival martingale and service martingale; calculating the queue length change based on the arrival martingale and service martingale; establishing a delay default probability inequality through the queue length change to obtain an upper bound on the delay default probability; establishing communication optimization constraints; constructing a multivariate optimization equation; solving the multivariate optimization equation based on the communication optimization constraints to obtain communication optimization parameters; and optimizing the optical communication using these communication optimization parameters.

Description

Technical Field

[0001] This invention relates to the field of optical communication technology, and in particular to methods, systems, devices, products and media for optimizing energy efficiency in multi-access point optical communication. Background Technology

[0002] Visible light communication, as a novel wireless communication technology, transmits data through the visible light band (380nm~780nm) of light-emitting diodes. Its core advantage lies in the deep integration of lighting and communication functions, without occupying scarce radio frequency spectrum resources, while possessing high bandwidth density, strong resistance to electromagnetic interference, and inherent security. With the increasing demand for large-scale device access in fields such as smart homes, industrial IoT, and smart healthcare, visible light communication has become an important supplementary technology for indoor network communication in sixth-generation mobile communication.

[0003] Therefore, visible light communication presents new challenges to resource allocation and energy management. In scenarios involving the collaborative deployment of multiple access points and multiple intelligent reflectors, factors such as signal power distribution, reflection path selection, and user scheduling significantly impact overall system performance. Improper resource allocation can lead to decreased communication rates or insufficient energy supply for some users, thereby reducing system energy efficiency and service quality. Simultaneously, visible light communication systems often face dual constraints of energy and latency. In energy-constrained IoT terminals or self-powered devices, excessively high power allocation will accelerate energy consumption and affect the continuity of uplink communication; while excessively low transmit power may increase transmission latency or cause queue backlog. How to achieve a dynamic balance between energy utilization efficiency and latency performance while ensuring data rate requirements becomes a key issue in the design of multi-access point visible light communication systems. Summary of the Invention

[0004] This invention aims to at least solve one of the technical problems existing in related technologies. To this end, this invention provides a method, system, device, product, and medium for optimizing the energy efficiency of multi-access point optical communication, achieving control over both delay rate and transmit power in optical communication, and optimizing the communication parameters of optical communication.

[0005] This invention provides a method for optimizing the energy efficiency of multi-access point optical communication, comprising:

[0006] S1: Deploy optical communication equipment, calculate the link channel gain of the optical communication equipment, and obtain the composite channel gain based on the link channel gain;

[0007] S2: Obtain the received energy at the receiver of the optical communication device, establish a nonlinear received energy expression based on the received energy, construct a battery dynamics model using the nonlinear received energy expression, and obtain the device energy constraint through the battery dynamics model;

[0008] S3: Determine the number of participating communication devices and their obstruction status, and establish the arrival Markov matrix and service Markov matrix based on the number of participating communication devices and their obstruction status.

[0009] S4: Calculate the arrival martingale based on the arrival Markov matrix and the composite channel gain, calculate the service martingale based on the service Markov matrix and the composite channel gain, calculate the queue length change based on the arrival martingale and the service martingale, establish the delay default probability inequality through the queue length change, and obtain the upper bound of the delay default probability based on the delay default probability inequality.

[0010] S5: Establish communication optimization constraints by using equipment energy constraints and upper bounds on delay default probability, construct multivariate optimization equations, solve the multivariate optimization equations according to the communication optimization constraints, obtain communication optimization parameters, and complete the optimization of optical communication using the communication optimization parameters.

[0011] According to the multi-access point optical communication energy efficiency optimization method provided by the present invention, step S1 further includes:

[0012] S11: Deploy optical communication equipment and calculate the direct link communication gain and reflected link communication gain of the optical communication equipment;

[0013] S12: The composite channel gain is obtained by the direct link communication gain and the reflected link communication gain.

[0014] According to the multi-access point optical communication energy efficiency optimization method provided by the present invention, step S2 further includes:

[0015] S21: Determine the optical communication composite signal, calculate the received energy at the receiving end based on the optical communication composite signal, determine the saturation power, and establish the nonlinear received energy expression based on the received energy at the receiving end and the saturation power.

[0016] S22: The battery dynamics model is established using the nonlinear received energy expression. The transmitted DC component and time slot energy consumption are obtained from the battery dynamics model. The energy constraint of the device is obtained through the transmitted DC component and time slot energy consumption.

[0017] According to the multi-access point optical communication energy efficiency optimization method provided by the present invention, in step S3, the number of participating communication devices, the device obstruction status, and the device Markov status are determined, and an arrival Markov matrix and a service Markov matrix are established according to the number of participating communication devices, the device Markov status, and the device obstruction status.

[0018] According to the multi-access point optical communication energy efficiency optimization method provided by the present invention, step S4 further includes:

[0019] S41: Perform an exponential column transformation on the arrival Markov matrix to obtain the arrival transformation matrix. Obtain the arrival exponential parameter and the arrival channel gain based on the arrival transformation matrix and the composite channel gain. Construct the arrival martingale using the arrival transformation matrix, the arrival exponential parameter, and the arrival channel gain.

[0020] S42: Perform an exponential column transformation on the service Markov matrix to obtain a service transformation matrix. Obtain the service exponential parameter and service channel gain based on the service transformation matrix and the composite channel gain. Construct the service martingale using the service transformation matrix, the service exponential parameter, and the service channel gain.

[0021] S43: Obtain the initial queue length change based on the arrival martingale and the service martingale, construct a critical parameter, and calculate the queue length change using the initial queue length change and the critical parameter;

[0022] S44: Construct a delay default parameter, and obtain the upper bound of the delay default probability based on the queue length change and the delay default parameter.

[0023] According to the multi-access point optical communication energy efficiency optimization method provided by the present invention, in step S5, the system energy efficiency is calculated, a timeout accumulation parameter and a reward / penalty coefficient are constructed, a timeout constraint condition is constructed through the upper bound of the timeout default probability, the timeout accumulation parameter and the reward / penalty coefficient, the communication optimization constraint condition is established through the timeout constraint condition and the equipment energy constraint condition, and the multivariate optimization equation is established through the system energy efficiency.

[0024] This invention also provides a multi-access-point optical communication energy efficiency optimization system, comprising:

[0025] Composite channel gain module: used to deploy optical communication equipment, calculate the link channel gain of the optical communication equipment, and obtain the composite channel gain based on the link channel gain;

[0026] The device energy constraint module is used to obtain the received energy of the optical communication device, establish a nonlinear received energy expression based on the received energy, construct a battery dynamics model using the nonlinear received energy expression, and obtain the device energy constraint through the battery dynamics model.

[0027] Markov matrix module: used to determine the number of participating communication devices and the device obstruction status, and to establish the arrival Markov matrix and service Markov matrix based on the number of participating communication devices and the device obstruction status;

[0028] The delay default probability upper bound module is used to calculate the arrival martingale based on the arrival Markov matrix and composite channel gain, calculate the service martingale based on the service Markov matrix and composite channel gain, calculate the queue length change based on the arrival martingale and service martingale, establish the delay default probability inequality through the queue length change, and obtain the delay default probability upper bound based on the delay default probability inequality.

[0029] The communication optimization parameter module is used to establish communication optimization constraints by means of equipment energy constraints and upper bound of delay default probability, construct multivariate optimization equations, solve the multivariate optimization equations according to the communication optimization constraints, obtain communication optimization parameters, and complete the optimization of optical communication through communication optimization parameters.

[0030] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above-described multi-access point optical communication energy efficiency optimization methods.

[0031] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the multi-access point optical communication energy efficiency optimization method described above.

[0032] The present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, and when the program instructions are executed by a computer, the computer is able to perform the steps of any of the above-described multi-access point optical communication energy efficiency optimization methods.

[0033] The above-described one or more technical solutions in the embodiments of the present invention have at least one of the following technical effects:

[0034] This invention provides a method, system, device, product, and medium for optimizing energy efficiency in multi-access point optical communication. It replaces the linear model, which deviates significantly from reality in traditional methods, with a nonlinear received energy expression, thereby constructing a battery dynamics model and obtaining device energy constraints. Arrival martingale and service martingale are used to represent the statistical characteristics of arrival and service states, thus constructing an upper bound on the delay default probability. By introducing device energy constraints and the upper bound on the delay default probability, the system's energy utilization and latency performance can be maximized while ensuring communication stability and service quality. This solution is not only applicable to high-density indoor communication environments but can also be extended to low-power IoT scenarios, providing a novel resource allocation strategy for energy efficiency optimization in future intelligent lighting, sensing, and communication convergence systems.

[0035] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0037] Figure 1 This is a flowchart illustrating the energy efficiency optimization method for multi-access point optical communication provided by the present invention.

[0038] Figure 2 This is a schematic diagram of the structure of the multi-access point optical communication energy efficiency optimization system provided by the present invention.

[0039] Figure 3 This is a schematic diagram of the structure of the multi-access point optical communication energy efficiency optimization device provided by the present invention.

[0040] Figure label:

[0041] 100. Composite channel gain module; 200. Device energy constraint module; 300. Markov matrix module; 400. Delay default probability upper bound module; 500. Communication optimization parameter module; 810. Processor; 820. Communication interface; 830. Memory; 840. Communication bus. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The following embodiments are used to illustrate this invention but cannot be used to limit the scope of this invention.

[0043] In the description of the embodiments of the present invention, it should be noted that the terms "first", "second" and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0044] In the description of the embodiments of the present invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in the embodiments of the present invention based on the specific circumstances.

[0045] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0046] The following is combined Figures 1 to 3 Specific embodiments of the present invention are described below. Figure 1 A flowchart illustrating the energy efficiency optimization method for multi-access point optical communication provided by the present invention includes:

[0047] S1: Deploy optical communication equipment, calculate the link channel gain of the optical communication equipment, and obtain the composite channel gain based on the link channel gain;

[0048] Furthermore, the objective of this stage is to calculate the link channel gain of the optical communication equipment, thereby obtaining the composite channel gain. Specifically, step S1 further includes:

[0049] S11: Deploy optical communication equipment and calculate the direct link communication gain and reflected link communication gain of the optical communication equipment;

[0050] S12: The composite channel gain is obtained by the direct link communication gain and the reflected link communication gain.

[0051] The specific implementation method for the above steps in this embodiment is as follows:

[0052] Firstly, optical communication equipment needs to be deployed indoors. In indoor long-timeslot visible light communication scenarios, this involves deploying optical communication equipment including multiple optical access points, multiple reconfigurable smart reflectors, and multiple user terminals. Each optical access point is installed on the ceiling, undertaking the dual functions of downlink data transmission and power supply. It can independently adjust the AC and DC components of the transmitted power through a power control unit, balancing information rate and energy harvesting efficiency. Multiple reconfigurable smart reflectors are distributed in key locations on the walls and ceiling to achieve multipath gain enhancement of the optical signal, enabling users who are blocked or in weak signal areas to still obtain an effective communication path.

[0053] In its implementation, the system employs a time-slotted design, dynamically scheduling resources within each time slot based on environmental conditions and user needs. A user allocation matrix is ​​introduced to determine the matching relationship between optical access points and users, while an intelligent reflector allocation matrix determines the links served by each reconfigurable intelligent reflector. The system calculates the direct link gain and reflected link gain based on the optical propagation geometry model, line-of-sight conditions, and non-line-of-sight conditions.

[0054] Specifically, for a channel where optical access points and user terminals communicate directly, the direct channel gain between the l-th optical access point and the k-th user receiver... for:

[0055]

[0056] in, The light access point is the Lambertian emission order of the LED light in this embodiment; The effective receiving area of ​​the photodiode at the user receiver end; This represents the propagation distance between the l-th optical access point and the k-th user receiver. The transmission coefficient of the optical filter at the user receiver at a specific light incident angle; The emission angle of the optical access point; The angle of light incidence at the user's receiver. This is the gain function of the optical condenser.

[0057] For the non-line-of-sight link portion, the channel gain of the reflected link from the l-th optical access point to the k-th user via the n-th smart reflector is... for:

[0058]

[0059] in, The reflection efficiency coefficient of the intelligent reflective surface. Let l be the distance between the l-th optical access point and the n-th smart reflector. Let n be the distance between the nth smart reflective surface and the kth user terminal. Let be the angle of incidence when light is emitted from the l-th light access point to the n-th smart reflector. Let be the angle of incidence of light from the nth smart reflector to the kth user terminal.

[0060] This allows us to obtain the composite channel gain by combining the direct link communication gain and the reflected link communication gain. :

[0061]

[0062] in, Parameters are assigned to the reflector of the l-th optical access point via the n-th smart reflector to the k-th user. Specifically, this parameter is 1 when the user and the reflector form a link, and 0 otherwise. Parameters are assigned to users from the l-th optical access point to the k-th user receiver. Similarly, the value is 1 when the k-th user and the l-th optical access point form a link, and 0 otherwise. N is the total number of smart reflectors.

[0063] S2: Obtain the received energy at the receiver of the optical communication device, establish a nonlinear received energy expression based on the received energy, construct a battery dynamics model using the nonlinear received energy expression, and obtain the device energy constraint through the battery dynamics model;

[0064] Furthermore, the objective of this stage is to establish a nonlinear received energy expression, thereby constructing a battery dynamics model and obtaining the device energy constraint. Specifically, step S2 further includes:

[0065] S21: Determine the optical communication composite signal, calculate the received energy at the receiving end based on the optical communication composite signal, determine the saturation power, and establish the nonlinear received energy expression based on the received energy at the receiving end and the saturation power.

[0066] S22: The battery dynamics model is established using the nonlinear received energy expression. The transmitted DC component and time slot energy consumption are obtained from the battery dynamics model. The energy constraint of the device is obtained through the transmitted DC component and time slot energy consumption.

[0067] The specific implementation method for the above steps in this embodiment is as follows:

[0068] First, it is necessary to determine the composite signal transmitted by each optical access point, that is, the composite optical communication signal transmitted by the l-th optical access point at time t. :

[0069]

[0070] Where K is the total number of optical access points. Let be the AC power from the l-th optical access point to the k-th user receiver. This refers to the signal information transmitted from the l-th optical access point to the k-th user receiver at time t. Let be the DC bias of the l-th optical access point at time t. Here, the DC bias is used to supply power to the signal, and the AC power is used to transmit information.

[0071] Once the optical communication composite signal is obtained, the energy received by the receiver can be calculated, which is the energy received by the k-th user receiver at time t. :

[0072]

[0073] Where L is the total number of optical access points. Let be the additive white Gaussian noise at time t.

[0074] User equipment (UE) uses a specific signal processing procedure to extract the AC component from the received composite signal for information decoding and the DC component for energy harvesting. This separation mechanism ensures that information transmission and energy harvesting can proceed in parallel without interference. Specifically, the DC component obtained from this process is used for energy harvesting at the k-th user receiver. for:

[0075]

[0076] Traditional research often simplifies energy harvesting processes into linear models, which deviates significantly from actual photoelectric conversion characteristics. Establishing a nonlinear energy harvesting model can more realistically reflect the energy conversion characteristics of actual systems. Here, it is necessary to determine the saturation power of the energy harvesting circuit. Thus, the expression for the nonlinear received energy is obtained:

[0077]

[0078] in, The threshold value is the start-up threshold; when the input power is below this value, the energy harvesting efficiency is very low. 'a' is a pre-set transition speed control parameter. Let be the power of the energy received by the k-th user receiver at time t.

[0079] This allows us to establish a battery dynamics model:

[0080]

[0081]

[0082]

[0083] in, The length of one time slot in optical communication This represents the total energy received by the k-th user receiver at time t, which is also the transmitted DC component. Let be the energy consumed by the k-th user receiver at time t for uplink transmission. Let be the energy consumed by the k-th user receiver at time t for signal processing. Let be the energy consumed by the circuitry of the k-th user receiver at time t, based on its fundamental power consumption. Let be the energy consumed by the k-th user receiver at time t, which is also the energy consumed in the time slot. The energy stored in the k-th user receiver at time t+1. Let be the energy stored in the k-th user receiver at time t. For charging efficiency, For discharge efficiency, Let be the self-discharge loss of the k-th user receiver at time t. The device energy constraint can be constructed based on the battery kinetic model.

[0084]

[0085] Where T represents the operating time of the optical communication equipment. The energy stored in the user's receiver at the moment t=0, which is when communication begins.

[0086] S3: Determine the number of participating communication devices and their obstruction status, and establish the arrival Markov matrix and service Markov matrix based on the number of participating communication devices and their obstruction status.

[0087] Furthermore, the objective of this stage is to establish an arrival Markov matrix and a service Markov matrix based on the number of participating communication devices and their obstruction status. Specifically, in step S3, the number of participating communication devices, their obstruction status, and their Markov states are determined, and an arrival Markov matrix and a service Markov matrix are established based on these parameters respectively.

[0088] The specific implementation method for the above steps in this embodiment is as follows:

[0089] First, it's necessary to determine the number of participating communication devices, i.e., the number of devices capable of participating in the optical communication process. This includes the number of intelligent reflectors that can participate in communication. Additionally, it's necessary to determine if the light transmission path of the participating devices is blocked, i.e., the device blocking state. Based on the number of participating devices, the state of each device can be determined, thus obtaining its Markov state. Furthermore, the communication environment can be assessed to predict whether blocking will occur in the next moment, and the Markov state in the next moment can also be predicted based on the blocking situation. A larger number of intelligent reflectors allows for more efficient load sharing, thus enabling more detailed division of Markov states. Changes in the number of participating intelligent reflectors can also cause changes in Markov states. In this embodiment, the number of states that an optical communication device can be in is assumed to be one more than the number of intelligent reflectors M capable of participating in communication. This allows the establishment of the service Markov matrix for the k-th user receiver during the communication process. :

[0090]

[0091] in, This refers to the transition probability of a user receiver in the Markov matrix transitioning from the i-th state at time t to the j-th state at time t+1, where the transition probability is calculated as follows:

[0092]

[0093] in, This expresses the probability of the preceding event occurring given that the subsequent event within the parentheses has occurred. This represents the indicator function. An indicator function is defined as follows: when the event specified in the indicator function occurs, the function takes a value of 1; otherwise, it takes a value of 0. This indicates that occlusion occurs at time t+1. This indicates that occlusion did not occur at time t+1. This indicates that the Markov state of the user receiver at time t is the i-th state. The number of intelligent reflective surfaces participating in communication at time t is j. And for the Markov matrix... In other words, its form is:

[0094]

[0095] in, Let be the transition probability of the user receiver in the Markov matrix transitioning from the i-th state at time t to the j-th state at time t+1, where the transition probability is calculated as follows:

[0096]

[0097] in, Let t+1 represent the Markov state of the user receiver at time t+1, which is the j-th state. This allows us to obtain the arrival Markov matrix. In practical applications, the arrival Markov matrix is ​​obtained first, followed by the service Markov matrix.

[0098] S4: Calculate the arrival martingale based on the arrival Markov matrix and the composite channel gain, calculate the service martingale based on the service Markov matrix and the composite channel gain, calculate the queue length change based on the arrival martingale and the service martingale, establish the delay default probability inequality through the queue length change, and obtain the upper bound of the delay default probability based on the delay default probability inequality.

[0099] Furthermore, the objective of this stage is to calculate the arrival martingale and the service martingale, thereby calculating the queue length change and obtaining the upper bound of the delay default probability. Specifically, step S4 further includes:

[0100] S41: Perform an exponential column transformation on the arrival Markov matrix to obtain the arrival transformation matrix. Obtain the arrival exponential parameter and the arrival channel gain based on the arrival transformation matrix and the composite channel gain. Construct the arrival martingale using the arrival transformation matrix, the arrival exponential parameter, and the arrival channel gain.

[0101] S42: Perform an exponential column transformation on the service Markov matrix to obtain a service transformation matrix. Obtain the service exponential parameter and service channel gain based on the service transformation matrix and the composite channel gain. Construct the service martingale using the service transformation matrix, the service exponential parameter, and the service channel gain.

[0102] S43: Obtain the initial queue length change based on the arrival martingale and the service martingale, construct a critical parameter, and calculate the queue length change using the initial queue length change and the critical parameter;

[0103] S44: Construct a delay default parameter, and obtain the upper bound of the delay default probability based on the queue length change and the delay default parameter.

[0104] The specific implementation method for the above steps in this embodiment is as follows:

[0105] Here, an exponential column transformation is performed on the arrival Markov matrix to obtain the arrival transformation matrix of the k-th user receiver. Then, the arrival exponent parameter of the k-th user receiver is obtained. , The transform parameters represent the exponential transform. The arrival channel gain of the k-th user receiver at time t is obtained from the composite channel gain at signal arrival. , among them This represents the number of parameter packets arriving at time t. The arrival channel gain at all times within the time interval of a single signal arrival constitutes the arrival channel correction function for the k-th user receiver. The arrival channel correction function and the arrival exponent parameter satisfy the following relationship:

[0106]

[0107]

[0108] Here, SP() denotes calculating the spectral radius within the parentheses. This allows us to obtain the arrival martingale of the k-th user receiver at time t. :

[0109]

[0110] in, This represents the total number of parameter packets received by the k-th user receiver up to time t.

[0111] Similarly, parsing the received parameter packets, which is the process of providing services, involves performing an exponential transformation on the service Markov matrix to obtain the service transformation matrix for the k-th user receiver. Then, the service index parameter of the k-th user receiver is obtained. The service channel gain of the k-th user receiver at time t is obtained based on the composite channel gain during service. , among them This represents the number of parameter packets served at time t. The service channel gain at all times within the duration of one signal service session constitutes the service channel correction function for the k-th user receiver. The service channel correction function and the service index parameter satisfy the following relationship:

[0112]

[0113]

[0114] This allows us to obtain the service martingale of the k-th user receiver at time t. :

[0115]

[0116] in, This represents the total number of parameter packets served by the k-th user receiver up to time t.

[0117] The initial queue length change of the k-th user receiver at time t is obtained based on the arrival martingale and the service martingale. :

[0118]

[0119] Here, the critical parameters for the k-th user receiver are constructed. :

[0120]

[0121] Sup{} denotes taking the supremum within the parentheses. At this critical point, the hypermartingale process describing the queue length change can be simplified, yielding the queue length change of the k-th user receiver at time t. ;

[0122]

[0123] Subsequently, a delay threshold for the k-th user receiver at time t is given. The actual delay of the k-th user receiver at time t is required. The probability of exceeding this threshold must not exceed the preset delay default probability. In other words:

[0124]

[0125] Here, PR() represents the probability of the content within the parentheses. According to the supermartingale stopping theorem, the following inequality regarding the probability of time-delay default holds:

[0126]

[0127] in, To find the expected value within the parentheses, This represents the number of parameter packets received by the k-th user receiver at time t=0. This represents the number of parameter packets served by the k-th user receiver at time t=0. The transformation parameter represents the service index parameter of the k-th user receiver when the transformation parameter is taken as the critical parameter. This represents the predefined maximum allowable delay.

[0128] Among them, the delayed default parameter The way to express it is:

[0129]

[0130] in, This indicates taking the minimum of the two values ​​within the parentheses. This allows us to obtain the upper bound of the latency default probability at the k-th user's receiver. :

[0131] .

[0132] S5: Establish communication optimization constraints by using equipment energy constraints and upper bounds on delay default probability, construct multivariate optimization equations, solve the multivariate optimization equations according to the communication optimization constraints, obtain communication optimization parameters, and complete the optimization of optical communication using the communication optimization parameters.

[0133] Furthermore, the objective of this stage is to establish and solve communication optimization constraints and multivariate optimization equations to obtain communication optimization parameters and complete the optimization of optical communication. Specifically, in step S5, the system energy efficiency is calculated, timeout accumulation parameters and reward / penalty coefficients are constructed, timeout constraints are constructed using the upper bound of the delay default probability, the timeout accumulation parameters, and the reward / penalty coefficients, the communication optimization constraints are established using the timeout constraints and the device energy constraints, and the multivariate optimization equations are established using the system energy efficiency.

[0134] The specific implementation method for the above steps in this embodiment is as follows:

[0135] Here, we first need to calculate the system energy efficiency of the k-th user receiver. :

[0136]

[0137] Where L is the total number of optical access points. Let be the communication rate of the k-th user receiver at time t, determined based on the available bandwidth. Let be the communication power between the l-th optical access point and the k-th user receiver at time t. Assign user parameters between the l-th optical access point and the k-th user receiver. Let t be the DC bias of the l-th optical access point at time t.

[0138] Subsequently, the reward / penalty parameter V was determined based on experience, and the timeout accumulation parameter for the k-th user receiver at time t was constructed. In other words, whenever the actual delay exceeds the delay threshold during optical communication, the timeout accumulation parameter is incremented by 1. This allows us to construct delay constraints:

[0139]

[0140] Where T represents the operating time of the optical communication equipment. This allows us to construct communication optimization constraints and obtain the multivariate optimization equation:

[0141]

[0142] in, This indicates that the reflector allocation parameters, user allocation parameters, and communication power are maximized, and st represents the constraint condition. Parameters are assigned to the reflector of the l-th optical access point via the n-th smart reflector to the k-th user receiver. To assign parameters to the user at the l-th optical access point and the k-th user receiver, N is the total number of smart reflectors. Let be the communication power between the l-th optical access point and the k-th user receiver at time t. This refers to the maximum permissible power of optical communication equipment. The minimum threshold for the predetermined system transmission rate, This is the minimum allowable value for DC bias. This is the maximum allowable DC bias value. Here, it is necessary to ensure that the optical communication equipment can continuously guarantee communication quality and service performance for as long as possible during its operation.

[0143] Solving the multivariate optimization equation yields the communication optimization parameters. While traditional algorithms can find local optima, static convex optimization struggles to describe dynamic evolution, resulting in high computational complexity and strong dependence on environmental models, making it unsuitable for energy efficiency optimization systems. Therefore, the optimization problem can be equivalently transformed into a conditional sequence decision generation problem. A series of decision sequences are generated, and a trained model is used to optimize these sequences, yielding the communication optimization parameters. Optical communication is then performed based on these parameters, completing the optimization of the optical communication process.

[0144] The energy efficiency optimization device for multi-access point optical communication provided by the present invention is described below. The energy efficiency optimization device for multi-access point optical communication described below can be referred to in correspondence with the energy efficiency optimization method for multi-access point optical communication described above.

[0145] Figure 2 A schematic diagram of a multi-access-point optical communication energy efficiency optimization system is shown, such as... Figure 2 As shown, the method for performing the multi-access point optical communication energy efficiency optimization method described above includes:

[0146] Composite channel gain module 100: used to deploy optical communication equipment, calculate the link channel gain of the optical communication equipment, and obtain the composite channel gain based on the link channel gain;

[0147] Equipment energy constraint module 200: used to obtain the received energy of the optical communication equipment, establish a nonlinear received energy expression based on the received energy, construct a battery dynamics model using the nonlinear received energy expression, and obtain the equipment energy constraint through the battery dynamics model;

[0148] Markov Matrix Module 300: Used to determine the number of participating communication devices and the device obstruction status, and to establish the arrival Markov matrix and service Markov matrix based on the number of participating communication devices and the device obstruction status;

[0149] Module 400 for upper bound of delay default probability: It is used to calculate the arrival martingale based on the arrival Markov matrix and composite channel gain, calculate the service martingale based on the service Markov matrix and composite channel gain, calculate the queue length change based on the arrival martingale and service martingale, establish the delay default probability inequality through the queue length change, and obtain the upper bound of the delay default probability based on the delay default probability inequality.

[0150] Communication optimization parameter module 500: It is used to establish communication optimization constraints through equipment energy constraints and upper bound of delay default probability, construct multivariate optimization equations, solve the multivariate optimization equations according to the communication optimization constraints, obtain communication optimization parameters, and complete the optimization of optical communication through communication optimization parameters.

[0151] Figure 3 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 3 As shown, the electronic device may include: a processor 810, a communications interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communications interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 can call a computer program in the memory 830 to execute a multi-access point optical communication energy efficiency optimization method, which includes:

[0152] S1: Deploy optical communication equipment, calculate the link channel gain of the optical communication equipment, and obtain the composite channel gain based on the link channel gain;

[0153] S2: Obtain the received energy at the receiver of the optical communication device, establish a nonlinear received energy expression based on the received energy, construct a battery dynamics model using the nonlinear received energy expression, and obtain the device energy constraint through the battery dynamics model;

[0154] S3: Determine the number of participating communication devices and their obstruction status, and establish the arrival Markov matrix and service Markov matrix based on the number of participating communication devices and their obstruction status.

[0155] S4: Calculate the arrival martingale based on the arrival Markov matrix and the composite channel gain, calculate the service martingale based on the service Markov matrix and the composite channel gain, calculate the queue length change based on the arrival martingale and the service martingale, establish the delay default probability inequality through the queue length change, and obtain the upper bound of the delay default probability based on the delay default probability inequality.

[0156] S5: Establish communication optimization constraints by using equipment energy constraints and upper bounds on delay default probability, construct multivariate optimization equations, solve the multivariate optimization equations according to the communication optimization constraints, obtain communication optimization parameters, and complete the optimization of optical communication using the communication optimization parameters.

[0157] Furthermore, when the computer program in the aforementioned memory 830 can be implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0158] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer is able to execute the multi-access point optical communication energy efficiency optimization method provided by the above methods, the method comprising:

[0159] S1: Deploy optical communication equipment, calculate the link channel gain of the optical communication equipment, and obtain the composite channel gain based on the link channel gain;

[0160] S2: Obtain the received energy at the receiver of the optical communication device, establish a nonlinear received energy expression based on the received energy, construct a battery dynamics model using the nonlinear received energy expression, and obtain the device energy constraint through the battery dynamics model;

[0161] S3: Determine the number of participating communication devices and their obstruction status, and establish the arrival Markov matrix and service Markov matrix based on the number of participating communication devices and their obstruction status.

[0162] S4: Calculate the arrival martingale based on the arrival Markov matrix and the composite channel gain, calculate the service martingale based on the service Markov matrix and the composite channel gain, calculate the queue length change based on the arrival martingale and the service martingale, establish the delay default probability inequality through the queue length change, and obtain the upper bound of the delay default probability based on the delay default probability inequality.

[0163] S5: Establish communication optimization constraints by using equipment energy constraints and upper bounds on delay default probability, construct multivariate optimization equations, solve the multivariate optimization equations according to the communication optimization constraints, obtain communication optimization parameters, and complete the optimization of optical communication using the communication optimization parameters.

[0164] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the aforementioned multi-access point optical communication energy efficiency optimization methods, the method comprising:

[0165] S1: Deploy optical communication equipment, calculate the link channel gain of the optical communication equipment, and obtain the composite channel gain based on the link channel gain;

[0166] S2: Obtain the received energy at the receiver of the optical communication device, establish a nonlinear received energy expression based on the received energy, construct a battery dynamics model using the nonlinear received energy expression, and obtain the device energy constraint through the battery dynamics model;

[0167] S3: Determine the number of participating communication devices and their obstruction status, and establish the arrival Markov matrix and service Markov matrix based on the number of participating communication devices and their obstruction status.

[0168] S4: Calculate the arrival martingale based on the arrival Markov matrix and the composite channel gain, calculate the service martingale based on the service Markov matrix and the composite channel gain, calculate the queue length change based on the arrival martingale and the service martingale, establish the delay default probability inequality through the queue length change, and obtain the upper bound of the delay default probability based on the delay default probability inequality.

[0169] S5: Establish communication optimization constraints by using equipment energy constraints and upper bounds on delay default probability, construct multivariate optimization equations, solve the multivariate optimization equations according to the communication optimization constraints, obtain communication optimization parameters, and complete the optimization of optical communication using the communication optimization parameters.

[0170] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0171] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0172] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for multi-access point optical communication energy efficiency optimization, characterized in that, Comprise: S1: layout optical communication equipment, calculate the link channel gain of optical communication equipment, according to the link channel gain to get composite channel gain; S2: get the receiving energy of the receiving end of optical communication equipment, establish a nonlinear receiving energy expression according to the receiving energy of the receiving end, use the nonlinear receiving energy expression to build the battery dynamics model, get the device energy constraint through the battery dynamics model; S3: determine the number of participating communication devices and the device shielding state, establish the arrival Markov matrix and the service Markov matrix according to the number of participating communication devices and the device shielding state; S4: calculate the arrival martingale according to the arrival Markov matrix and the composite channel gain, calculate the service martingale according to the service Markov matrix and the composite channel gain, calculate the queue length change according to the arrival martingale and the service martingale, establish the delay violation probability inequality through the queue length change, get the upper bound of the delay violation probability according to the delay violation probability inequality; S5: establish communication optimization constraint condition through device energy constraint and delay violation probability upper bound, construct multivariable optimization equation, solve the multivariable optimization equation according to the communication optimization constraint condition, get the communication optimization parameter, complete the optimization of optical communication through the communication optimization parameter.

2. The method of claim 1, wherein, Step S1 further comprises: S11: layout optical communication equipment, calculate the direct link communication gain and the reflected link communication gain of the optical communication equipment; S12: get the composite channel gain through the direct link communication gain and the reflected link communication gain.

3. The method of claim 1, wherein, Step S2 further comprises: S21: determine the optical communication composite signal, calculate the receiving energy of the receiving end according to the optical communication composite signal, determine the saturation power, establish the nonlinear receiving energy expression according to the receiving energy of the receiving end and the saturation power; S22: use the nonlinear receiving energy expression to establish the battery dynamics model, get the transmitting DC component and the time slot energy consumption from the battery dynamics model, get the device energy constraint through the transmitting DC component and the time slot energy consumption.

4. The method of claim 1, wherein, In step S3, the number of participating communication devices, device shielding state and device Markov state are determined, and the arrival Markov matrix and the service Markov matrix are established according to the number of participating communication devices, device Markov state and device shielding state respectively.

5. The method of claim 1, wherein, Step S4 further comprises: S41: perform exponential column transformation on the arrival Markov matrix to get the arrival transformation matrix, get the arrival exponential parameter and the arrival channel gain according to the arrival transformation matrix and the composite channel gain, and construct the arrival martingale through the arrival transformation matrix, the arrival exponential parameter and the arrival channel gain; S42: perform exponential column transformation on the service Markov matrix to get the service transformation matrix, get the service exponential parameter and the service channel gain according to the service transformation matrix and the composite channel gain, and construct the service martingale through the service transformation matrix, the service exponential parameter and the service channel gain; S43: get the initial queue length change according to the arrival martingale and the service martingale, construct the critical parameter, and calculate the queue length change through the initial queue length change and the critical parameter; S44: constructing a delay violation parameter, and obtaining the upper bound of the delay violation probability according to the queue length change and the delay violation parameter.

6. The method of claim 1, wherein, In step S5, the system energy efficiency is calculated, the timeout accumulation parameter and the reward and punishment coefficient are constructed, the delay constraint condition is constructed through the upper bound of the delay violation probability, the timeout accumulation parameter and the reward and punishment coefficient, the communication optimization constraint condition is established through the delay constraint condition and the device energy constraint, and the multivariable optimization equation is established through the system energy efficiency.

7. A multi-access point optical communication energy efficiency optimization system for performing the multi-access point optical communication energy efficiency optimization method according to any one of claims 1 to 6, characterized in that, Comprise: A composite channel gain module: used for deploying optical communication devices, calculating the link channel gain of the optical communication devices, and obtaining the composite channel gain according to the link channel gain; A device energy constraint module: used for obtaining the receiving energy of the receiving end of the optical communication devices, establishing a nonlinear receiving energy expression according to the receiving energy of the receiving end, constructing a battery dynamics model using the nonlinear receiving energy expression, and obtaining the device energy constraint through the battery dynamics model; A Markov matrix module: used for determining the number of participating communication devices and the device occlusion state, and establishing an arrival Markov matrix and a service Markov matrix according to the number of participating communication devices and the device occlusion state; An upper bound of the delay violation probability module: used for calculating an arrival martingale according to the arrival Markov matrix and the composite channel gain, calculating a service martingale according to the service Markov matrix and the composite channel gain, calculating a queue length change according to the arrival martingale and the service martingale, establishing a delay violation probability inequality through the queue length change, and obtaining the upper bound of the delay violation probability according to the delay violation probability inequality; A communication optimization parameter module: used for establishing a communication optimization constraint condition through the device energy constraint and the upper bound of the delay violation probability, constructing a multivariable optimization equation, solving the multivariable optimization equation according to the communication optimization constraint condition, obtaining a communication optimization parameter, and completing the optimization of optical communication through the communication optimization parameter.

8. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the steps of the multi-access point optical communication energy efficiency optimization method according to any one of claims 1 to 6. 9.A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the multi-access point optical communication energy efficiency optimization method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions, characterized in that, When the program instructions are executed by the computer, the computer can execute the steps of the multi-access point optical communication energy efficiency optimization method according to any one of claims 1 to 6.

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