An airport wireless power beaming communication method and system assisted by a clamping antenna system

Abstract

This invention relates to a PASS-assisted airport wireless power communication method and system, belonging to the field of wireless communication technology. It solves the problems of large-scale path loss and difficulties in non-line-of-sight transmission in existing technologies. The method includes: S1: Establishing a PASS-assisted airport downlink transmission system, including a base station equipped with a waveguide, multiple single-antenna information users, and multiple single-antenna power users. The waveguide has multiple clamping antennas. S2: Establishing free-space channel vectors from all clamping antennas to each information user and to each power user within the airport, propagation channel vectors within the waveguide, and models of the signals received by the information users and power users. S3: Establishing a multi-objective optimization problem model, including multi-objective optimization problem models for NOMA, FDMA, and TDMA schemes. S4: Solving the multi-objective optimization problem model to obtain the solution results. S5: Providing the solution results to the PASS-assisted airport downlink transmission system.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and more specifically to an airport wireless power-carrying communication method and system assisted by a clamping antenna system. Background Technology

[0002] With the evolution of 5G AeroMACS (Aeronautical 5G Airport Surface Broadband Mobile Communication System), the number of users within airports is constantly increasing, ranging from aircraft, guidance vehicles, and ground support equipment to a large number of sensors and personnel. This makes ensuring seamless communication and sustainable power supply for a massive number of users a key challenge for the efficient and safe operation of airport surfaces. Specifically, on the one hand, airport surfaces are vast, with high user deployment density and numerous obstacles (such as parked aircraft and cars), which can easily cause severe shadowing and non-line-of-sight propagation of wireless signals, affecting communication reliability. On the other hand, the massive number of terminal devices within airports require continuous power supply. Traditional power supply methods, such as periodic battery replacement and wired charging, are severely limited in the large-scale, highly dynamic environment of airports that requires uninterrupted service due to high maintenance costs, large manpower requirements, and limited deployment flexibility, making it difficult to meet the actual needs of the densely distributed user base at airports.

[0003] To overcome these limitations, SWIPT (Wireless Power-Carrying Telecommunication) technology offers a viable solution. By using the same radio frequency signal, SWIPT can simultaneously provide data transmission services to information users and enable wireless power transfer for energy harvesting users, thereby effectively reducing system maintenance costs and improving deployment flexibility. However, the practical deployment of SWIPT is fundamentally limited by the quality of the wireless channel. Poor channel conditions limit channel gain, which simultaneously reduces reliable data transmission for information users and effective power harvesting for energy users, even making the latter impractical. Therefore, establishing a stable and reliable high-gain line-of-sight (LoS) link is the primary issue driving the practical application of SWIPT systems within airports.

[0004] To address this issue, MIMO (Multiple-Input Multiple-Output) technology offers a viable solution. Specifically, MIMO-assisted SWIPT systems utilize beamforming to direct communication / energy signals to the desired information / energy users, reducing interference from other directions and thus improving the overall performance of the SWIPT system. However, fixed antenna positions prevent MIMO from being fully utilized, limiting the system's spatial multiplexing performance. To overcome the inherent limitations of traditional MIMO, various flexible antenna technologies, such as MA (Movable Antenna) and FA (Fluorescent Antenna), have been widely applied. By moving the antenna position within a small range, MA and FA can actively reconfigure the wireless propagation environment, significantly improving the effective channel gain of the SWIPT system. However, MA and FA are limited to movement within a few wavelengths, providing insufficient small-scale adjustments to cope with the challenges posed by large-scale path loss at airports. Furthermore, when the communication link between the base station and the user is blocked by aircraft or vehicles within the airport, the small-scale displacement of the antenna is often insufficient to establish a viable line-of-sight link.

[0005] Chinese invention patent application, publication number CN115173901A, entitled "Energy Efficiency Maximization Method for MISO Wireless Powered Communication System Based on IRS Assistance," establishes a model for maximizing system energy efficiency; designs and analyzes an alternating optimization algorithm that jointly optimizes the transmit beamforming vector, the reflective beamforming vector, and the power allocation factor to maximize system energy efficiency. However, the communication system in this method uses fixed antenna positions, which limits the spatial multiplexing performance of the system.

[0006] Chinese invention patent application, publication number CN111726151A, entitled "A Resource Allocation Method and Apparatus Based on Wireless Powered Communication," aims to maximize the system's energy efficiency by constructing a joint digital multicast, unicast precoding, and power splitting rate optimization problem. This problem is solved using a double-loop iterative algorithm to obtain the power splitting ratio and digital coding scheme that maximizes system energy. However, this method still uses fixed antenna positions in the communication system, which limits the system's spatial multiplexing performance.

[0007] As a novel paradigm of flexible antenna technology, the PASS (Pinch-Apart Antenna System) offers a promising solution to address the massive signal path loss at airports. PASS utilizes dielectric waveguides as its transmission medium, and the clamp-apart antenna, made of low-cost dielectric materials, can be flexibly placed anywhere needed for signal transmission. This unique architecture allows the antenna to be deployed closer to the user, effectively combating massive path loss and establishing a more reliable Loss-of-Sight (LoS) link. Therefore, combining PASS with SWIPT technology can improve the performance of airport information transmission and energy harvesting.

[0008] However, current PASS-assisted airport SWIPT systems face the following key technical challenges: Complex channel modeling: The position of the clamping antenna in the PASS simultaneously affects the cascaded channel characteristics of signal propagation within the waveguide and in free space outside the waveguide. The relationship between channel gain and antenna position is complex, making accurate modeling and optimization difficult; Multi-user target conflict: The position of the clamping antenna simultaneously affects the communication rate of information users and the energy collection efficiency of energy users. These two performance indicators conflict in their optimization objectives, making it difficult to achieve the overall optimal trade-off for the system; Joint optimization coupling problem: The deployment of the clamping antenna position is coupled with the resource allocation (power, time slots, bandwidth) under multiple access. The joint optimization problem exhibits highly non-convex characteristics, making it difficult to solve. Summary of the Invention

[0009] This invention aims to overcome the technical challenges of existing wireless energy-carrying communication systems in combating large-scale path loss. It provides a method and system for airport wireless energy-carrying communication assisted by a clamping antenna system. By flexibly adjusting the position of the clamping antenna, it simultaneously optimizes information transmission and energy harvesting, significantly improving the system's signal transmission quality and overall performance. The implementation process of the airport wireless energy-carrying communication method assisted by the clamping antenna system provided by this invention includes: First, constructing a channel model for transmission between the base station and users via PASS; second, establishing a multi-objective optimization problem for three multiple access methods—NOMA, FDMA, and TDMA—that simultaneously maximizes the minimum communication rate for information users and the minimum energy harvesting efficiency for energy users; then, utilizing… The constraint method transforms the multi-objective optimization problem into a single-objective optimization problem, and uses an alternating optimization algorithm to decompose the non-convex optimization problem into two sub-problems: antenna position optimization and resource allocation optimization. These are solved using particle swarm optimization and successive convex approximation algorithms, respectively. This invention can effectively combat large-scale path loss during signal propagation within airports, achieving a significant improvement in the rate-energy boundary.

[0010] According to one embodiment of the present invention, an airport wireless power-carrying communication method assisted by a clamping antenna system is provided, comprising: S1: Establish a PASS-assisted airport downlink transmission system, including a base station equipped with a waveguide and multiple single-antenna information users and multiple single-antenna energy users, wherein the waveguide equipped with the base station has multiple clamping antennas; S2: Based on PASS-assisted airport downlink transmission system, establish free space channel vectors from all clamped antennas to each information user and to each energy user, propagation channel vectors in the waveguide, and models of the signals received by each information user and each energy user. S3: Establish multi-objective optimization problem models, including establishing multi-objective optimization problem models for the NOMA scheme, FDMA scheme, and TDMA scheme respectively; S4: Introduce the minimum collected energy of the energy user, transform the multi-objective optimization problem model respectively, and solve it to obtain the solution results; S5: Provide the obtained solution results to the PASS-assisted airport downlink transmission system to achieve communication that maximizes the minimum communication rate for information users and the minimum energy harvesting efficiency for energy users; S4 includes: S4.1: The multi-objective optimization problem model of the NOMA scheme is transformed into the clamping antenna position optimization sub-problem model and the power allocation sub-problem model of the NOMA scheme, and solved by the particle swarm optimization algorithm and the SCA method respectively to obtain the clamping antenna position scheme and the information user power allocation scheme. S4.2: The multi-objective optimization problem model of the FDMA scheme is transformed into the FDMA scheme clamping antenna position optimization sub-problem model, as well as the frequency resource allocation vector and power allocation vector optimization sub-problem model. The clamping antenna position scheme and the frequency resource allocation vector and power allocation vector scheme of the information user are solved by the particle swarm optimization algorithm and CVX respectively. S4.3: The multi-objective optimization problem model of the TDMA scheme is transformed into a TDMA scheme clamping antenna position optimization sub-problem model and a time resource allocation vector optimization sub-problem model, and solved by particle swarm optimization algorithm and CVX respectively to obtain the clamping antenna position scheme and the optimal time allocation scheme for information users in each time slot.

[0011] Optionally, S1 also includes: Obtain the orientation and dimensions of the waveguide in a three-dimensional Cartesian coordinate system, as well as the location of the waveguide feed point; wherein the waveguide is parallel to... The axis, dimensions include the waveguide's height and maximum length; The positions of each clamped antenna on the waveguide are obtained, and the positions of each clamped antenna on the waveguide are obtained accordingly. Position on the axis; Obtain the location of each information user and the location of each energy user.

[0012] Optionally, S2 includes: Based on the location of each information user and each energy user, as well as the location of each clamping antenna on the waveguide, and considering path loss, free space channel vectors from all clamping antennas to each information user and from all clamping antennas to each energy user are established. Based on the clamping antenna Based on the position on the axis and the maximum length of the waveguide, establish the propagation channel vector within the waveguide; Based on the free-space channel vectors from all clamped antennas to each information user and to each energy user, and the propagation channel vectors within the waveguide, considering the Gaussian white noise received by each information user and each energy user, the signals received by each information user and each energy user are established.

[0013] Optionally, the multi-objective optimization problem model for the NOMA scheme in S3 includes: The information users are configured to use continuous interference cancellation to decode the desired signal. Based on the signals received by each information user, and considering the power allocation coefficient vector of each information user, the achievable communication rate of each information user is established. Based on the signals received by each energy user, and considering the energy collection efficiency of each energy user, the energy collected by each energy user is established. Establish a multi-objective optimization problem model for the NOMA scheme, where the objective function includes: clamping the antenna in The position on the axis and the power allocation coefficient vector of the information user are variables, maximizing the minimum achievable communication rate of the information user; and using the clamped antenna in... The position on the axis is a variable, maximizing the energy collected by the energy user; the constraints include: minimum spacing constraint of the clamping antenna, distribution range constraint of the clamping antenna, decoding order constraint of the information user in NOMA, and power allocation constraint of the information user.

[0014] Optionally, the multi-objective optimization problem model for the FDMA scheme in S3 includes: Based on the signals received by each information user, and considering the frequency allocation factor and power allocation factor of each information user, the achievable communication rate of each information user is established. Based on the signals received by each energy user, and considering the energy collection efficiency of each energy user, the energy collected by each energy user is established. Establish a multi-objective optimization problem model for the FDMA scheme, where the objective function includes: clamping the antenna in Using the position on the axis, the frequency resource allocation vector and power allocation vector of the information user as variables, the goal is to maximize the minimum achievable communication rate of the information user; and to use the clamped antenna in... The position on the axis is a variable, maximizing the energy collected by the energy user; the constraints include: minimum spacing constraint of the clamping antenna, distribution range constraint of the clamping antenna, frequency allocation constraint of the information user, and maximum transmit power constraint of the information user.

[0015] Optionally, the multi-objective optimization problem model for the TDMA scheme in S3 includes: Based on the signals received by each information user and the propagation channel vector in the waveguide, and considering the time allocation factor of the information user, the achievable communication rate of each information user is established; wherein, it is set that the base station serves one information user in each time slot, that is, in each time slot, the clamping antenna of the base station sends the signal to the corresponding information user. Based on the signals received by each energy user, considering the energy collection efficiency of the energy user and the time allocation factor of each information user, the energy collected by each energy user in each time slot is established, and then the total energy collected by each energy user in all time slots is established. Establish a multi-objective optimization problem model for the TDMA scheme, where the objective function includes: clamping the antenna in Using the position on the axis and the time resource allocation vector of the information user as variables, the goal is to maximize the minimum achievable communication rate of the information user; and to use the clamped antenna in... The position on the axis is a variable, maximizing the energy collected by the energy user; constraints include: minimum spacing and distribution range constraints of the clamping antennas in each time slot, and time allocation factor constraints of the information user.

[0016] Optionally, the solution to the multi-objective optimization problem model for the NOMA scheme in S4 includes: S4.1.1: Introducing the minimum harvested energy for energy users transforms the multi-objective optimization problem model of the NOMA scheme into a single-objective optimization problem model, where the objective function is: [The objective function is to use the clamped antenna in...] The position on the axis and the power allocation coefficient vector of the information user are variables to maximize the achievable communication rate of the information user; the constraints include: minimum spacing constraint of the clamping antenna, distribution range constraint of the clamping antenna, decoding order constraint of the information user in NOMA, power allocation constraint of the information user, and minimum energy collection constraint of the energy user. S4.1.2: Single-objective optimization problem model based on the NOMA scheme. By setting the power allocation coefficient vector of the information user as a fixed power allocation factor, the sub-problem model for optimizing the clamping antenna position under the NOMA scheme is obtained. The objective function is: [Equation missing - likely related to the objective function]. The position on the axis is used as an optimization variable to maximize the minimum achievable communication rate for information users; the constraints include: minimum spacing constraint of clamping antennas, distribution range constraint of clamping antennas, decoding order constraint of information users in NOMA, and minimum energy collection constraint of energy users. S4.1.3: Solve the sub-problem model of the clamping antenna position optimization in the NOMA scheme using the particle swarm optimization algorithm to obtain the position scheme of the clamping antenna; S4.1.4: A single-objective optimization problem model based on the NOMA scheme, with fixed antenna positions and the introduction of an auxiliary variable regarding the achievable communication rate of the information user as a lower bound of the objective function, yields the transformed power allocation sub-problem model of the NOMA scheme. The objective function is: maximizing the auxiliary variable regarding the achievable communication rate of the information user, using the power allocation coefficient vector of the information user as the variable; the constraints include: power allocation constraints for the information user and constraints on the auxiliary variable regarding the achievable communication rate of the information user. S4.1.5: Using the SCA method, an auxiliary variable regarding the power allocation of information users is introduced to solve the power allocation subproblem model of the NOMA scheme, thereby obtaining the power allocation scheme for information users, including the optimal solution of the power allocation factor; The obtained antenna positioning scheme and information user power allocation scheme are the solution results of the multi-objective optimization problem model of the NOMA scheme.

[0017] Optionally, the solution to the multi-objective optimization problem model for the FDMA scheme in S4 includes: S4.2.1: Introducing the minimum harvested energy for energy users transforms the multi-objective optimization problem model of the FDMA scheme into a single-objective optimization problem model of the FDMA scheme, where the objective function is: [Equation missing - likely related to the minimum harvested energy of the energy user]. The position on the axis, the frequency resource allocation vector and the power allocation vector of the information user are variables, and the minimum achievable rate of the information user is maximized. The constraints include: minimum spacing constraint of the clamping antenna, distribution range constraint of the clamping antenna, frequency allocation constraint of the information user, maximum transmit power constraint of the information user, and minimum collected energy constraint of the energy user. S4.2.2: Based on the FDMA scheme, a single-objective optimization problem model is derived by fixing the frequency resource allocation vector and power allocation vector of the information user, resulting in the sub-problem model of the clamping antenna position optimization for the FDMA scheme. The objective function is to optimize the clamping antenna position in a position where... The position on the axis is a variable, maximizing the minimum achievable rate for information users; constraints include: the minimum spacing of the clamping antennas, the distribution range of the clamping antennas, and the minimum harvested energy for energy users; S4.2.3: Solve the sub-problem model of the clamping antenna position optimization in the FDMA scheme using the particle swarm optimization algorithm to obtain the position scheme of the clamping antenna; S4.2.4: A single-objective optimization problem model based on the FDMA scheme is introduced. An auxiliary variable regarding the achievable communication rate of the information user is used as the lower bound of the objective function. With the antenna position fixed, a sub-problem model for optimizing the frequency resource allocation vector and power allocation vector of the FDMA scheme is obtained. The objective function is to maximize the auxiliary variable regarding the achievable communication rate of the information user, using the frequency resource allocation vector and power allocation vector of the information user as variables. The constraints include the frequency allocation constraint and power allocation constraint of the information user, as well as the auxiliary variable constraint regarding the achievable communication rate of the information user. S4.2.5: Solve the frequency resource allocation vector and power allocation vector optimization sub-problem model of the FDMA scheme through CVX to obtain the frequency resource allocation vector and power allocation vector scheme of information users; The obtained antenna positioning scheme, frequency resource allocation vector, and power allocation vector scheme for information users are the solution results of the multi-objective optimization problem model of the FDMA scheme.

[0018] Optionally, the solution to the multi-objective optimization problem model for the TDMA scheme in S4 includes: S4.3.1: Introducing the minimum harvested energy for energy users transforms the multi-objective optimization problem model of the TDMA scheme into a single-objective optimization problem model of the TDMA scheme, where the objective function is: [Equation missing - likely related to the minimum harvested energy of the energy user]. The position on the axis and the time resource allocation vector of the information user are variables, maximizing the minimum achievable rate of the information user; the constraints include: minimum spacing and distribution range constraints of the clamping antennas in each time slot, time resource allocation constraints of the information user, and minimum collection energy constraints of the energy user. S4.3.2: A single-objective optimization problem model based on the TDMA scheme, fixing the time resource allocation vector of information users, iteratively optimizing the position of the clamping antenna in each time slot, yields the sub-problem model for clamping antenna position optimization in the TDMA scheme, where the objective function is to determine the position of the clamping antenna in each time slot. The position on the axis is a variable, maximizing the minimum achievable rate for each information user in each time slot; the constraints include: minimum spacing and distribution range constraints of the clamping antennas in each time slot, minimum collection energy constraints for energy users; and the clamping antenna position in each time slot is obtained by solving the particle swarm optimization algorithm. S4.3.3: A single-objective optimization problem model based on the TDMA scheme. The clamping antenna positions within each time slot are fixed. An auxiliary variable relating to the achievable communication rate of the information user is introduced as a lower bound of the objective function, resulting in a sub-problem model for optimizing the time resource allocation vector of the TDMA scheme. The objective function is to maximize the auxiliary variable relating to the achievable communication rate of the information user, using the information user's time resource allocation vector as the variable. Constraints include: time resource allocation constraints for the information user, constraints on the auxiliary variable relating to the achievable communication rate of the information user, and minimum collection energy constraints for the energy user. The optimal time allocation scheme for the information user is obtained by solving using CVX. The obtained optimal time allocation scheme for the clamping antenna position and information user in each time slot is the solution result of the multi-objective optimization problem model of the TDMA scheme.

[0019] According to another embodiment of the present invention, an airport wireless power-carrying communication system assisted by a clamping antenna system is provided for performing the above-described airport wireless power-carrying communication method assisted by a clamping antenna system, comprising: a base station equipped with a waveguide, multiple single-antenna information users, multiple single-antenna power users, and a processor, wherein the waveguide equipped with the base station has multiple clamping antennas, and the processor performs the modeling and calculation functions in S1 to S5.

[0020] Compared with the prior art, the present invention provides an airport wireless power-carrying communication method and system assisted by a clamping antenna system, which has at least the following beneficial effects.

[0021] (1) The method of the present invention can significantly improve the user fairness and energy harvesting efficiency of the system, and provide airports with a high-gain and high-flexibility SWIPT solution.

[0022] (2) The method of the present invention can combat large-scale path loss during signal propagation and significantly improve the performance upper limit of communication users and energy users.

[0023] (3) This invention deploys PASS (clamping antenna) into the airport SWIPT (wireless energy-carrying communication) system and provides services to multiple information users and energy users simultaneously through a multiple access scheme.

[0024] (4) The method of the present invention can enhance the communication rate and energy harvesting efficiency of the airport system by adjusting the position of the clamping antenna and the allocation of multi-user resources, wherein the clamping antenna can be deployed close to the user to construct a robust LoS link and significantly enhance the channel gain. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the embodiments will be briefly introduced below. The features and advantages of the present invention can be more clearly understood by referring to the accompanying drawings. The accompanying drawings are schematic and should not be construed as limiting the present invention in any way. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A flowchart outlining the overall steps of an airport wireless power-carrying communication method assisted by a clamping antenna system, according to an embodiment of the present invention.

[0027] Figure 2 A schematic diagram of an airport wireless power-carrying communication system assisted by a clamping antenna system according to another embodiment of the present invention.

[0028] Figure 3 A flowchart of an airport wireless power-carrying communication method and system assisted by a clamping antenna system according to another embodiment of the present invention.

[0029] Figure 4 An achievable rate-energy region test map was obtained by applying Embodiment 1 of the airport wireless power-carrying communication method assisted by the clamping antenna system provided according to an embodiment of the present invention.

[0030] Figure 5 The maximum minimum information rate obtained under different transmission powers in Example 1 is compared with the results of the prior art.

[0031] Figure 6 The maximum minimum information rate obtained in Example 1 for different numbers of clamped antennas is compared with the results of the prior art. Detailed Implementation

[0032] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other.

[0033] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein. Therefore, the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0034] The following detailed description, with reference to the accompanying drawings, illustrates an airport wireless power-carrying communication method and system assisted by a clamped antenna system, according to an embodiment of the present invention. This embodiment proposes a solution for airport wireless power-carrying communication assisted by a clamped antenna system, which provides optimized services to both information and energy users simultaneously by flexibly adjusting the position of the clamped antenna and resource allocation. The technical solution of the airport wireless power-carrying communication method assisted by the clamped antenna system provided in this embodiment is as follows: A channel model is established between the base station, PASS, and information / energy users, clarifying the impact of antenna position changes on the channel; for three multiple access schemes (non-orthogonal multiple access (NOMA), frequency division multiplexing (FDMA), and time division multiplexing (TDMA)), a multi-objective optimization problem is established to simultaneously maximize the minimum communication rate for information users and the minimum energy harvesting efficiency for energy users, and an antenna position and resource allocation optimization method is designed; utilizing… The constraint method transforms the multi-objective optimization problem into a single-objective optimization problem, and uses an alternating optimization algorithm to decompose the original optimization problem into two sub-problems: antenna position optimization and resource allocation optimization. The antenna position optimization sub-problem is solved by particle swarm optimization, and the resource allocation optimization sub-problem is solved by successive convex approximation algorithm. Through continuous iterative optimization, the rate-energy region of the system can be obtained.

[0035] like Figure 2 As shown, an airport wireless power-carrying communication system assisted by a clamping antenna system according to an embodiment of the present invention includes a base station equipped with a dielectric waveguide and Individual antenna information user and Individual single-antenna energy users; wherein, the base station is equipped with a dielectric waveguide having A clamping antenna. Optionally, the clamping antenna system-assisted airport wireless power-carrying communication system of this embodiment may further include a processor, wherein the modeling and calculation functions in S1 to S5 of a clamping antenna system-assisted airport wireless power-carrying communication method according to the following embodiment are executed.

[0036] like Figure 1 As shown, according to another embodiment of the present invention, an airport wireless power-carrying communication method assisted by a clamping antenna system includes the following steps.

[0037] S1: Establishing a PASS-assisted airport downlink transmission system, including a base station equipped with waveguides and Individual antenna information user and Single-antenna energy user.

[0038] like Figure 2 As shown, within the airport, the waveguides equipped with base stations have... A clamping antenna, a collection of clamping antennas. express, Indicates the index of the clamping antenna, in Figure 2 PA1 to PA In a three-dimensional Cartesian coordinate system, the waveguide is parallel to... Axis, height is The maximum length is . No. The position of the clamped antenna on the waveguide can be represented as follows: All clamped antennas in The position on the axis is available The location of the waveguide feed point is indicated. The waveguides used in the base station are dielectric waveguides.

[0039] The collection of all information users is The set of energy users is ,in, For information users' index, Index for energy users. The location of each information user is , No. The location of each energy user is Among them, the subscript in the symbol Indicates information user, subscript Indicates an energy user. Figure 2 In China, from EU1 to EU Indicates energy user, in IU1 to IU Indicates information user.

[0040] S2: Based on the PASS-assisted airport downlink transmission system, establish the free space channel vector from all clamped antennas to each information user and each energy user, the propagation channel vector in the waveguide, and establish the signal model received by each information user and each energy user.

[0041] Based on the locations of all information users and energy users within the airport, as well as the positions of each clamped antenna on the waveguide, and considering path loss, a path is established from all clamped antennas to the first... The information user and the first Free space channel vectors for each energy user:

[0042] in, This is the path loss coefficient. At the speed of light, For signal carrier frequency, For the signal wavelength, Represents the imaginary unit. For the first The clamping antenna and the first The distance between individual information users For the first The clamping antenna and the first The distance between energy users.

[0043] like Figure 2 As shown, since all the clamped antennas are located on the same waveguide, the signal transmitted by one clamped antenna is a phase-shifted version of the signal transmitted by the other clamped antenna, which means that one waveguide can only support one data stream.

[0044] Based on the clamping antenna Based on the position on the axis and the maximum length of the waveguide, establish the propagation path vector within the waveguide:

[0045] in, Indicates the waveguide wavelength. The effective refractive index of the waveguide.

[0046] Based on the free-space channel vectors from all clamped antennas to each information user and to each energy user, and the propagation channel vectors within the waveguide, considering the Gaussian white noise received by each information user and each energy user, the first... The signal received by the user and the first The signals received by each energy user are as follows:

[0047] in, This indicates the base station's transmission power. Indicates the first The user receives Gaussian white noise. Indicates the first Gaussian white noise received by each energy user For the first The corresponding noise power for each information user It is the first The corresponding noise power for each energy user This indicates the transmitted signal from the base station.

[0048] Since a single waveguide can only transmit one type of signal at a time, this implementation considers three different multiple access schemes to simultaneously serve multiple information users and energy users.

[0049] S3: For a PASS-assisted airport downlink transmission system, establish a multi-objective optimization problem model, including multi-objective optimization problem models for the NOMA, FDMA, and TDMA schemes respectively. For example... Figure 3 As shown, step S3 may specifically include the following steps.

[0050] S3.1: Establish a multi-objective optimization problem model for the NOMA scheme to simultaneously maximize the minimum data rate for information users and the minimum collection energy for energy users.

[0051] In the NOMA scheme, the base station first superimposes the signals sent to each information user, and the resulting superimposed signal can be represented as: ,in, For the first Power allocation coefficient for each information user For the first The information users receive desired signals. They employ Continuous Interference Cancellation (SIC) to decode different desired signals. According to the SIC principle, the information user with stronger channel gain first decodes the signal from the information user with weaker channel gain and removes it from the received signal, then decodes its own signal. This is defined as the decoding order for all information users. For information users... and ,if This means that information users First, decode the information user The signal is then decoded, and then its own signal is decoded. The channel gain order needs to satisfy... .

[0052] Based on the signals received by each information user, and considering the power allocation coefficients of each information user, the first... The achievable communication rate for an individual information user is expressed as:

[0053] in, For the first The information user index is decoded after each information user.

[0054] In the NOMA scheme, the decoding order and power allocation strategy do not affect the energy harvesting efficiency of energy users. (Establishing the first...) The energy collected by an energy user can be represented as:

[0055] in, The energy harvesting efficiency of each energy user is given by the above formula. It can be seen from the formula that the energy harvested by each energy user depends only on the channel gain, which in turn is determined by the position of the clamping antenna.

[0056] A multi-objective optimization problem model for the NOMA scheme is established to simultaneously maximize the minimum data rate for information users and the minimum collection energy for energy users, namely:

[0057] Wherein, the power allocation coefficient vector for information users is denoted as , This indicates the minimum spacing between the clamped antennas.

[0058] The objective function of the multi-objective optimization problem model for the NOMA scheme includes the clamping antenna in The position on the axis and the power allocation coefficient vector of the information user are variables, maximizing the minimum achievable communication rate of the information user; and using the clamped antenna in... The position on the axis is a variable, and the goal is to maximize the energy collected by the energy users. The constraints of the multi-objective optimization problem model for the NOMA scheme include: minimum spacing constraints for the clamping antennas, distribution range constraints for the clamping antennas, decoding order constraints for information users in NOMA, and power allocation constraints for information users.

[0059] S3.2: Establish a multi-objective optimization problem model for the FDMA scheme. In the FDMA scheme, the frequency band is divided into multiple orthogonal frequency sub-bands to provide services to multiple information users.

[0060] Based on the signals received by each information user, and considering the frequency allocation factor and power allocation factor of each information user, the first... The achievable communication rate for an individual information user is expressed as:

[0061] in, Indicates the first Frequency allocation factor for each information user Indicates the first Power allocation factor for each information user.

[0062] Since the power and frequency allocation in the FDMA scheme has no impact on the energy harvesting capability of energy users, therefore... The energy collected by each energy user is the same as that in equation (7), depending only on the location of the PASS.

[0063] The multi-objective optimization problem model for the FDMA scheme is established as follows:

[0064] in, Assign frequency resource vectors to information users. Power allocation vector for information users.

[0065] The objective function of the multi-objective optimization problem model for the FDMA scheme includes: using the clamping antenna in... Using the position on the axis, the frequency resource allocation vector and power allocation vector of the information user as variables, the goal is to maximize the minimum achievable communication rate of the information user; and to use the clamped antenna in... The position on the axis is a variable, and the goal is to maximize the energy collected by the energy user. The constraints of the multi-objective optimization problem model for the FDMA scheme include: minimum spacing constraint of the clamping antennas, distribution range constraint of the clamping antennas, frequency allocation constraint of the information user, and maximum transmit power constraint of the information user.

[0066] S3.3: Establishing a multi-objective optimization problem for the TDMA scheme. In the TDMA scheme, the clamped antenna serves different information users through multiple orthogonal time slots. Based on the flexible positioning characteristics of the clamped antenna, a time-switching operating mode is designed. Specifically, all clamped antennas can be redeployed in each time period to serve one information user and all energy users.

[0067] For ease of representation, the base station is set to the [number]th [number]. Within the time slot, the service... For each information user, the number of time slots is equal to the number of information users, and the index of the time slot corresponds one-to-one with the index of the information user. Based on the signal received by each information user and the propagation channel vector in the waveguide, and considering the time allocation factor of the information user, the corresponding expression for the achievable communication rate of each information user is established:

[0068] in, For the first The time allocation factor for each information user For the first All clamped antenna positions within each time slot Indicates the first The antenna is clamped to the first time slot. Channel vectors of individual information users Indicates the first The propagation channel vector within the waveguide in each time slot.

[0069] Within each time slot, since the clamping antenna only needs to send the required signal to one information user, all of its transmit power can be allocated to that signal.

[0070] Based on the signals received by each energy user, and considering the energy collection efficiency of each energy user and the time allocation factor of each information user, the first... The energy user in the first The energy collected within each time slot is represented as follows:

[0071] in, Indicates the first The antenna is clamped to the first time slot. Channel vectors for each energy user.

[0072] Unlike NOMA and FDMA schemes, the energy harvesting equations of the TDMA scheme depend on the location and time resource allocation of the clamping antenna.

[0073] Establish the first The total collected energy of an energy user across all time slots is represented as follows:

[0074] Establish a multi-objective optimization problem model for the TDMA scheme:

[0075] The position matrix of the clamping antenna in all time slots is as follows: The information user's time resource allocation vector is .

[0076] The objective function of the multi-objective optimization problem model for the TDMA scheme includes: using the clamping antenna in Using the position on the axis and the time resource allocation vector of the information user as variables, the goal is to maximize the minimum achievable communication rate of the information user; and to use the clamped antenna in... The position on the axis is a variable, maximizing the energy collected by the energy user. The constraints of the multi-objective optimization problem model of the TDMA scheme include: minimum spacing and distribution range constraints of the clamping antennas in each time slot, and time allocation factor constraints of the information user (i.e., time resource allocation constraints).

[0077] S4: Solve the multi-objective optimization problem model, including solving the multi-objective optimization problem models of the NOMA scheme, FDMA scheme and TDMA scheme respectively to obtain the solution results.

[0078] Due to the conflict between multiple objectives and the strong coupling between multiple variables during the optimization process, problems (8), (10), and (14) are difficult to solve directly. The constraint method can effectively explore the inspiration of Pareto boundary for complex conflict objectives. In this implementation, the method is used to transform the multi-objective optimization problem established above into a single-objective optimization problem, and to obtain solutions for different multiple access schemes.

[0079] S4.1: Solve the multi-objective optimization problem model for the NOMA scheme.

[0080] S4.1.1: Introducing the minimum harvested energy for energy users transforms the multi-objective optimization problem model of the NOMA scheme into the following single-objective optimization problem model of the NOMA scheme:

[0081] in, The minimum energy collected by the energy user.

[0082] The rate-energy tradeoff between information users and energy users can be adjusted. This is achieved by solving a series of different values. The corresponding single-objective optimization problem yields the Pareto boundary of the original problem. Due to the strong coupling between antenna placement and power allocation factors, the problem becomes difficult to solve. This implementation uses an alternating optimization algorithm to solve the problem.

[0083] The objective function of the single-objective optimization problem model for the NOMA scheme is: [The function is defined as follows:] [The objective ... The position on the axis and the power allocation coefficient vector of the information user are variables, maximizing the achievable communication rate of the information user. The constraints of the single-objective optimization problem model of the NOMA scheme include: minimum spacing constraint of the clamping antennas, distribution range constraint of the clamping antennas, decoding order constraint of the information user in NOMA, power allocation constraint of the information user, and minimum energy collection constraint of the energy user.

[0084] S4.1.2: Single-objective optimization problem model based on NOMA scheme, which transforms the power allocation coefficient vector of information users. Optimize by setting a fixed power allocation factor. The transformed model of the clamping antenna position optimization sub-problem for the NOMA scheme is obtained as follows:

[0085] The objective function of the single-objective optimization problem model after the conversion of the NOMA scheme is: [The objective function is to] clamp the antenna in... The position on the axis is used as an optimization variable to maximize the minimum achievable communication rate for information users. The constraints of the single-objective optimization problem model after the conversion of the NOMA scheme include: minimum spacing constraint of the clamping antennas, distribution range constraint of the clamping antennas, decoding order constraint of information users in NOMA, and minimum energy collection constraint of energy users.

[0086] S4.1.3: Solve the sub-problem model of the clamping antenna position optimization in the above NOMA scheme using the particle swarm optimization algorithm, and obtain the clamping antenna position... Positioning scheme on the axis.

[0087] Define and initialize a space of size 1. A swarm of particles, where the position of each particle is associated with the positions of all the clamping antennas. For the index of the particle. The first... The initial positions of the particles are represented as follows: ,in, Indicates the first The first particle A clamping antenna in Initial projection coordinates along the axis. To satisfy the antenna position constraints, the initial positions of all clamped antennas are distributed within the region. Inside. Additionally, the first... The initial velocity of a particle is defined as . Indicates the first The first particle The initial velocity of the clamping antenna.

[0088] The system optimizes the antenna position to maximize the particle fitness function, as follows:

[0089] in, , , and It is a punishment factor. , , This represents the effect function of the current antenna position violating the constraints. Indicates the first The optimal position for each individual particle Indicates the first The fitness function value corresponding to each particle.

[0090] The effect function of the current antenna position violating the constraint is expressed as:

[0091] in, This indicates an indicator function; if the condition within the parentheses is true, it equals 1; otherwise, it equals 0. Indicates the first The first particle A clamping antenna in Projected coordinates along the axis.

[0092] To ensure the NOMA decoding order constraint, the decoding order of information users can be determined by exhaustively searching all possible combinations of decoding orders. Furthermore, since the channel gain is related to the particle position, the decoding order needs to be updated in each iteration of the PSO (Particle Swarm Optimization) algorithm.

[0093] To satisfy the constraints of the optimization problem, the penalty factor can be set to a large value. During the iteration process, each particle dynamically updates its individual best position and global best position according to the fitness function. The optimal position of each particle is used The global optimal position of all particles is represented by... express.

[0094] In each iteration, the update equations for the particle's position and velocity are:

[0095] in, For iterative index, For the first The inertia weight in the next iteration represents the degree of confidence in the previous velocity direction. Indicates the first The particle in the first The speed of the next iteration Indicates the first The particle in the first The optimal position of the individual in the next iteration. and To adjust the acceleration constant for the maximum learning step size, and for Two random numbers within the range are used to increase the randomness of the search. As the number of iterations decreases linearly, The inertial weight varies within the upper and lower limits, where and These represent the maximum and minimum values ​​of the inertia weight, respectively.

[0096] The update equation for the dynamic inertia weights is:

[0097] in, This indicates the maximum number of iterations.

[0098] S4.1.4: Single-objective optimization problem model based on NOMA scheme, fixed Establish an optimized power allocation coefficient vector for information users. Model of the power allocation subproblem of the NOMA scheme:

[0099] The objective function of the power allocation subproblem model is to maximize the minimum achievable rate of the information users, using the power allocation coefficient vector of the information users as variables; the constraints include the power allocation constraints of the information users.

[0100] By introducing satisfaction Auxiliary variables As a lower bound of the objective function (the achievable communication rate of information users), the problem can be equivalently transformed into the transformed power allocation subproblem model of the NOMA scheme:

[0101] Among them, constraints It is non-convex.

[0102] In the transformed power allocation subproblem model of the NOMA scheme, the objective function is to maximize the auxiliary variable concerning the achievable communication rate of the information user, using the power allocation coefficient vector of the information user as the variable. The constraints include: power allocation constraints for the information user; and auxiliary variable constraints concerning the achievable communication rate of the information user.

[0103] S4.1.5: To solve the transformed power allocation subproblem model of the NOMA scheme (25), the SCA method is adopted, and auxiliary variables regarding the power allocation of information users are introduced. Relaxing the achievable rate for information users, where and .in, This introduces auxiliary variables related to power distribution. In the SCA's... In each iteration, the achievable rate for information users is at a given local point. Applying a first-order Taylor expansion, we obtain a concave lower bound for the achievable rate.

[0104] Establish the first The expanded expression for the achievable rate for a single information user is:

[0105] By setting a concave lower bound for the achievable rate Substitution constraints The transformed power allocation subproblem model of the NOMA scheme can be transformed into a convex problem:

[0106] The objective function of the problem model is to maximize the auxiliary variable concerning the achievable communication rate of the information user, using the auxiliary variable concerning power allocation as the variable. The constraints include: constraints on the auxiliary variable concerning power allocation and constraints on the auxiliary variable concerning the achievable communication rate of the information user.

[0107] The above problem model (27) can be solved using CVX to obtain its objective function value and power allocation vector. Due to constraints The lower bound is replaced by a concave lower bound, so the derived objective function value can be used as the lower bound of the problem model (25).

[0108] Furthermore, the power allocation scheme for information users is obtained by solving the following equation:

[0109] in, and The optimal solution to problem model (27) is... This represents the optimal solution for the power allocation factor obtained through this method.

[0110] S4.2: Solve the multi-objective optimization problem model of the FDMA scheme and obtain the solution results.

[0111] S4.2.1: Adopt The constraint method introduces the minimum harvested energy for energy users, transforming the objective of maximizing the minimum harvested energy in the FDMA scheme problem model into a minimum energy constraint. The transformed single-objective optimization problem model is as follows:

[0112] In the converted single-objective optimization problem model of the FDMA scheme, the objective function is: [to use the clamping antenna in...] The position on the axis, the frequency resource allocation vector and the power allocation vector of the information user are variables, and the minimum achievable rate of the information user is maximized. The constraints include: minimum spacing constraint of the clamping antenna, distribution range constraint of the clamping antenna, frequency allocation constraint of the information user, maximum transmit power constraint of the information user, and minimum collected energy constraint of the energy user.

[0113] To address the coupling between variables, the original problem is decomposed into an antenna location optimization subproblem and a resource allocation optimization subproblem.

[0114] S4.2.2: Single-objective optimization problem model based on FDMA scheme, where the objective is to allocate vectors using a fixed frequency resource. and power allocation vector To optimize The sub-problem model for optimizing the clamping antenna position in the FDMA scheme is obtained as follows:

[0115] The objective function of this optimization subproblem model is to clamp the antenna in... The position on the axis is a variable, maximizing the minimum achievable rate for information users; constraints include: minimum spacing of clamping antennas, distribution range of clamping antennas, and minimum collectable energy for energy users.

[0116] S4.2.3: Solve the sub-problem model of the clamping antenna position optimization in the above FDMA scheme using the particle swarm optimization algorithm to obtain the position of the clamping antenna. The specific process and method of the particle swarm optimization algorithm in this step can be found in the description in section S4.1.3 above.

[0117] S4.2.4: Single-objective optimization problem model based on the FDMA scheme, given... In this case, the frequency resource allocation vector of the solution information user is obtained. and power allocation vector The sub-problem model introduces auxiliary variables regarding the achievable communication rate of information users. As a lower bound of the objective function, the optimization sub-problem model of the frequency resource allocation vector and power allocation vector of the FDMA scheme is obtained as follows:

[0118] in, .

[0119] The objective function of this optimization subproblem model is to maximize the auxiliary variable concerning the achievable communication rate of the information user, using the frequency resource allocation vector and power allocation vector of the information user as variables. The constraints include the frequency allocation constraint and power allocation constraint of the information user, as well as the auxiliary variable constraint concerning the achievable communication rate of the information user.

[0120] S4.2.5: The frequency resource allocation vector and power allocation vector optimization sub-problem model (31) of the FDMA scheme can be verified as a convex problem, and the above sub-problem model can be solved by CVX to obtain the frequency resource allocation vector of the information user. and power allocation vector The solution.

[0121] S4.3: Solve the multi-objective optimization problem model of the TDMA scheme, introduce the minimum collected energy of the energy user, transform the multi-objective optimization problem model of the TDMA scheme into a single-objective optimization problem model of the TDMA scheme, and use the alternating optimization algorithm to optimize the position and time resource allocation of the clamping antenna respectively. First, fix the time resource allocation to optimize the position of the clamping antenna in each time slot, and then fix the position of the clamping antenna to solve for the optimal time resource allocation, and obtain the solution result of the TDMA scheme.

[0122] S4.3.1: In the TDMA scheme, the following is adopted: The constraint method introduces the minimum harvested energy for energy users, transforming the multi-objective optimization problem model of the TDMA scheme into a single-objective optimization problem model of the TDMA scheme:

[0123] The objective function of this single-objective problem model is to clamp the antenna in... The position on the axis and the time resource allocation vector of the information user are variables, maximizing the minimum achievable rate of the information user; the constraints include: minimum spacing and distribution range constraints of the clamping antennas in each time slot, time resource allocation constraints of the information user (i.e., time allocation factor constraints of the information user), and minimum collection energy constraints of the energy user.

[0124] Unlike NOMA and FDMA, the TDMA scheme allows the antenna to adjust its position in different time slots, thereby maximizing the data rate for the corresponding information users while providing power to all energy users.

[0125] To address this, the implementation scheme employs an alternating optimization algorithm to optimize both the position and time resource allocation of the clamping antenna. First, a fixed time resource allocation is used to optimize the position of the clamping antenna within each time slot. Second, the time resource allocation is optimized by fixing the position of the clamping antenna across all time slots.

[0126] S4.3.2: A single-objective optimization problem model based on the TDMA scheme is used to determine the time resource allocation vector for information users and iteratively optimize the position of the clamping antenna in each time slot. In the... Within each time slot, the clamping antenna positions in other time slots are kept fixed, optimizing the current time slot. To serve the first Information users and all energy users.

[0127] Within a time slot, PASS serves only one information user. Therefore, it is only necessary to optimize the clamping antenna position to maximize the rate of the currently served information user. A sub-problem model for clamping antenna position optimization in the FDMA scheme is established as follows:

[0128] The objective function of the FDMA scheme's clamping antenna position optimization sub-problem model is: [Equation missing - likely related to the objective function of the clamping antenna position optimization within each time slot]. The position on the axis is a variable, maximizing the minimum achievable rate for each information user in each time slot; the constraints include: minimum spacing and distribution range constraints of the clamping antennas in each time slot, and minimum collection energy constraints for energy users.

[0129] Similarly, the sub-problem model for optimizing the clamping antenna position in the FDMA scheme can be solved using the particle swarm optimization algorithm to obtain the clamping antenna position within each time slot. For the specific solution process and method, please refer to the description in section S4.1.3 above.

[0130] S4.3.3: Single-objective optimization problem model based on TDMA scheme, given the time slot... The problem is transformed into obtaining the optimal time resource allocation vector by introducing a solution that satisfies... Auxiliary variables As a lower bound of the objective function, we obtain the subproblem of optimizing the time resource allocation vector for the TDMA scheme:

[0131] in, .

[0132] The objective function of this subproblem model is to maximize the auxiliary variable concerning the achievable communication rate of the information user, using the information user's time resource allocation vector as the variable. The constraints include: the information user's time resource allocation constraint, the auxiliary variable constraint concerning the information user's achievable communication rate, and the energy user's minimum collection energy constraint.

[0133] This subproblem is a convex problem, which can be solved using the CVX tool to obtain the optimal time resource allocation scheme for information users.

[0134] Optionally, such as Figure 3 As shown, in another embodiment, a judgment step may also be included, which sets a parameter threshold. After obtaining the solution result in S4, the change in the solution result is compared with the set parameter threshold. If the change is less than the parameter threshold, the next step is executed; otherwise, the process returns to step S4.1 to solve the problem again.

[0135] S5: The solution obtained from solving the multi-objective optimization problem model is provided to the PASS-assisted airport downlink transmission system to achieve the goal of maximizing the minimum communication rate for information users and the minimum energy collection efficiency for energy users.

[0136] Example 1 The solution obtained in Embodiment 1 of an airport wireless power-carrying communication method assisted by a clamping antenna system provided by an embodiment of the present invention is simulated. The simulation process is as follows.

[0137] The performance of the airport wireless power-carrying communication method assisted by the clamping antenna system in Example 1 is numerically evaluated. GHz, noise power per information user is dBm, the energy harvesting efficiency per energy user is The minimum spacing between the clamping antennas is The waveguide height is m, maximum length set to m, the effective refractive index of the waveguide is set to m. The number of information users is The number of energy users is Furthermore, for the PSO (Particle Swarm Optimization) algorithm, the particle swarm size is set to... , , , , , The convergence threshold is .

[0138] To verify the reliability and effectiveness of the system, the following benchmark is considered: a traditional single-antenna system, A single antenna is deployed at a location and provides services to multiple information and energy users via NOMA, FDMA, or TDMA (labeled "Con1"); traditional MIMO systems, in A radio frequency chain and the same number of fixed antennas as PASS are deployed at the location, with the antennas spaced at half wavelengths, and services are provided to multiple information users and energy users via NOMA, FDMA or TDMA (labeled "Con2").

[0139] exist Figure 4 In the middle, use and The achievable rate-energy regions under multiple information user and energy user scenarios were explored. In the figure, the horizontal axis represents the maximum minimum collected energy (in μm), and the vertical axis represents the maximum minimum information rate (in bps / Hz). PASS-NOMA, PASS-FDMA, and PASS-TDMA are the test results corresponding to the solution results of the three schemes obtained in Example 1. A fundamental trade-off was observed in all multiple access schemes, where the maximum minimum information rate decreases as the maximum minimum collected energy acquired by the energy user increases. This trade-off stems from inherent resource competition, as optimizing the clamp antenna positions and resource allocation to improve energy collection inevitably reduces the achievable information rate for information users. Under the same conditions, the maximum minimum information rate under TDMA is significantly better than NOMA and FDMA. The combination of TDMA and PASS has a unique advantage: it can redeploy all clamp antennas in each time slot to meet energy constraints and maximize the achievable data rate of the served IU. Furthermore, NOMA outperforms FDMA by simultaneously serving all users within the same time-frequency resource block.

[0140] exist Figure 5 The paper demonstrates three multiple access schemes at various transmission powers, assuming a minimum harvesting energy threshold. The graph shows the maximum and minimum achievable data rates, with the horizontal axis representing transmit power in dBm and the vertical axis representing the maximum minimum data rate in bps / Hz. Clearly, compared to traditional single-antenna and MIMO systems, the PASS-assisted SWIPT system can achieve higher maximum and minimum achievable data rates. In traditional systems, TDMA and FDMA have similar maximum and minimum achievable data rates, both lower than NOMA.

[0141] exist Figure 6 Further research was conducted on maximizing the minimum information rate for various multiple access schemes, considering different numbers of clamping antennas and minimum harvesting energy thresholds. In the figure, the horizontal axis represents the number of clamped antennas (in units), and the vertical axis represents the maximum minimum data rate (in bps / Hz). First, it is observed that both the maximum and minimum achievable data rates of PASS and conventional MIMO systems improve with increasing antenna number. This is because higher beamforming gain and the additional spatial degrees of freedom provided by more antennas enhance information transmission and energy harvesting. Furthermore, a key difference from conventional MIMO systems is that the PASS performance gain of TDMA becomes more significant with increasing antenna number compared to NOMA and FDMA. This is because antenna deployment flexibility is amplified with an increase in the number of clamped antennas.

[0142] All of the above-mentioned optional technical solutions can be combined in any way to form optional embodiments of the present invention, and will not be described in detail here.

[0143] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order and method of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0144] It should be understood that the foregoing only illustrates some embodiments, and changes, modifications, additions, and / or variations can be made without departing from the scope and spirit of the disclosed embodiments. These embodiments are illustrative and not restrictive. Furthermore, the described embodiments relate to those currently considered most practical and preferred, and should be understood as not being limited to the disclosed embodiments, but rather intended to cover different modifications and equivalent arrangements included within the spirit and scope of those embodiments. Moreover, the various embodiments described above can be used in conjunction with other embodiments; for example, an aspect of one embodiment can be combined with an aspect of another embodiment to achieve yet another embodiment. Additionally, individual features or components of any given component can constitute another embodiment.

[0145] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. 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 or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for airport wireless power-carrying communication assisted by a clamping antenna system, characterized in that, include: S1: Establish a PASS-assisted airport downlink transmission system, including a base station equipped with a waveguide and multiple single-antenna information users and multiple single-antenna energy users, wherein the waveguide equipped with the base station has multiple clamping antennas; S2: Based on PASS-assisted airport downlink transmission system, establish free space channel vectors from all clamped antennas to each information user and to each energy user, propagation channel vectors in the waveguide, and models of the signals received by each information user and each energy user. S3: Establish multi-objective optimization problem models, including establishing multi-objective optimization problem models for the NOMA scheme, FDMA scheme, and TDMA scheme respectively; S4: Introduce the minimum collected energy of the energy user, transform the multi-objective optimization problem model respectively, and solve it to obtain the solution results; S5: Provide the obtained solution results to the PASS-assisted airport downlink transmission system to achieve communication that maximizes the minimum communication rate for information users and the minimum energy harvesting efficiency for energy users; S4 includes: S4.1: The multi-objective optimization problem model of the NOMA scheme is transformed into the clamping antenna position optimization sub-problem model and the power allocation sub-problem model of the NOMA scheme, and solved by the particle swarm optimization algorithm and the SCA method respectively to obtain the clamping antenna position scheme and the information user power allocation scheme. S4.2: The multi-objective optimization problem model of the FDMA scheme is transformed into the FDMA scheme clamping antenna position optimization sub-problem model, as well as the frequency resource allocation vector and power allocation vector optimization sub-problem model. The clamping antenna position scheme and the frequency resource allocation vector and power allocation vector scheme of the information user are solved by the particle swarm optimization algorithm and CVX respectively. S4.3: The multi-objective optimization problem model of the TDMA scheme is transformed into a TDMA scheme clamping antenna position optimization sub-problem model and a time resource allocation vector optimization sub-problem model, and solved by particle swarm optimization algorithm and CVX respectively to obtain the clamping antenna position scheme and the optimal time allocation scheme for information users in each time slot.

2. The airport wireless power-carrying communication method assisted by the clamping antenna system according to claim 1, characterized in that, S1 also includes: Obtain the orientation and dimensions of the waveguide in a three-dimensional Cartesian coordinate system, as well as the location of the waveguide feed point; wherein the waveguide is parallel to... The axis, dimensions include the waveguide's height and maximum length; The positions of each clamped antenna on the waveguide are obtained, and the positions of each clamped antenna on the waveguide are obtained accordingly. Position on the axis; Obtain the location of each information user and the location of each energy user.

3. The airport wireless power-carrying communication method assisted by the clamping antenna system according to claim 2, characterized in that, S2 include: Based on the location of each information user and each energy user, as well as the location of each clamping antenna on the waveguide, and considering path loss, free space channel vectors from all clamping antennas to each information user and from all clamping antennas to each energy user are established. Based on the clamping antenna Based on the position on the axis and the maximum length of the waveguide, establish the propagation channel vector within the waveguide; Based on the free-space channel vectors from all clamped antennas to each information user and to each energy user, and the propagation channel vectors within the waveguide, considering the Gaussian white noise received by each information user and each energy user, the signals received by each information user and each energy user are established.

4. The airport wireless power-carrying communication method assisted by the clamping antenna system according to claim 1, characterized in that, The multi-objective optimization problem model for the NOMA scheme in S3 includes: The information users are configured to use continuous interference cancellation to decode the desired signal. Based on the signals received by each information user, and considering the power allocation coefficient vector of each information user, the achievable communication rate of each information user is established. Based on the signals received by each energy user, and considering the energy collection efficiency of each energy user, the energy collected by each energy user is established. Establish a multi-objective optimization problem model for the NOMA scheme, where the objective function includes: clamping the antenna in The position on the axis and the power allocation coefficient vector of the information user are variables, maximizing the minimum achievable communication rate of the information user; and using the clamped antenna in... The position on the axis is a variable, maximizing the energy collected by the energy user; the constraints include: minimum spacing constraint of the clamping antenna, distribution range constraint of the clamping antenna, decoding order constraint of the information user in NOMA, and power allocation constraint of the information user.

5. The airport wireless power-carrying communication method assisted by the clamping antenna system according to claim 1, characterized in that, The multi-objective optimization problem model for the FDMA scheme in S3 includes: Based on the signals received by each information user, and considering the frequency allocation factor and power allocation factor of each information user, the achievable communication rate of each information user is established. Based on the signals received by each energy user, and considering the energy collection efficiency of each energy user, the energy collected by each energy user is established. Establish a multi-objective optimization problem model for the FDMA scheme, where the objective function includes: clamping the antenna in Using the position on the axis, the frequency resource allocation vector and power allocation vector of the information user as variables, the goal is to maximize the minimum achievable communication rate of the information user; and to use the clamped antenna in... The position on the axis is a variable, maximizing the energy collected by the energy user; the constraints include: minimum spacing constraint of the clamping antenna, distribution range constraint of the clamping antenna, frequency allocation constraint of the information user, and maximum transmit power constraint of the information user.

6. The airport wireless power-carrying communication method assisted by the clamping antenna system according to claim 1, characterized in that, The multi-objective optimization problem model for the TDMA scheme in S3 includes: Based on the signals received by each information user and the propagation channel vector in the waveguide, and considering the time allocation factor of the information user, the achievable communication rate of each information user is established; wherein, it is set that the base station serves one information user in each time slot, that is, in each time slot, the clamping antenna of the base station sends the signal to the corresponding information user. Based on the signals received by each energy user, considering the energy collection efficiency of the energy user and the time allocation factor of each information user, the energy collected by each energy user in each time slot is established, and then the total energy collected by each energy user in all time slots is established. Establish a multi-objective optimization problem model for the TDMA scheme, where the objective function includes: clamping the antenna in Using the position on the axis and the time resource allocation vector of the information user as variables, the goal is to maximize the minimum achievable communication rate of the information user; and to use the clamped antenna in... The position on the axis is a variable, maximizing the energy collected by the energy user; constraints include: minimum spacing and distribution range constraints of the clamping antennas in each time slot, and time allocation factor constraints of the information user.

7. The airport wireless power-carrying communication method assisted by the clamping antenna system according to claim 1, characterized in that, The solution to the multi-objective optimization problem model for the NOMA scheme in S4 includes: S4.1.1: Introducing the minimum harvested energy for energy users transforms the multi-objective optimization problem model of the NOMA scheme into a single-objective optimization problem model, where the objective function is: [The objective function is to use the clamped antenna in...] The position on the axis and the power allocation coefficient vector of the information user are variables to maximize the achievable communication rate of the information user; the constraints include: minimum spacing constraint of the clamping antenna, distribution range constraint of the clamping antenna, decoding order constraint of the information user in NOMA, power allocation constraint of the information user, and minimum energy collection constraint of the energy user. S4.1.2: Single-objective optimization problem model based on the NOMA scheme. By setting the power allocation coefficient vector of the information user as a fixed power allocation factor, the sub-problem model for optimizing the clamping antenna position under the NOMA scheme is obtained. The objective function is: [Equation missing - likely related to the objective function]. The position on the axis is used as an optimization variable to maximize the minimum achievable communication rate for information users; the constraints include: minimum spacing constraint of clamping antennas, distribution range constraint of clamping antennas, decoding order constraint of information users in NOMA, and minimum energy collection constraint of energy users. S4.1.3: Solve the sub-problem model of the clamping antenna position optimization in the NOMA scheme using the particle swarm optimization algorithm to obtain the position scheme of the clamping antenna; S4.1.4: A single-objective optimization problem model based on the NOMA scheme, with fixed antenna positions and the introduction of an auxiliary variable regarding the achievable communication rate of the information user as a lower bound of the objective function, yields the transformed power allocation sub-problem model of the NOMA scheme. The objective function is: maximizing the auxiliary variable regarding the achievable communication rate of the information user, using the power allocation coefficient vector of the information user as the variable; the constraints include: power allocation constraints for the information user and constraints on the auxiliary variable regarding the achievable communication rate of the information user. S4.1.5: Using the SCA method, an auxiliary variable regarding the power allocation of information users is introduced to solve the power allocation subproblem model of the NOMA scheme, thereby obtaining the power allocation scheme for information users, including the optimal solution of the power allocation factor; The obtained antenna positioning scheme and information user power allocation scheme are the solution results of the multi-objective optimization problem model of the NOMA scheme.

8. The airport wireless power-carrying communication method assisted by the clamping antenna system according to claim 1, characterized in that, The solution to the multi-objective optimization problem model of the FDMA scheme in S4 includes: S4.2.1: Introducing the minimum harvested energy for energy users transforms the multi-objective optimization problem model of the FDMA scheme into a single-objective optimization problem model of the FDMA scheme, where the objective function is: [Equation missing - likely related to the minimum harvested energy of the energy user]. The position on the axis, the frequency resource allocation vector and the power allocation vector of the information user are variables, and the minimum achievable rate of the information user is maximized. The constraints include: minimum spacing constraint of the clamping antenna, distribution range constraint of the clamping antenna, frequency allocation constraint of the information user, maximum transmit power constraint of the information user, and minimum collected energy constraint of the energy user. S4.2.2: Based on the FDMA scheme, a single-objective optimization problem model is derived by fixing the frequency resource allocation vector and power allocation vector of the information user, resulting in the sub-problem model of the clamping antenna position optimization for the FDMA scheme. The objective function is to optimize the clamping antenna position in a position where... The position on the axis is a variable, maximizing the minimum achievable rate for information users; constraints include: the minimum spacing of the clamping antennas, the distribution range of the clamping antennas, and the minimum harvested energy for energy users; S4.2.3: Solve the sub-problem model of the clamping antenna position optimization in the FDMA scheme using the particle swarm optimization algorithm to obtain the position scheme of the clamping antenna; S4.2.4: A single-objective optimization problem model based on the FDMA scheme is introduced. An auxiliary variable regarding the achievable communication rate of the information user is used as the lower bound of the objective function. With the antenna position fixed, a sub-problem model for optimizing the frequency resource allocation vector and power allocation vector of the FDMA scheme is obtained. The objective function is to maximize the auxiliary variable regarding the achievable communication rate of the information user, using the frequency resource allocation vector and power allocation vector of the information user as variables. The constraints include the frequency allocation constraint and power allocation constraint of the information user, as well as the auxiliary variable constraint regarding the achievable communication rate of the information user. S4.2.5: Solve the frequency resource allocation vector and power allocation vector optimization sub-problem model of the FDMA scheme through CVX to obtain the frequency resource allocation vector and power allocation vector scheme of information users; The obtained antenna positioning scheme, frequency resource allocation vector, and power allocation vector scheme for information users are the solution results of the multi-objective optimization problem model of the FDMA scheme.

9. The airport wireless power-carrying communication method assisted by the clamping antenna system according to claim 1, characterized in that, The solution to the multi-objective optimization problem model of the TDMA scheme in S4 includes: S4.3.1: Introducing the minimum harvested energy for energy users transforms the multi-objective optimization problem model of the TDMA scheme into a single-objective optimization problem model of the TDMA scheme, where the objective function is: [Equation missing - likely related to the minimum harvested energy of the energy user]. The position on the axis and the time resource allocation vector of the information user are variables, maximizing the minimum achievable rate of the information user; the constraints include: minimum spacing and distribution range constraints of the clamping antennas in each time slot, time resource allocation constraints of the information user, and minimum collection energy constraints of the energy user. S4.3.2: A single-objective optimization problem model based on the TDMA scheme, fixing the time resource allocation vector of information users, iteratively optimizing the position of the clamping antenna in each time slot, yields the sub-problem model for clamping antenna position optimization in the TDMA scheme, where the objective function is to determine the position of the clamping antenna in each time slot. The position on the axis is a variable, maximizing the minimum achievable rate for each information user in each time slot; the constraints include: minimum spacing and distribution range constraints of the clamping antennas in each time slot, minimum collection energy constraints for energy users; and the clamping antenna position in each time slot is obtained by solving the particle swarm optimization algorithm. S4.3.3: A single-objective optimization problem model based on the TDMA scheme. The clamping antenna positions within each time slot are fixed. An auxiliary variable relating to the achievable communication rate of the information user is introduced as a lower bound of the objective function, resulting in a sub-problem model for optimizing the time resource allocation vector of the TDMA scheme. The objective function is to maximize the auxiliary variable relating to the achievable communication rate of the information user, using the information user's time resource allocation vector as the variable. Constraints include: time resource allocation constraints for the information user, constraints on the auxiliary variable relating to the achievable communication rate of the information user, and minimum collection energy constraints for the energy user. The optimal time allocation scheme for the information user is obtained by solving using CVX. The obtained optimal time allocation scheme for the clamping antenna position and information user in each time slot is the solution result of the multi-objective optimization problem model of the TDMA scheme.

10. An airport wireless power-carrying communication system assisted by a clamping antenna system, used to perform the airport wireless power-carrying communication method assisted by a clamping antenna system as described in any one of claims 1-9, characterized in that, include: A base station equipped with a waveguide, multiple single-antenna information users, multiple single-antenna energy users, and a processor, wherein the waveguide equipped with the base station has multiple clamping antennas, and the processor performs the modeling and calculation functions in S1 to S5.

Images