Qos-QoE driven VLC-RF heterogeneous network energy efficiency optimization method

Abstract

The invention discloses a Qos-QoE driven VLC-RF heterogeneous network energy efficiency optimization method. The method comprises the following steps: acquiring physical layer transmission rates of a VLC access point and an RF access point in a VLC-RF heterogeneous network; and mapping the transmission rate of the physical layer of the system to the transmission rate of the link layer, and modeling the service process of the system according to the transmission rate of the link layer. A hyper-yoke theory is introduced to evaluate network time delay QoS performance, and a more compact time delay violation probability boundary is obtained; and calculating the throughput and the total power of the system, and constructing a heterogeneous network QoE model. Constructing an optimization problem which takes energy efficiency maximization as a target and takes a time delay violation probability boundary and user experience guarantee as constraint conditions; and solving the energy efficiency maximization optimization problem by improving a golden litsea optimization algorithm to obtain the optimal energy efficiency value and transmitting power of the VLC-RF heterogeneous network system.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and in particular relates to a QoS-QoE driven VLC-RF heterogeneous network energy efficiency optimization method. Background Art

[0002] Traditional radio frequency (RF) communication technology faces resource bottlenecks and interference problems. Visible light communication (VLC) has the advantages of high transmission rate, low interference, and rich spectrum resources, and can be used as a powerful supplement to RF communication technology. Taking full advantage of the technical advantages of both VLC and RF, integrating the two technologies to form a VLC-RF heterogeneous network can not only overcome the limitations of RF spectrum resources and provide highly reliable data transmission, but also provide better regional coverage, which has broad application prospects.

[0003] Communication users are experiencing a change from focusing on network service quality (QoS) to emphasizing user experience quality (QoE). At the same time, energy efficiency is an issue that needs to be focused on during the service process of heterogeneous networks. Therefore, designing a QoS-QoE driven VLC-RF heterogeneous network energy efficiency optimization method is of great significance to effectively improve the service efficiency of communication networks. Summary of the invention

[0004] The present invention proposes a QoS-QoE driven VLC-RF heterogeneous network energy efficiency optimization method to solve the problems existing in the above-mentioned prior art.

[0005] To achieve the above object, the present invention provides a QoS-QoE driven VLC-RF heterogeneous network energy efficiency optimization method, comprising:

[0006] Obtain the physical layer transmission rate of the VLC access point and the RF access point in the VLC-RF heterogeneous network; map the physical layer transmission rate to the link layer transmission rate, model the heterogeneous network service process according to the link layer transmission rate, evaluate the network delay QoS performance of the heterogeneous network service process through the hypermartingale theory, and obtain the delay violation probability bound;

[0007] Calculate the throughput and total power of heterogeneous networks based on the link layer transmission rate, and build a QoE model for heterogeneous network file download services;

[0008] Construct an energy efficiency maximization optimization problem based on the goal of maximizing energy efficiency and the QoE model of heterogeneous network file downloading service with delay violation probability bound and user experience guarantee as constraints;

[0009] By improving the golden jackal optimization algorithm, the energy efficiency maximization optimization problem is solved, and the optimal energy efficiency value and transmission power of the VLC-RF heterogeneous network system are obtained.

[0010] Optionally, the process of acquiring the physical layer transmission rate includes:

[0011] The channel gains between the VLC access point, the RF access point and the user terminal, and the signal-to-noise ratio of the user terminal to each access point are obtained respectively; and the physical layer transmission rate is calculated according to the channel gain and the signal-to-noise ratio.

[0012] Optionally, the link layer transmission rate conversion process includes:

[0013]

[0014]

[0015] in, is the transmission rate between the VLC system and the RF system and the user terminal, R RF They represent the physical layer transmission rates of the VLC system and the RF system respectively, T represents the duration of the time slot, and L represents the length of the data packet.

[0016] Optionally, the process of modeling heterogeneous network service processes includes:

[0017] Construct the instantaneous service process rate of heterogeneous networks and the service martingale of heterogeneous networks;

[0018] Among them, the rate of the instantaneous service process is:

[0019]

[0020] Among them, s m (n) represents the rate of the instantaneous service process of the heterogeneous network system in time slot n, β represents the occlusion probability of the terminal in the time slot, and M represents the number of VLC access points;

[0021] Martingale for heterogeneous networks (s,m) (n) is:

[0022]

[0023] Among them, s m Sm(n) and Sm(n) represent the instantaneous and cumulative services of the system in time slot n, respectively. m represents the QoS parameter, K (s,m) (θ m ) is the hypermartingale correction function of the service process.

[0024] Optionally, the martingale domain delay violation probability bound is:

[0025]

[0026] Among them, h (s,m) is the relevant function of the service process, h (u,m) is the relevant function for the arrival process, is the martingale parameter that links the arrival martingale and the service martingale parameters together, h (s,m) and h (u,m) The values ​​depend on H m represents the threshold value, D m is the target delay, They represent the initial value expectation of the characteristic function related to the service process and arrival process of the mth link, u m (n) represents the instantaneous number of arriving packets, U is the arrival rate, ε m is the delay violation probability threshold.

[0027] Optionally, the throughput acquisition process is:

[0028]

[0029] Where T represents the duration of the time slot, L represents the length of the data packet, is the transmission rate between the VLC system and the RF system and the user terminal, β represents the blocking probability of the terminal in the time slot, and M represents the number of VLC access points.

[0030] Optionally, the QoE model for heterogeneous network file download service is:

[0031]

[0032] Where ω and υ are weight coefficients, which are determined by the maximum and minimum values ​​of user throughput. m Indicates throughput, MOS m Indicates the QoE metric value.

[0033] Optionally, the energy efficiency maximization optimization model is:

[0034]

[0035] Where EE represents energy efficiency, ω, υ are weight coefficients, T represents the duration of the time slot, and L represents the length of the data packet. = is the transmission rate between the VLC system and the RF system and the user terminal, β represents the blocking probability of the terminal in the time slot, M represents the number of VLC access points, h (s,m) is the relevant function of the service process, h (u,m) is the relevant function for the arrival process, is a special martingale parameter that ties together the arrival and service martingale parameters, h (s,m) and h (u,m) The values ​​depend on H m represents the threshold value, D m is the target delay, They represent the initial value expectation of the characteristic function related to the service process and arrival process of the mth link, u m (n) represents the instantaneous number of arriving packets, U is the arrival rate, ε m is the delay violation probability threshold, is the transmission power from VLC AP to the mth user terminal; p RF Indicates the transmission power from RF AP to user, MOS m Indicates the QoE metric, MOS min is the minimum QoE requirement of the user, m is the user terminal number, and M is the total number of user terminals.

[0036] Optionally, the process of solving the energy efficiency maximization optimization problem by improving the golden jackal optimization algorithm includes:

[0037] Initialize the population, where the individual positions in the population are initialized through Logistic-tent chaotic mapping;

[0038] Calculate the fitness of the individual positions, and select the individual with the smallest fitness value as the optimal male golden jackal in the population, and the individual with the second-best fitness value as the optimal female golden jackal in the population;

[0039] Update the individual positions in the population by adding the sine-cosine search algorithm;

[0040] Update the individual positions in the group again through the adaptive t mutation strategy;

[0041] Based on the fitness of the individual position, the greedy rule is used to determine whether to adopt the individual position in the group that is updated again through the adaptive t mutation strategy;

[0042] The operation after the initialization of the iterative loop is repeated until the maximum number of iterations is reached, and then the optimal energy efficiency value and transmission power of each VLC and RF in the VLC-RF heterogeneous network system are obtained.

[0043] Compared with the prior art, the present invention has the following advantages and technical effects:

[0044] Research on energy efficiency optimization of VLC-RF heterogeneous communication systems can generate valuable results in network resource management and provide theoretical and technical support for future heterogeneous communication network service resource management. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0046] Figure 1 The figure is a schematic diagram of a method flow of an embodiment of the present invention. DETAILED DESCRIPTION

[0047] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0048] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0049] The present invention proposes a QoS-QoE driven VLC-RF heterogeneous network energy efficiency optimization method. For VLC-RF heterogeneous networks, the channel gain, signal-to-noise ratio and communication rate of visible light communication system and radio frequency communication system are derived respectively, and the service process is modeled considering the occlusion characteristics of the system. Then, the hypermartingale theory is introduced to evaluate the network delay QoS performance, and a more compact delay violation probability bound is obtained. The throughput and total power of the system are calculated, and the user throughput is mapped to the experience quality QoE level, and the MOS mapping function is used to measure the user QoE demand. The optimization problem is constructed with the goal of maximizing the system energy efficiency. The Logistic-tent chaotic mapping, the sine-cosine search algorithm and the adaptive t mutation strategy are combined to improve the golden jackal optimization algorithm and the improved algorithm is used to solve the system energy efficiency maximization problem. The method proposed by the present invention can maximize the energy efficiency under the premise of ensuring QoS requirements and improving the user QoE level, thereby improving the utilization rate of system resources.

[0050] The present invention provides a QoS-QoE driven VLC-RF heterogeneous network energy efficiency optimization method, which can realize efficient configuration of network service resources and improve energy efficiency while improving user experience, aiming to provide reliable theoretical and technical support for heterogeneous network operation.

[0051] In order to achieve the above technical objectives and the above technical effects, the technical solution of the present invention includes the following steps:

[0052] Step (1): For the indoor VLC-RF heterogeneous network, a system model with M users, M VLC access points (APs), and one RFAP is established. Each user is equipped with a photoelectric sensing receiver and corresponds to a VLC AP. The coverage of VLCAP is limited and will be blocked by physical obstacles, which will affect the signal strength and quality. The RF network has strong diffraction ability and the signal can cover the entire room.

[0053] Step (2): For heterogeneous network communication scenarios, the channel gain, signal-to-noise ratio, and physical layer transmission rate between VLCAP and RF AP and users are calculated respectively. The physical layer services in the VLC system and RF system are mapped to the data link layer services, and the service process is modeled considering the system's shielding characteristics. Based on the hypermartingale theory, the martingale process for system analysis is constructed, and the martingale domain delay violation probability bound is derived.

[0054] Step (3): Solve the service process rate of the data link layer in the VLC and RF systems, and then calculate the system throughput and total power. Map the user throughput to the QoE level and use the MOS mapping function to measure the user QoE requirements, and build a QoE model for file download services in heterogeneous networks.

[0055] Step (4): Taking system delay requirements, user QoE, and system transmit power as constraints, maximizing energy efficiency is used as the objective function to construct an optimization problem.

[0056] Step (5): Initialize the golden jackal prey population through Logistic-tent chaotic mapping, improve the golden jackal optimization algorithm by updating the population position using the sine-cosine search algorithm and the adaptive t mutation strategy, and use the improved algorithm to solve the optimization problem to maximize the system energy efficiency.

[0057] A detailed description of the above steps is given below:

[0058] In step (1), for the indoor VLC-RF heterogeneous network, a system model of M users, M VLC access points (AP), and one RF access point is established; each user terminal has a photoelectric sensing receiver and corresponds to a VLC. The VLC AP will be blocked by physical obstacles, affecting the signal strength and quality, and the RF network covers the entire room.

[0059] Step (2) Based on the VLC-RF heterogeneous network model, the channel gain, signal-to-noise ratio and rate between the VLC AP and the RF AP and the user are calculated respectively. In order to ensure the delay QoS requirements of the VLC-RF system, the physical layer service process of the VLC system and the RF system is transformed into the link layer service process. Considering the occlusion characteristics of the system, the system service process is established and the system service martingale is constructed. The delay QoS performance is obtained through the supermartingale stopping time theorem.

[0060] Step (2.1) calculates the channel gain between the VLCAP and the user, and the signal-to-noise ratio of the user to each access point, and then obtains the communication rate (physical layer transmission rate) of the VLC system.

[0061] Step (2.2) calculates the channel gain between the RFAP and the user, the signal-to-noise ratio of the user to each access point, and then obtains the communication rate (physical layer transmission rate) of the RF system.

[0062] Step (2.3) converts the achievable rate in bits / s to a rate in packets / slot. Let T[s / slot] denote the duration of a time slot and L[bits / packet] denote the length of a packet. For terminal m, calculate the link layer transmission rates of the VLC system and the RF system after conversion. for:

[0063]

[0064] in, R RF Represent the physical layer transmission rates of the VLC system and RF system respectively.

[0065] Step (2.4) In order to ensure the VLC-RF system delay QoS requirements, the service process of the VLC-RF system network is established according to the transmission rate converted in step (2.3).

[0066] Step (2.4.1) When the VLC direct link is blocked, the system will switch to the RF network, and the RFAP will transmit signals to the terminal. The rate of the instantaneous service process can be expressed as:

[0067]

[0068] Where β represents the blocking probability of the terminal in the time slot, and M represents the number of VLC access points.

[0069] Step (2.4.2) constructs the service martingale of the heterogeneous network system, and then obtains the delay violation probability bound through the supermartingale stopping time theorem.

[0070] Supermartingale of system service process (s,m) (n) is:

[0071]

[0072] Among them, s m Sm(n) and Sm(n) represent the instantaneous and cumulative services of the system in time slot n, respectively. m represents the QoS parameter, K (s,m) (θ m ) is the hypermartingale correction function of the service process, which is obtained by the following formula:

[0073]

[0074] For the arrival process, construct the hypermartingale M of the arrival process (u,m) (n) is:

[0075]

[0076] Among them, u m (n), U m (n) represents the instantaneous and cumulative arrival of data packets. The present invention adopts a constant rate arrival, the arrival rate is U, so h (u,m) (u m (n))=1.

[0077] Construct a hypermartingale M relative to the queue length (L,m) (n), as follows:

[0078]

[0079] According to the stopping theorem of the hypermartingale, the delay violation probability bound can be derived as:

[0080]

[0081] Among them, d m (n) represents the delay, D m is the target delay, They respectively represent the initial value expectations of the characteristic functions related to the service process and arrival process of the mth link.

[0082] is a special martingale parameter that ties together the arrival and service martingale parameters, Determined by the following formula:

[0083]

[0084] Threshold H m Determined by the following formula:

[0085] H m:=min{(h (u,m) (u m (n)))(h (s,m) (s m (n))):u m (n)-s m (n)>0}

[0086] In order to ensure the latency QoS requirements of each terminal in the system, the following inequality must be established:

[0087] P m {d m (n)≥D m}≤ε m

[0088] In the above formula, ε m is the delay violation probability threshold.

[0089] The Martingale domain delay violation probability bound obtained by this patent is as follows:

[0090]

[0091] Step (3) In the VLC-RF heterogeneous network, the throughput and total power of the network system are calculated according to the rates of the VLC and RF systems. A MOS model for file download services in heterogeneous networks is established, user throughput is mapped to the QoE level, and the MOS mapping function is used to measure user QoE requirements.

[0092] Step (3.1) calculates the system throughput C m And the total power P is:

[0093]

[0094] Where T[s / slot] represents the duration of a time slot, and L[bits / packet] represents the length of a data packet. is the transmission power from VLC AP to user m; p RF Indicates the transmission power from RF AP to user, p c It is the power loss of other components in the transmitter circuit.

[0095] Step (3.2) The user experience quality describes the overall performance of the network from the user's perspective and directly reflects the user's recognition of the network service. Compared with QoS, QoE can better measure user satisfaction. In order to measure the transformation of user QoE levels, the QoE metric Mean Opinion Score (MOS) is used to measure user QoE. According to user satisfaction, the subjective feeling of QoE is divided into five levels: "excellent", "good", "medium", "poor" and "very poor". By mapping throughput to QoE level, the MOS mapping function can be used to measure user QoE requirements.

[0096] File download service is a non-real-time service commonly used in wireless network services. It uses the file transfer protocol to achieve fast data file transmission. The core feature of this service is that the user's service experience is affected by the wireless network transmission rate. The user's expectation for the file download service is that the higher the rate, the better. Construct a QoE model for the file download service and obtain the mapping relationship from user throughput to MOS function. The QoE model for the file download service can be described as:

[0097]

[0098] Where ω and υ are weight coefficients, which are determined by the maximum and minimum values ​​of the user throughput. If the user receives a throughput of C m =C max , then the user satisfaction on MOS should be the largest, that is, 5. On the other hand, a minimum throughput is defined and assigned a MOS value of 1.

[0099] Step (4) measures the QoE satisfaction with the user as the center and derives the user's energy efficiency based on QoE. Construct the energy efficiency maximization optimization problem under the VLC-RF network. The optimization problem is expressed as:

[0100]

[0101] The constraint C1 represents the probability bound of delay violation; the constraints C2-C3 represent the total power constraints of VLC and RF systems respectively; the constraints C4-C5 ensure the non-negative power requirements of VLC and RF. The constraint C6 is the minimum QoE limit for users, MOS min MOS is the minimum QoE requirement of users. min The general value is 3.

[0102] Step (5) designs an improved Golden Jackal Optimization (GJO) algorithm and solves the optimization problem. Three major improvements are introduced. The prey population is initialized by introducing Logistic-tent chaotic mapping, and the sine-cosine search algorithm and adaptive t mutation strategy are added to update the population position. The improved Golden Jackal algorithm is used to optimize and solve step (4) to construct the VLC-RF network lower martingale domain energy efficiency maximization optimization problem. The specific steps are as follows.

[0103] Step (5.1) In the algorithm initialization population stage, the parameters of the golden jackal population are initialized, and the population position matrix is ​​initialized using the Logistic-tent chaotic mapping, where the population position matrix represents the energy efficiency value and transmission power of the VLC-RF heterogeneous network system during the optimization process. The function expression of the chaotic particle sequence generated by the Logistic-tent mapping is:

[0104]

[0105] Among them, r is the relevant control factor. When r∈(0,1), x i When ∈(0,1), the chaotic function is in a chaotic state. mod represents the modulo operation. Through the modulo operation, the distribution trajectory range of the chaotic map is expanded.

[0106] The objective function of step (5.2) is the maximum energy efficiency of the martingale domain under the VLC-RF network. The fitness value of the golden jackal position in the population is calculated according to the objective function, and the individual with the smallest fitness value is selected as the optimal male golden jackal in the population, and the individual with the second-best fitness value is selected as the optimal female golden jackal in the population.

[0107] Step (5.3) adds the sine-cosine search algorithm to update the position of the population during the process of the golden jackal searching for prey. The information of the golden jackal pair obtained in the previous iteration is used to update the values ​​of the candidate solutions in each dimension according to the sine and cosine functions. The cyclic pattern of the sine-cosine function only allows one solution to be relocated around other solutions, ensuring that the search in the space between two solutions can improve the convergence speed of global convergence. The improved search formula is:

[0108]

[0109] In the formula, represents the position of the i-th individual in the space in the t+1-th iteration; Represents the position of the optimal golden jackal population obtained in the tth iteration. r2∈[0,2π], r3∈[0,2], r4∈[0,1] are random numbers, and r1 is a linear decreasing function, which represents the positional relationship between the next solution and the current solution and the optimal solution. The expression is:

[0110]

[0111] Where a is a constant.

[0112] Step (5.4) In the later stage of algorithm iteration, in order to solve the problem that the algorithm falls into the local optimal value and the convergence accuracy is not high, the adaptive t mutation strategy is used to improve the position update formula of the golden jackal in the population, enhance the local optimization ability of the algorithm, and improve the convergence accuracy. The position update formula is:

[0113] X b ' est =X best +X best *trnd(t)

[0114] Where, X best Indicates the current optimal position of the golden jackal; X b ' est Represents the position of the golden jackal after the adaptive t mutation perturbation.

[0115] In step (5.5), after the mutation update, in order to compare the quality of the golden jackal's position before and after the mutation, a greedy rule is introduced to determine whether to adopt the mutation updated position.

[0116]

[0117] Step (5.6) determines whether the population has reached the maximum number of iterations. If so, the optimal energy efficiency value and transmission power of the VLC-RF heterogeneous network system are output. Otherwise, return to step (5.2) to continue optimizing.

[0118] In order to make the purpose, technical solution and advantages of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0119] like Figure 1 As shown, step (1) constructs a heterogeneous network scenario including M users, M VLC APs and one RF AP, and the set of users is m = {1, 2, ..., M}. Each VLCAP is placed on the ceiling, and the RF AP is fixed on the wall. Each user is equipped with a photoelectric sensing receiver and corresponds to a VLC AP. The coverage of the VLC AP is limited and will be blocked by physical obstacles, which will affect the signal strength and quality. The RF network has strong diffraction ability and the signal can cover the entire room. Since the VLC system and the RF system are two different systems, the VLC channel will not interfere with the RF channel.

[0120] Step (2) calculates the channel gain, signal-to-noise ratio and physical layer rate between VLCAP and RFAP and the user respectively. The physical layer services in the VLC system and RF system are mapped to the data link layer services, and the service process is modeled considering the system's shielding characteristics. Based on the supermartingale theory, the martingale process of system analysis is constructed, and the martingale domain delay violation probability bound is derived.

[0121] Step (2.1) calculates the channel gain between the VLC AP and the user.

[0122] The method proposed in the present invention is general, and the following calculations are all performed using user m as an example. Consider the system gain of the VLC system under LOS. According to the Lambertian radiation model, the channel gain between the mth user and the VLC AP is:

[0123]

[0124] In the above formula, b represents the distance from the user to the VLC access point, ψ m is the incident angle of the user photodetector, A m is the physical area of ​​the photodetector, φ m represents the radiation angle of the LED, g(ψ m ) represents the receiver optical filter gain, and the Lambert order is represented by ξ, as shown in the following formula: θ 12 represents the half-power angle of VLC AP. m ) is the gain of the light collector, that is,

[0125]

[0126] ψ FOV is the field of view (FOV) of the user's light detector, and l is the refractive index.

[0127] Step (2.2) calculates the channel gain between the RFAP and the user.

[0128] The path loss model for RF networks usually takes the following form:

[0129]

[0130] Where S is the path loss, b0 is the distance, and the path loss exponent is η. The RF channel gain is G RF =10 -S / 10 .

[0131] Step (2.3) uses the Shannon capacity formula to calculate the physical layer data rates of the VLC system and the RF system respectively as follows:

[0132]

[0133] in, With p RF represent the transmission power of the VLC system and the RF system respectively, and B RF Represent the frequency bandwidth of VLC system and RF system respectively, They represent the noise power spectral density of the received signals of the VLC receiver and the RF receiver respectively.

[0134] Step (2.4) converts the achievable rate in “bits / s” into the heterogeneous network transmission rate in “packets / slot”. Let T[s / slot] denote the duration of a time slot and L[bits / packet] denote the length of a packet. The transmission rates of the VLC system and the RF system are:

[0135]

[0136]

[0137] Step (2.5) constructs the system service model. When the VLC link is not blocked, the VLC AP transmits at a rate of Provide services to users; when only one of the M VLC APs is blocked and the others are not blocked, the RF AP transmits at a rate of Provide services to users, otherwise, do not provide services to users. Then, the instantaneous service process of heterogeneous networks can be expressed as:

[0138]

[0139] Where β represents the occlusion probability of the system.

[0140] Step (2.6) derives the probability bound of the system martingale domain delay violation. The supermartingale of the system service process based on the supermartingale theory is:

[0141]

[0142] Among them, s m (n), S m (n) represent the instantaneous and cumulative services of the system in time slot n, respectively, and K (s,m) (θ m ) is the hypermartingale correction function of the service process, which is obtained by the following formula:

[0143]

[0144] The arrival process of system data packets is a constant rate arrival, and the arrival rate is U. The supermartingale of the system arrival process constructed based on the supermartingale theory is:

[0145]

[0146] Wherein, um(n) and Um(n) represent the instantaneous and cumulative arrival data packets. um (u m (n))=1,K (u,m) (θ m ) is the hypermartingale correction function of the arrival process, K (u,m) (θ m ) is a constant U.

[0147] Multiplying the service martingale with the arrival martingale gives the supermartingale relative to the queue length, as shown below:

[0148]

[0149] According to the stopping theorem of the hypermartingale, the delay violation probability bound can be derived as:

[0150]

[0151] Among them, D m is the target delay, They respectively represent the initial value expectations of the characteristic functions related to the service process and arrival process of the mth link.

[0152] θ m The value of

[0153]

[0154] H m Determined by the following formula:

[0155] H m :=min{(h (u,m) (u m (n)))(h (s,m) (s m (n))):u m (n)-s m (n)>0}

[0156] In order to ensure the latency QoS requirements of each terminal in the system, the following inequality must be established:

[0157] P m {d m (n)≥D m}≤ε m

[0158] In the above formula, ε m is the delay violation probability threshold.

[0159] Therefore, the martingale domain delay violation probability bound can be expressed as:

[0160]

[0161] Step (3) The service rates of the VLC and RF systems are calculated, and then the system throughput and total power are calculated. The user throughput is mapped to the QoE level and the MOS mapping function is used to measure the user QoE requirements, and the MOS model of the file download service in the heterogeneous network is constructed.

[0162] Step (3.1) calculates the throughput and total power of the system:

[0163]

[0164] Where T[s / slot] represents the duration of a time slot, and L[bits / packet] represents the length of a data packet. is the transmission power from VLC AP to user m; p RF Indicates the transmission power from RF AP to user, p c It is the power loss of other components in the transmitter circuit.

[0165] Step (3.2) constructs the QoE model for the file download service. File download service is a non-real-time service commonly used in wireless network services. It uses the file transfer protocol to achieve fast transmission of data files. The core feature of this service is that the user's service experience is affected by the wireless network transmission rate. The user's expectation for the file download service is that the higher the rate, the better. The user experience quality describes the overall performance of the network from the user's perspective and directly reflects the user's recognition of the service. In order to measure the change of user QoE level, the QoE metric Mean Opinion Score (MOS) is used as a performance indicator. According to user satisfaction, the subjective perception of QoE is divided into five levels: "excellent", "good", "medium", "poor" and "very poor".

[0166] The specific quantification method of MOS value is shown in Table 1 below:

[0167] Table 1

[0168]

[0169] By mapping throughput to QoE level, the MOS mapping function can be used to measure user QoE requirements.

[0170]

[0171] Where ω and υ are determined by the maximum and minimum values ​​of user throughput.

[0172] Step (4) constructs an optimization problem with the goal of maximizing system energy efficiency. Different from the traditional way of defining energy efficiency, the present invention measures QoE satisfaction with a user-centric approach, defining energy efficiency as the ratio of the total MOS value of all users in the system to the total power consumed in the system, that is, maximizing the average opinion score of users per unit power consumption. Construct an optimization problem for maximizing energy efficiency in the martingale domain under the VLC-RF network. The optimization problem is expressed as:

[0173]

[0174] The constraint C1 represents the probability bound of delay violation; the constraints C2-C3 represent the total power constraints of VLC and RF systems respectively; the constraints C4-C5 ensure the non-negative power requirements of VLC and RF. The constraint C6 is the minimum QoE limit for users, MOS min MOS is the minimum QoE requirement of users. min The general value is 3.

[0175] Step (5) initializes the prey population by introducing Logistic-tent chaotic mapping, adds sine-cosine search algorithm and adaptive t mutation strategy to update the population position to improve the golden jackal algorithm, and uses the improved golden jackal algorithm to optimize and solve step (4) to construct the VLC-RF network under the martingale domain energy efficiency maximization optimization problem. The specific steps are:

[0176] Step (5.1) In the algorithm initialization population stage, the parameters of the golden jackal population are initialized, including the number of male and female golden jackals in the population and the dimensions of the population individuals. The maximum number of population iterations T is set, and the positions of the population individuals are initialized using the Logistic-tent chaotic mapping. The function expression of the chaotic particle sequence generated by the Logistic-tent mapping is as follows:

[0177]

[0178] Among them, r is the relevant control factor. When r∈(0,1), the individual position x i When ∈(0,1), the chaotic function is in a chaotic state. mod represents the modulo operation. Through the modulo operation, the distribution trajectory range of the chaotic map is expanded.

[0179] The objective function of step (5.2) is the maximum energy efficiency of the martingale domain under the VLC-RF network. The fitness value of the golden jackal position in the population is calculated according to the objective function, and the individual with the smallest fitness value is selected as the optimal male golden jackal in the population, and the individual with the second-best fitness value is selected as the optimal female golden jackal in the population.

[0180] Step (5.3) adds the sine-cosine search algorithm to update the position of the population during the process of the golden jackal searching for prey. The information of the golden jackal pair obtained in the previous iteration is used to update the values ​​of the candidate solutions in each dimension according to the sine and cosine functions. The cyclic pattern of the sine-cosine function only allows one solution to be relocated around other solutions, ensuring that the search in the space between two solutions can improve the convergence speed of global convergence. The improved search formula is:

[0181]

[0182] In the formula, represents the position of the i-th individual in the space in the t+1-th iteration; Represents the position of the optimal golden jackal population obtained in the tth iteration. r2∈[0,2π], r3∈[0,2], r4∈[0,1] are random numbers, and r1 is a linear decreasing function, which represents the positional relationship between the next solution and the current solution and the optimal solution. The specific expression is:

[0183]

[0184] Where a is a constant.

[0185] Step (5.4) In the later stage of algorithm iteration, in order to solve the problem that the algorithm falls into the local optimal value and the convergence accuracy is not high, the adaptive t mutation strategy is used to improve the position update formula of the golden jackal in the population, enhance the local optimization ability of the algorithm, and improve the convergence accuracy. The position update formula is:

[0186] X b ' est =X best +X best *trnd(t)

[0187] Where, X best Indicates the current optimal position of the golden jackal; X b ' est It represents the position of the golden jackal after the adaptive t-variation perturbation, and trnd(t) is the T distribution of the number of iterations t.

[0188] After the mutation update, in order to compare the quality of the golden jackal position before and after the mutation, the greedy rule is used to determine whether to adopt the mutation update position, and f(·) is the fitness function.

[0189]

[0190] Step (5.5) determines whether the population has reached the maximum number of iterations. If not, return to step (5.2) to continue optimizing, and the current number of iterations t = t + 1. Otherwise, the algorithm ends and outputs the system's optimal solution energy efficiency value and transmission power.

[0191] By using the improved algorithm to solve the optimization problem, users send data according to the solved power, which can maximize energy efficiency and improve system resource utilization while ensuring QoS requirements and improving user QoE levels.

[0192] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A QoS-QoE driven VLC-RF heterogeneous network energy efficiency optimization method, characterized in that: include: Obtain the physical layer transmission rate of the VLC access point and the RF access point in the VLC-RF heterogeneous network; The physical layer transmission rate is mapped to the link layer transmission rate, and the heterogeneous network service process is modeled according to the link layer transmission rate. The network delay QoS performance of the heterogeneous network service process is evaluated through the hypermartingale theory, and the delay violation probability bound is obtained. Calculate the throughput and total power of heterogeneous networks based on the link layer transmission rate, and build a QoE model for heterogeneous network file download services; Construct an energy efficiency maximization optimization problem based on the goal of maximizing energy efficiency, the delay violation probability bound, and the QoE model of heterogeneous network file download service for maintaining user experience guarantee as constraints; By improving the golden jackal optimization algorithm, the energy efficiency maximization optimization problem is solved, and the optimal energy efficiency value and transmission power of the VLC-RF heterogeneous network system are obtained.

2. The method according to claim 1, characterized in that The process of acquiring the physical layer transmission rate includes: The channel gains between the VLC access point, the RF access point and the user terminal, and the signal-to-noise ratio of the user terminal to each access point are obtained respectively; and the physical layer transmission rate is calculated according to the channel gain and the signal-to-noise ratio.

3. The method according to claim 1, characterized in that The link layer transmission rate conversion process includes: in, is the link layer transmission rate between the VLC system and the RF system and the user terminal, R RF They represent the physical layer transmission rates of the VLC system and the RF system respectively, T represents the duration of the time slot, and L represents the length of the data packet.

4. The method according to claim 3, characterized in that The process of modeling heterogeneous network service processes includes: Construct the instantaneous service process rate of heterogeneous networks and the service martingale of heterogeneous networks; Among them, the rate of the instantaneous service process is: Among them, s m (n) represents the rate of the instantaneous service process of the heterogeneous network system in time slot n, β represents the occlusion probability of the terminal in the time slot, and M represents the number of VLC access points; Martingale for heterogeneous networks (s,m) (n) is: Among them, s m Sm(n) and Sm(n) represent the instantaneous and cumulative services of the system in time slot n, respectively. m represents the QoS parameter, K (s,m) (θ m ) is the hypermartingale correction function of the service process.

5. The method according to claim 1, characterized in that The delay violation probability bound is: Among them, h (s,m) is the relevant function of the service process, h (u,m) is the relevant function for the arrival process, is the martingale parameter that links the arrival martingale and the service martingale parameters together, h (s,m) and h (u,m) The values ​​depend on H m represents the threshold value, D m is the target delay, They represent the initial value expectation of the characteristic function related to the service process and arrival process of the mth link, u m (n) represents the instantaneous number of arriving packets, U is the arrival rate, ε m is the delay violation probability threshold.

6. The method according to claim 1, characterized in that The process of obtaining throughput is as follows: Where T represents the duration of the time slot, L represents the length of the data packet, is the transmission rate between the VLC system and the RF system and the user terminal, β represents the blocking probability of the terminal in the time slot, and M represents the number of VLC access points.

7. The method according to claim 1, characterized in that The QoE model of heterogeneous network file download service is: Where ω and υ are weight coefficients, which are determined by the maximum and minimum values ​​of user throughput. m Indicates throughput, MOS m Indicates the QoE metric value.

8. The method according to claim 1, characterized in that The energy efficiency maximization optimization problem is: C4:0≤p RF Where EE represents energy efficiency, ω, υ are weight coefficients, T represents the duration of the time slot, and L represents the length of the data packet. = is the transmission rate between the VLC system and the RF system and the user terminal, β represents the blocking probability of the terminal in the time slot, M represents the number of VLC access points, D m is the target delay, They represent the initial value expectation of the characteristic function related to the service process and arrival process of the mth link, u m (n) represents the instantaneous number of arriving data packets, h (s,m) is the relevant function of the service process, h (u,m) is the relevant function for the arrival process, is the martingale parameter that links the arrival martingale and the service martingale parameters together, h (s,m) and h (u,m) The values ​​depend on H m represents the threshold, U is the arrival rate, ε m is the delay violation probability threshold, is the transmission power from VLC AP to the mth user terminal; p RF Indicates the transmission power from RF AP to user, MOS m Indicates the QoE metric, MOS min is the minimum QoE requirement of the user, m is the user terminal number, and M is the total number of user terminals.

9. The method according to claim 1, characterized in that: The process of solving the energy efficiency maximization optimization problem by improving the golden jackal optimization algorithm includes: Initialize the population, where the individual positions in the population are initialized through Logistic-tent chaotic mapping; Calculate the fitness of the individual positions, and select the individual with the smallest fitness value as the optimal male golden jackal in the population, and the individual with the second-best fitness value as the optimal female golden jackal in the population; Update the individual positions in the population by adding the sine-cosine search algorithm; Update the individual positions in the group again through the adaptive t mutation strategy; Based on the fitness of the individual position, the greedy rule is used to determine whether to adopt the individual position in the group that is updated again through the adaptive t mutation strategy; The operation after the initialization of the iterative loop is repeated until the maximum number of iterations is reached, and then the optimal energy efficiency value and transmission power of each VLC and RF in the VLC-RF heterogeneous network system are obtained.

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