6G network signal transmission optimization method and device based on star flash network

Abstract

The invention relates to a 6G network signal transmission optimization method and device based on a star flash network, and belongs to the field of wireless networks. Comprising the steps of determining the position of an electromagnetic energy collector through a K-means + + algorithm, calculating an impedance value of a high-impedance antenna, matching the high-impedance antenna and a diode, obtaining standby energy of a diode end, determining an intermediate node from a transmitting end to a receiving end through a Fang algorithm, verifying whether the computing power of the transmitting end and the computing power of the receiving end meet requirements or not by using a computing power quantization model, and if yes, determining that the computing power of the transmitting end and the computing power of the receiving end meet requirements. And verifying whether the standby energy satisfies the transmission energy consumption of the matching network by using a computing power energy consumption quantification model, selecting the matching network with the transmitting end computing power and the receiving end computing power lower than the corresponding idle computing power and the standby energy satisfying the transmission energy consumption of the matching network as a target matching network, and performing 6G network signal transmission by using the target matching network. Through the method, the transmission efficiency is improved, and the loss in the transmission process is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of wireless networks, and in particular to a 6G network signal transmission optimization method and device based on a Star Flash network. Background Art

[0002] 6G network signal transmission refers to wireless data transmission through the sixth generation of mobile communication technology, which is designed to provide higher transmission rates, extremely low latency and seamless global coverage. 6G will support a wide range of application scenarios, such as augmented reality, virtual reality, the Internet of Things and autonomous driving, and will be able to meet future needs for ultra-high speed, low latency and large-scale connections. The Star Flash network may refer to a space-ground integrated communication network that uses low-orbit satellites combined with ground base stations, similar to existing satellite constellation networks such as Starlink. In the 6G era, such networks will supplement the coverage of ground networks through satellites to ensure high-speed and stable communications worldwide, especially in remote areas and oceans where traditional ground base stations cannot cover.

[0003] 6G networks pursue ultra-high speed, ultra-low latency and seamless global coverage. However, the coverage of ground base stations is limited. Especially in remote areas, oceans and air areas that are difficult to reach, traditional ground networks can hardly provide high-quality signal transmission. The optimization method based on Star Flash Network can greatly improve the coverage and reliability of the network and realize true global connection. With the diversification of application scenarios in 6G networks, such as autonomous driving, remote surgery, smart cities, etc., the network must be able to cope with changing user needs, dynamic network loads and harsh environmental conditions.

[0004] However, the energy harvester has a limited pickup range, making it difficult to collect enough energy from distant RF sources. The RF signal is not always present or stable, resulting in the device being unable to continuously obtain energy during non-working cycles. In addition, the performance of the diodes used for energy conversion is limited under low-power conditions, resulting in low energy conversion efficiency for high-impedance antennas, making it impossible to efficiently maintain the energy required for radio wave transmission, which in turn leads to interruptions or unstable signals in the 6G network. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, in order to solve the problems in the prior art that the energy collector has a limited pickup range and it is difficult to collect enough energy from a distant RF source, the RF signal is not always present or stable, resulting in the device being unable to continuously obtain energy during the non-working cycle, and the performance of the diode used for energy conversion is limited under low-power conditions, resulting in the low energy conversion efficiency of the high-impedance antenna, and unable to efficiently maintain the energy required for radio wave transmission, which in turn leads to the technical problem that the 6G network signal is interrupted or the signal is unstable. The present invention provides a 6G network signal transmission optimization method and device based on the Star Flash network.

[0006] In a first aspect of the present invention, a 6G network signal transmission optimization method based on a star flash network is proposed, the method comprising:

[0007] S1, determine the location of electromagnetic energy collector by K-means++ algorithm;

[0008] S2, calculating the impedance value of the high impedance antenna by combining the conductor loss, dielectric loss and radiation loss to match the high impedance antenna and the diode used for rectification, wherein the diode is used to convert the energy collected by the electromagnetic energy collector;

[0009] S3, transmitting the energy collected by the electromagnetic energy collector through the matched high impedance antenna and diode to obtain the spare energy at the diode end;

[0010] S4, combining the star flash model and ultra-wideband transmission mode to locate the transmitter and receiver;

[0011] S5, determining the intermediate nodes from the transmitting end to the receiving end by using the Fang algorithm, wherein the transmitting end, the intermediate nodes and the receiving end form a plurality of matching networks;

[0012] S6, build computing power quantification model and computing power energy consumption quantification model;

[0013] S7, using the computing power quantification model to verify whether the computing power of the transmitter and the computing power of the receiver are lower than the corresponding idle computing power, and using the computing power energy consumption quantification model to verify whether the spare energy meets the matching network transmission energy consumption;

[0014] S8, selecting a matching network whose transmitting end computing power and receiving end computing power are lower than the corresponding idle computing power and whose spare energy satisfies the transmission energy consumption of the matching network as the target matching network;

[0015] S9, using the target matching network to transmit 6G network signals.

[0016] In a second aspect of the present invention, a 6G network signal transmission optimization device based on a Star Flash network is proposed, which is used to implement any of the 6G network signal transmission optimization methods based on a Star Flash network in the first aspect, and the device includes:

[0017] A first positioning module is used to determine the position of the electromagnetic energy collector by using a K-means++ algorithm;

[0018] A matching module, used for calculating the impedance value of the high impedance antenna by combining the conductor loss, dielectric loss and radiation loss, so as to match the high impedance antenna and a diode for rectification, wherein the diode is used for converting the energy collected by the electromagnetic energy collector;

[0019] The first transmission module is used to transmit the energy collected by the electromagnetic energy collector through the matched high impedance antenna and the diode to obtain the spare energy at the diode end;

[0020] The second positioning module is used to locate the transmitter and the receiver by combining the star flash model and the ultra-wideband transmission mode;

[0021] A calculation module, used for determining the intermediate nodes from the transmitting end to the receiving end by using the Fang algorithm, wherein the transmitting end, the intermediate nodes and the receiving end form a plurality of matching networks;

[0022] Modeling module, used to build computing power quantification model and computing power energy consumption quantification model;

[0023] The verification module is used to use the computing power quantification model to verify whether the computing power of the transmitter and the computing power of the receiver are lower than the corresponding idle computing power, and to use the computing power energy consumption quantification model to verify whether the spare energy meets the matching network transmission energy consumption;

[0024] A selection module is used to select a matching network whose transmitting end computing power and receiving end computing power are lower than the corresponding idle computing power and whose spare energy satisfies the transmission energy consumption of the matching network as a target matching network;

[0025] The second transmission module is used to transmit 6G network signals using the target matching network.

[0026] The beneficial effects of the present invention are as follows:

[0027] In an embodiment of the present invention, firstly, the K-means++ algorithm is used to optimize the position layout of the electromagnetic energy collector to expand the range of energy collection and improve the collection efficiency of radio waves. Secondly, by calculating the conductor loss, dielectric loss and radiation loss of the high-impedance antenna, the matching between the high-impedance antenna and the diode is optimized, the efficiency of converting radio waves into DC power is improved, and energy loss is reduced. Finally, the transmitting end and the receiving end are determined and the intermediate nodes are calculated by the Fang algorithm to improve the stability of energy collection and signal transmission. The constraints provided by the computing power quantification model and the computing power energy consumption quantification model are used to adapt and select the intermediate nodes to form a matching network, ensuring that the collected energy and the computing power of the path nodes can meet the 6G network signal transmission to the receiving end, ensuring that the system can work effectively under different spatial conditions and different transmission distances, and improving the continuous stability of 6G network signal transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings are only used to illustrate specific embodiments and are not considered to limit the present invention. In the entire drawings, the same reference symbols represent the same components. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0029] Figure 1 It is a flowchart of a 6G network signal transmission optimization method based on a Star Flash network provided by an embodiment of the present invention;

[0030] Figure 2 It is a structural diagram of selecting an intermediate node through the Fang algorithm provided by an embodiment of the present invention;

[0031] Figure 3 It is a structural schematic diagram of a 6G network signal transmission optimization device based on the Star Flash network provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making creative work should fall within the scope of protection of the present invention.

[0033] Furthermore, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily obscuring the concepts disclosed in the present invention.

[0034] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the orientation or position relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside", etc. is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. In addition, the terms "first", "second", and "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. The terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0035] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of methods and systems consistent with some aspects of the present invention as detailed in the appended claims.

[0036] The present invention proposes a 6G network signal transmission optimization method and device based on the Star Flash network, in order to solve the problems that the existing energy collector has a limited pickup range and it is difficult to collect enough energy from a long-distance RF source. The RF signal is not always present or stable, resulting in the device being unable to continuously obtain energy during the non-working cycle. In addition, the performance of the diode used for energy conversion is limited under low-power conditions, resulting in low energy conversion efficiency of the high-impedance antenna, and unable to efficiently maintain the energy required for radio wave transmission, which in turn leads to problems such as interruption of the 6G network signal or unstable signal.

[0037] Method Embodiment

[0038] Reference Figure 1 , showing a flow chart of a 6G network signal transmission optimization method based on Star Flash network provided in an embodiment of the present invention.

[0039] An embodiment of the present invention provides a 6G network signal transmission optimization method based on a Star Flash network, the method comprising:

[0040] S1, determine the location of the electromagnetic energy collector using the K-means++ algorithm.

[0041] Among them, the K-means++ algorithm is an improved clustering algorithm, which improves the convergence speed and clustering effect of the K-means algorithm by more reasonably selecting the initial clustering center. It iteratively selects data points that are far away from the existing center point as the new center to avoid the inefficiency caused by the randomness of the initial center selection. The electromagnetic energy harvester is a device that can extract energy from radio waves or electromagnetic radiation in the surrounding environment, and is usually used to power or charge low-power devices.

[0042] In a possible implementation, S1 specifically includes:

[0043] S101, obtaining existing placement points of electromagnetic energy collectors.

[0044] S102, randomly selecting multiple initial cluster centers from existing placement points.

[0045] Among them, the existing placement points refer to the set of electromagnetic energy collector locations that are currently available for selection. These points may be candidate locations based on existing data or specific areas. The initial cluster center refers to the initial center point as the starting point in the clustering algorithm, which determines how the algorithm divides the data. In the K-means++ algorithm, the selection of the initial cluster center will affect the subsequent clustering effect.

[0046] S103, calculating the sum of the Euclidean distances between the remaining existing placement points and the initial cluster center:

[0047]

[0048] Where D represents the sum of the Euclidean distance, u i represents the i-th initial cluster center, i = 1, 2, ..., N, where N represents the total number of initial cluster centers, d(x, u i ) represents any existing placement point x and u among the remaining existing placement points i The Euclidean distance of .

[0049] S104, randomly selecting an iterative cluster center from the remaining existing placement points with a selection probability that is proportional to the value of the sum of the Euclidean distances, and updating the centroid position according to the iterative cluster center.

[0050] Among them, the sum of Euclidean distances refers to the sum of all Euclidean distances between the remaining existing placement points and the initial cluster center, representing the overall distance of each point relative to the cluster center. The selection probability is the probability assigned to each remaining point in the K-means++ algorithm in proportion to the sum of the Euclidean distances. The farther the point is from the existing center, the higher the probability of being selected as the new cluster center to avoid excessive concentration of the center. The iterative cluster center is the new center selected from the remaining placement points in each iteration, which is used to gradually optimize the clustering results. The centroid position is the geometric center of each cluster in the cluster, which is updated according to the newly selected cluster center to reflect the center position of the entire cluster.

[0051] It should be noted that through the selection probability that is proportional to the Euclidean distance, the placement point that is farther away from the existing center can be preferentially selected as the new center, resulting in a more uniform distribution of the clustering centers and reducing local optimal problems. The update of the centroid position ensures that each iteration can more accurately reflect the data distribution and improve the rationality and efficiency of the electromagnetic energy collector layout.

[0052] S105, taking the iterative cluster center as the initial cluster center, increasing the number of initial cluster centers by 1, and returning to step S103 until the number of initial cluster centers is greater than the preset number of initial cluster centers.

[0053] S106, determine whether the changes in the positions of adjacent centroids are equal, if they are equal, end the iteration process, otherwise, reset the initial number of cluster centers and return to step S103.

[0054] It should be noted that those skilled in the art can set the size of the preset number of initial cluster centers according to actual needs, and the present invention is not limited thereto.

[0055] In a possible implementation manner, the determination method of S106 is specifically as follows:

[0056]

[0057] in, and They represent the changes in the positions of the nth centroid and the n+1th centroid respectively.

[0058] It should be noted that the K-means++ algorithm is used to optimize the position of electromagnetic energy collectors. By reasonably selecting cluster centers, it ensures that the collectors are arranged in energy-intensive areas, which can significantly improve the efficiency of energy collection, reduce the energy waste caused by the randomness of the collector position, and ensure that the system has a wider coverage and higher energy utilization.

[0059] S2, calculate the impedance value of the high impedance antenna by combining the conductor loss, dielectric loss and radiation loss to match the high impedance antenna and the diode used for rectification.

[0060] Among them, the diode is used to convert the energy collected by the electromagnetic energy collector.

[0061] Among them, conductor loss refers to the energy loss caused by the resistance of the antenna material when a high-impedance antenna transmits electromagnetic waves. The conductor loss is usually related to the material conductivity and surface resistance of the antenna. The dielectric loss is the energy loss caused by the electromagnetic waves passing through the medium (such as air, insulating materials, etc.) when the antenna is working. It is related to the dielectric constant and loss tangent of the medium. Radiation loss refers to the energy consumed when the antenna emits electromagnetic waves into free space. The radiation loss is related to the radiation efficiency and operating frequency of the antenna. A high-impedance antenna is an antenna with a higher impedance characteristic, which is usually used to collect or transmit electromagnetic energy to improve the energy transmission efficiency. The impedance value is the electrical impedance of the antenna or circuit, which represents the total resistance encountered by alternating current when passing through the element. The antenna impedance needs to be matched with the rectifier circuit to achieve maximum energy transmission. A diode is a nonlinear element that can convert AC signals into DC signals. In an electromagnetic energy harvester, a diode is used to rectify the RF energy collected by the antenna into usable DC power.

[0062] S201, obtain the conductor loss, dielectric loss and radiation loss of the high impedance antenna during energy transmission:

[0063]

[0064]

[0065]

[0066] Among them, α c represents the conductor loss, W, Z0, μ0, σ and ω represent the width, characteristic impedance, free space permeability, conductivity and angular frequency of the high impedance antenna respectively, and R s Represents the surface resistivity of the conductor, α d represents dielectric loss, k0 and tanδ represent the free space wave number and the tangent value of dielectric loss angle δ, respectively, ε r and ε e represent the relative permittivity and effective permittivity, respectively.

[0067] S202, calculate the quality factor of the high impedance antenna based on conductor loss, dielectric loss and radiation loss:

[0068]

[0069] α T =α c +α d +α r

[0070] Where Q is the quality factor, β is the phase constant, and α is the T represents the intermediate variable, α r Represents radiation loss.

[0071] Among them, the quality factor is an important parameter of the antenna, which is used to measure the ratio of the antenna's energy storage capacity to its energy loss. A higher quality factor usually indicates lower antenna loss and higher energy conversion efficiency. The phase constant is a parameter that describes the rate of change of the phase of electromagnetic wave propagation and affects the electromagnetic wave transmission characteristics of the antenna.

[0072] It should be noted that by comprehensively considering conductor loss, dielectric loss and radiation loss, the quality factor of the high-impedance antenna can be accurately calculated, which helps to evaluate the overall energy transmission efficiency of the antenna and optimize the design and debugging of the antenna to ensure that the loss is minimized and the efficiency is maximized, thereby improving the collection and transmission performance of electromagnetic energy.

[0073] S203, calculate the insertion loss of the high impedance antenna:

[0074]

[0075]

[0076]

[0077] Where IL is the insertion loss, P in , P L and P diss Represents input power, load power and power loss respectively, Q m and Q r They represent the designed Q value and the required Q value for matching, R low and R high They represent the lowest impedance of the circuit, which represents the impedance of the high-impedance antenna, and the highest impedance of the circuit, which represents the resistance of the nonlinear junction.

[0078] Among them, insertion loss refers to the signal power loss caused by the introduction of antennas or other components in the circuit, reflecting the energy loss of the signal when passing through the antenna or device, and input power refers to the initial power transmitted to the high-impedance antenna, that is, the amount of electrical energy before entering the antenna.

[0079] It should be noted that by calculating the insertion loss, the loss of high-impedance antennas in energy transmission can be quantified, thereby better optimizing the antenna design and matching network. By evaluating the loss between input power and load power, the parameters of circuits and components can be accurately adjusted, resulting in the energy harvester having minimal loss during transmission, maximizing energy utilization, and improving the overall efficiency of the system.

[0080] S204, solving the minimum impedance of the circuit when the quality factor is maximum, and using the minimum impedance of the circuit as the impedance value of the high impedance antenna.

[0081] It should be noted that the impedance value of the high-impedance antenna is accurately calculated by combining the conductor loss, dielectric loss and radiation loss, so that the antenna and the diode in the rectifier circuit can achieve good impedance matching. Matching optimization reduces the loss in the energy transmission process, improves the efficiency of energy collection, and makes the energy conversion rate extracted from electromagnetic waves higher, thereby improving the performance and stability of the overall energy collection system.

[0082] S3, the energy collected by the electromagnetic energy collector is transmitted through the matched high impedance antenna and the diode to obtain the spare energy at the diode end.

[0083] It should be noted that by matching the optimized high-impedance antenna and diode, the energy transmission efficiency is significantly improved, allowing the electromagnetic energy harvester to efficiently extract and transmit energy from the environment and convert it into backup electrical energy. This not only improves the energy conversion efficiency, but also enables continuous acquisition of backup energy under unstable RF signals, thereby enhancing the system's reliability and power supply stability.

[0084] S4, combines the star flash model and ultra-wideband transmission mode to locate the transmitter and receiver.

[0085] Among them, the star flash model is a model used to optimize the communication path between network nodes. It usually combines the transmission characteristics of satellites and ground base stations to improve the coverage and efficiency of the communication network. The model optimizes signal transmission by analyzing multi-hop paths and node selection in wireless communications. The ultra-wideband transmission mode is a high-frequency, low-power wireless transmission technology that can transmit data at a high rate over a wide spectrum range. It is usually used for short-distance, high-precision positioning and communication. The transmitter refers to the starting device or node that sends the signal, which is usually the source of data or energy. The receiver refers to the terminal device or node that receives the signal, which is responsible for receiving and processing data or energy from the transmitter.

[0086] It should be noted that the combination of the star flash model and the ultra-wideband transmission mode can accurately locate the transmitter and receiver and optimize the signal transmission path. The star flash model uses multi-node and multi-hop communications, combined with the high precision and low latency of ultra-wideband transmission, resulting in faster and more stable signal transmission and a wider coverage. This method effectively reduces the delay and energy loss in signal transmission, improves the overall performance and efficiency of the network, and is particularly suitable for high-bandwidth applications such as 6G networks.

[0087] Reference Figure 2 , shows a structural schematic diagram of selecting an intermediate node through the Fang algorithm provided by an embodiment of the present invention.

[0088] Figure 2 In, R i is a known node, T is a node to be tested, r i is the distance from the known node to the node to be measured T. Fang algorithm is used to solve the hyperbolic equations. Fang algorithm lists the distance equation through geometric relationships, obtains the hyperbolic equation by conversion, and respectively substitutes the coordinates of the origin R1, the transmitting end R2 and the receiving end R3 into the hyperbolic equation to solve the coordinates (x, y) of the intermediate node T.

[0089] S5, determine the intermediate nodes from the transmitter to the receiver through the Fang algorithm.

[0090] Among them, the transmitting end, the intermediate node and the receiving end form multiple matching networks.

[0091] In a possible implementation, S5 specifically includes:

[0092] S501, establish a hyperbolic equation based on the transmitting end and the receiving end:

[0093]

[0094] r i 2 =(x i -x)2 +(y i -y) 2

[0095]

[0096] r i 2 =(x i -x) 2 +(y i -y) 2 =K i -2x i x-2y i y+x 2 +y 2 (i=1,2,3)

[0097] r i,1 =r i -r1

[0098] r i 2 =(r i,1 +r1) 2

[0099]

[0100] i=1,r1 2 =K1-2x1x-2y1y+x 2 +y 2

[0101] Let i = 2 and i = 3 respectively, then

[0102]

[0103]

[0104] y=gx+h

[0105] Among them, (x i ,y i ) represents a known node R i The coordinates of the node R1 are (x1, y1), (x2, y2) and (x3, y3). i Represents the distance between the known node and the node to be tested, r i,1 , g, and h all represent intermediate variables;

[0106] S502, solving the hyperbolic equation and determining the intermediate node;

[0107] S503, starting from the transmitting device, passing through the intermediate nodes to reach the receiving end, and obtaining multiple initial matching networks;

[0108] S504, calculating the actual duration of star flash transmission for transmitting the same data through the ultra-wideband transmission mode and the star flash model in different initial matching networks;

[0109] S505, selecting an initial matching network whose actual star flash transmission duration is less than a preset star flash duration as a matching network output.

[0110] It should be noted that those skilled in the art can set the preset star flash transmission duration according to actual needs, and the present invention is not limited thereto.

[0111] Specifically, through the Fang algorithm and the preset star flash transmission duration, the optimal intermediate node can be quickly determined to ensure that the signal is transmitted through the optimal path in the shortest time. This step reduces the delay and energy consumption in the transmission process, optimizes network performance, and ensures efficient transmission of signals.

[0112] S6, build computing power quantification model and computing power energy consumption quantification model.

[0113] Among them, after the computing power quantification model matches different computing power requirements for network business scenarios, it uniformly quantifies the computing power and provides a reliable link load indicator for the link transmission of network nodes. The larger the idle computing power, the better. The unified quantification of computing power is the basis for computing power scheduling and use. After calculating the idle computing power of each link through the model, the link load is "dynamically" selected to map the idle computing power, so that the utilization of each link is more balanced, thereby improving the performance of the application (FCT, throughput). The computing power energy consumption quantification model is used to consider the diversity of users and structural complexity in computing power tasks in heterogeneous network scenarios from a global perspective, including (wireless transmission energy consumption, edge computing node energy consumption, wide area network transmission energy consumption, cloud data center energy consumption) The total energy consumption of four parts of possible docking application scenarios and related networks is used as the minimum energy consumption standard.

[0114] In a possible implementation, the computing power quantization model is specifically:

[0115]

[0116] Among them, C br represents the total computing power requirement, f(a i )、f(b j ) and f(c k ) respectively represent the i 、b j and c k The mapping function, a i 、b j and ck They represent the logical computing capability of the i-th logical computing chip a, the parallel computing capability of the j-th parallel computing chip b, and the neural network acceleration capability of the k-th neural network acceleration chip b, respectively. i=1,2,…n,j=1,2,…m,k=1,2,…p,where n, m, and p represent the total number of logical computing chips, the total number of parallel computing chips, and the total number of neural network acceleration chips, respectively. α i , β j and γ k They represent f(a i )、f(b j ) and f(c k ), q1 represents the redundant computing power of logical calculation, q2 represents the redundant computing power of parallel calculation, q3 represents the redundant computing power of neural network acceleration, TOPS represents the number of integer operations per second, and FLOPS represents the number of floating-point operations per second.

[0117] In a possible implementation, the computing power energy consumption quantification model is specifically as follows:

[0118]

[0119]

[0120]

[0121]

[0122]

[0123] Among them, E i represents the total computing power consumption of the i-th task, represents the wireless transmission energy consumption, represents the edge computing energy consumption, represents the WAN transmission energy consumption, represents the energy consumption of cloud data center, P W It represents the energy consumed by transmitting a unit bit of data through an infinite link, D i represents the unit bit data volume of task i, η e represents the energy consumption factor of the CPU revolutions per unit of the edge node server, λ i represents the allocation ratio of the i-th task on the edge node, C i represents the complexity of the i-th task, P 1 It represents the energy consumed by the WAN link to transmit a unit bit of data, η c Indicates the energy consumption factor of the CPU revolutions per server in a cloud data center.

[0124] It should be noted that by accurately evaluating the computing power and energy consumption requirements of the transmitter and receiver, we can ensure efficient use of network resources, avoid waste of computing power and excessive energy consumption, thereby improving the computing efficiency and energy management capabilities of the overall system, and achieving smarter resource scheduling and longer device battery life.

[0125] S7, using the computing power quantification model to verify whether the computing power of the transmitter and the computing power of the receiver are lower than the corresponding idle computing power, and using the computing power energy consumption quantification model to verify whether the spare energy meets the matching network transmission energy consumption;

[0126] Among them, idle computing power refers to the remaining computing power of the device that has not been utilized under the current task load and can be allocated to additional tasks or loads.

[0127] It should be noted that the computing power quantification model and the computing power energy consumption quantification model can effectively evaluate the computing load and energy consumption of the transmitter and receiver, ensuring that the system can still reasonably allocate computing tasks when energy resources are limited, avoiding energy shortages or waste of computing power, thereby improving the system's operating stability and energy efficiency.

[0128] S8, selecting a matching network whose transmitting end computing power and receiving end computing power are lower than the corresponding idle computing power and whose spare energy satisfies the transmission energy consumption of the matching network as the target matching network;

[0129] Specifically, by selecting a matching network in which the computing power of both the transmitter and the receiver is lower than the idle computing power and the spare energy meets the transmission energy consumption, it is possible to ensure efficient resource utilization and avoid resource overload. By balancing the computing power demand and energy consumption conditions, efficient energy utilization and stable system operation are achieved, optimizing the performance of the 6G network and ensuring reliable data transmission under limited energy conditions.

[0130] S9, using the target matching network to transmit 6G network signals.

[0131] It should be noted that selecting the target matching network for 6G network signal transmission maximizes the resource utilization of the network, reduces latency and energy waste, and ensures high reliability and low latency during data transmission, which is suitable for the strict requirements of high speed, low power consumption and wide coverage in 6G networks.

[0132] The beneficial effects of the present invention are as follows:

[0133] In an embodiment of the present invention, firstly, the K-means++ algorithm is used to optimize the position layout of the electromagnetic energy collector to expand the range of energy collection and improve the collection efficiency of radio waves. Secondly, by calculating the conductor loss, dielectric loss and radiation loss of the high-impedance antenna, the matching between the high-impedance antenna and the diode is optimized, the efficiency of converting radio waves into DC power is improved, and energy loss is reduced. Finally, the transmitting end and the receiving end are determined and the intermediate nodes are calculated by the Fang algorithm to improve the stability of energy collection and signal transmission. The constraints provided by the computing power quantification model and the computing power energy consumption quantification model are used to adapt and select the intermediate nodes to form a matching network, ensuring that the collected energy and the computing power of the path nodes can meet the 6G network signal transmission to the receiving end, ensuring that the system can work effectively under different spatial conditions and different transmission distances, and improving the continuous stability of 6G network signal transmission.

[0134] Device Embodiment

[0135] Reference Manual Attached Figure 3 , showing a structural schematic diagram of a 6G network signal transmission optimization device based on Star Flash network provided in an embodiment of the present invention.

[0136] Another specific embodiment of the present invention discloses a 6G network signal transmission optimization device based on a Star Flash network, which is used to implement any one of the 6G network signal transmission optimization methods based on a Star Flash network in the method embodiments. The 6G network signal transmission optimization device 20 based on a Star Flash network includes:

[0137] The first positioning module 201 is used to determine the position of the electromagnetic energy collector by using the K-means++ algorithm.

[0138] The matching module 202 is used to calculate the impedance value of the high impedance antenna by combining the conductor loss, dielectric loss and radiation loss to match the high impedance antenna and the diode used for rectification, wherein the diode is used to convert the energy collected by the electromagnetic energy collector.

[0139] The first transmission module 203 is used to transmit the energy collected by the electromagnetic energy collector through the matched high-impedance antenna and the diode to obtain the spare energy at the diode end.

[0140] The second positioning module 204 is used to locate the transmitting end and the receiving end by combining the star flash model and the ultra-wideband transmission mode.

[0141] The calculation module 205 is used to determine the intermediate nodes from the transmitting end to the receiving end by using the Fang algorithm, wherein the transmitting end, the intermediate nodes and the receiving end form a plurality of matching networks.

[0142] The modeling module 206 is used to construct a computing power quantification model and a computing power energy consumption quantification model.

[0143] Verification module 207 is used to verify whether the computing power of the transmitter and the computing power of the receiver are lower than the corresponding idle computing power using the computing power quantification model, and to verify whether the spare energy meets the matching network transmission energy consumption using the computing power energy consumption quantification model.

[0144] The selection module 208 is used to select a matching network whose transmitting end computing power and receiving end computing power are lower than the corresponding idle computing power and whose spare energy satisfies the transmission energy consumption of the matching network as a target matching network.

[0145] The second transmission module 209 is used to transmit 6G network signals using the target matching network.

[0146] The beneficial effects of the present invention are as follows:

[0147] In an embodiment of the present invention, firstly, the K-means++ algorithm is used to optimize the position layout of the electromagnetic energy collector to expand the range of energy collection and improve the collection efficiency of radio waves. Secondly, by calculating the conductor loss, dielectric loss and radiation loss of the high-impedance antenna, the matching between the high-impedance antenna and the diode is optimized, the efficiency of converting radio waves into DC power is improved, and energy loss is reduced. Finally, the transmitting end and the receiving end are determined and the intermediate nodes are calculated by the Fang algorithm to improve the stability of energy collection and signal transmission. The constraints provided by the computing power quantification model and the computing power energy consumption quantification model are used to adapt and select the intermediate nodes to form a matching network, ensuring that the collected energy and the computing power of the path nodes can meet the 6G network signal transmission to the receiving end, ensuring that the system can work effectively under different spatial conditions and different transmission distances, and improving the continuous stability of 6G network signal transmission.

[0148] The applicant of the present invention has made a detailed explanation and description of the implementation examples of the present invention in conjunction with the drawings in the specification. However, those skilled in the art should understand that the above implementation examples are only preferred implementation schemes of the present invention, and the detailed description is only to help readers better understand the spirit of the present invention, but not to limit the scope of protection of the present invention. On the contrary, any improvements or modifications based on the inventive spirit of the present invention should fall within the scope of protection of the present invention.

[0149] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the above embodiments, or replace some of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. Any changes or replacements that can be easily thought of by those skilled in the art within the technical scope disclosed in the present invention should be covered within the protection scope of the present invention.

Claims

1. A 6G network signal transmission optimization method based on Star Flash network, characterized in that the method include: S1, determine the location of electromagnetic energy collector by K-means++ algorithm; S2, calculating the impedance value of the high-impedance antenna in combination with the conductor loss, dielectric loss and radiation loss to match the high-impedance antenna and a diode for rectification, wherein the diode is used to convert the energy collected by the electromagnetic energy collector; S3, transmitting the energy collected by the electromagnetic energy collector through the matched high impedance antenna and diode to obtain the spare energy at the diode end; S4, combining the star flash model and ultra-wideband transmission mode to locate the transmitter and receiver; S5, determining an intermediate node from the transmitting end to the receiving end by using a Fang algorithm, wherein the transmitting end, the intermediate node and the receiving end form a plurality of matching networks; S6, build computing power quantification model and computing power energy consumption quantification model; S7, using the computing power quantification model to verify whether the computing power of the transmitter and the computing power of the receiver are lower than the corresponding idle computing power, and using the computing power energy consumption quantification model to verify whether the backup energy meets the matching network transmission energy consumption; S8, selecting a matching network whose computing power of the transmitting end and the computing power of the receiving end are lower than the corresponding idle computing power and whose spare energy satisfies the transmission energy consumption of the matching network as a target matching network; S9, using the target matching network to transmit 6G network signals.

2. According to claim 1, the 6G network signal transmission optimization method based on the Star Flash network is characterized in that: The S1 specifically includes: S101, obtaining an existing placement point of an electromagnetic energy collector; S102, randomly selecting a plurality of initial cluster centers from the existing placement points; S103, calculating the sum of the Euclidean distances between the remaining existing placement points and the initial cluster center: Where D represents the sum of the Euclidean distances, u i represents the i-th initial cluster center, i = 1, 2, ..., N, where N represents the total number of initial cluster centers, d(x, u i ) represents any existing placement point x and u among the remaining existing placement points i The Euclidean distance of S104, randomly selecting an iterative cluster center from the remaining existing placement points with a selection probability that is proportional to the value of the sum of the Euclidean distances, and updating the centroid position according to the iterative cluster center; S105, taking the iterative cluster center as the initial cluster center, adding 1 to the number of the initial cluster centers, and returning to step S103 until the number of the initial cluster centers is greater than the preset number of the initial cluster centers; S106, determine whether the changes in the positions of adjacent centroids are equal, if they are equal, end the iteration process, otherwise, reset the initial number of cluster centers and return to step S103.

3. The 6G network signal transmission optimization method based on the Star Flash network according to claim 2 is characterized in that: The determination method of S106 is specifically as follows: in, and They represent the changes in the positions of the nth centroid and the n+1th centroid respectively.

4. The 6G network signal transmission optimization method based on the Star Flash network according to claim 1 is characterized in that: The S2 specifically includes: S201, obtaining the conductor loss, dielectric loss and radiation loss of the high impedance antenna during energy transmission: Among them, α c represents the conductor loss, W, Z0, μ0, σ and ω represent the width, characteristic impedance, free space permeability, conductivity and angular frequency of the high impedance antenna respectively, and R s Represents the surface resistivity of the conductor, α d represents the dielectric loss, k0 and tanδ represent the free space wave number and the tangent value of the dielectric loss angle δ, respectively, ε r and ε e represent the relative dielectric constant and the effective dielectric constant respectively; S202, calculating the quality factor of the high impedance antenna based on the conductor loss, the dielectric loss and the radiation loss: α T ＝α c +α d +α r Wherein, Q represents the quality factor, β represents the phase constant, α T represents the intermediate variable, α r represents the radiation loss; S203, calculating the insertion loss of the high impedance antenna: Where IL represents the insertion loss, P in , P L and P diss Represents input power, load power and power loss respectively, Q m and Q r They represent the designed Q value and the required Q value for matching, R low and R high They represent the lowest impedance of the circuit representing the impedance of the high impedance antenna and the highest impedance of the circuit representing the resistance of the nonlinear junction respectively; S204, solving the minimum impedance of the circuit when the quality factor is maximum, and using the minimum impedance of the circuit as the impedance value of the high impedance antenna.

5. The 6G network signal transmission optimization method based on the Star Flash network according to claim 1 is characterized in that: The S5 specifically includes: S501, establishing a hyperbolic equation according to the transmitting end and the receiving end: r i 2 =(x i -x) 2 +(y i -y) 2 r i 2 =(x i -x) 2 +(y i -y) 2 =K i -2x i x-2y i y+x 2 +y 2 (i=1,2,3) r i,1 ＝r i -r1 r i 2 ＝(r i,1 +r1) 2 i=1,r1 2 =K1-2x1x-2y1y+x 2 +y 2 Let i = 2 and i = 3 respectively, then y=gx+h Among them, (x i ,y i ) represents a known node R i The coordinates of the node R1 are (x1, y1), (x2, y2) and (x3, y3). i Represents the distance between the known node and the node to be tested, r i,1 , g, and h all represent intermediate variables; S502, solving the hyperbolic equation to determine the intermediate node; S503, starting from the transmitting device and reaching the receiving end via the intermediate node, to obtain multiple initial matching networks; S504, calculating the actual duration of star flash transmission for transmitting the same data through the ultra-wideband transmission mode and the star flash model in different initial matching networks; S505, selecting an initial matching network whose actual star flash transmission duration is less than a preset star flash duration as the matching network output.

6. The 6G network signal transmission optimization method based on the Star Flash network according to claim 1 is characterized in that: The computing power quantification model is specifically: Among them, C br represents the total computing power requirement, f(a i )、f(b j ) and f(c k ) respectively represent the i 、b j and c k The mapping function, a i 、b j and c k They represent the logical computing capability of the i-th logical computing chip a, the parallel computing capability of the j-th parallel computing chip b, and the neural network acceleration capability of the k-th neural network acceleration chip b, respectively. i=1,2,…n,j=1,2,…m,k=1,2,…p,where n, m, and p represent the total number of logical computing chips, the total number of parallel computing chips, and the total number of neural network acceleration chips, respectively. α i , β j and γ k They represent f(a i )、f(b j ) and f(c k ), q1 represents the redundant computing power of logical calculation, q2 represents the redundant computing power of parallel calculation, q3 represents the redundant computing power of neural network acceleration, TOPS represents the number of integer operations per second, and FLOPS represents the number of floating-point operations per second.

7. The 6G network signal transmission optimization method based on the Star Flash network according to claim 1 is characterized in that: The computing power energy consumption quantification model is specifically as follows: Among them, E i represents the total computing power consumption of the i-th task, represents the wireless transmission energy consumption, represents the edge computing energy consumption, represents the WAN transmission energy consumption, represents the energy consumption of cloud data center, P W It represents the energy consumed by transmitting a unit bit of data through an infinite link, D i represents the unit bit data volume of task i, η e represents the energy consumption factor of the CPU revolutions per unit of the edge node server, λ i represents the allocation ratio of the i-th task on the edge node, C i represents the complexity of the i-th task, P 1 It represents the energy consumed by the WAN link to transmit a unit bit of data, η c Indicates the energy consumption factor of the CPU revolutions per server in a cloud data center.

8. A 6G network signal transmission optimization device based on Star Flash Network, characterized in that: The device includes: A first positioning module is used to determine the position of the electromagnetic energy collector by using a K-means++ algorithm; A matching module, used to calculate the impedance value of the high-impedance antenna by combining the conductor loss, dielectric loss and radiation loss, so as to match the high-impedance antenna and a diode for rectification, wherein the diode is used to convert the energy collected by the electromagnetic energy collector; The first transmission module is used to transmit the energy collected by the electromagnetic energy collector through the matched high impedance antenna and the diode to obtain the spare energy at the diode end; The second positioning module is used to locate the transmitter and the receiver by combining the star flash model and the ultra-wideband transmission mode; A calculation module, used to determine the intermediate nodes from the transmitting end to the receiving end by using a Fang algorithm, wherein the transmitting end, the intermediate nodes and the receiving end form a plurality of matching networks; Modeling module, used to build computing power quantification model and computing power energy consumption quantification model; A verification module, used to verify whether the computing power of the transmitting end and the computing power of the receiving end are lower than the corresponding idle computing power by using the computing power quantification model, and to verify whether the backup energy meets the matching network transmission energy consumption by using the computing power energy consumption quantification model; A selection module is used to select a matching network whose computing power of the transmitting end and the computing power of the receiving end are lower than the corresponding idle computing power and whose spare energy satisfies the transmission energy consumption of the matching network as a target matching network; The second transmission module is used to transmit 6G network signals using the target matching network.

9. The 6G network signal transmission optimization device based on the Star Flash network according to claim 8 is characterized in that: The first positioning module is specifically used for: An acquisition submodule, used for acquiring existing placement points of electromagnetic energy collectors; A selection submodule, used for randomly selecting a plurality of initial cluster centers from the existing placement points; The calculation submodule is used to calculate the sum of the Euclidean distances between the remaining existing placement points and the initial cluster center: Where D represents the sum of the Euclidean distances, u i represents the i-th initial cluster center, i = 1, 2, ..., N, where N represents the total number of initial cluster centers, d(x, u i ) represents any existing placement point x and u among the remaining existing placement points i The Euclidean distance of An updating submodule, configured to randomly select an iterative cluster center from the remaining existing placement points with a selection probability that is proportional to the value of the sum of the Euclidean distances, and update the centroid position according to the iterative cluster center; The return submodule is used to use the iterative cluster center as the initial cluster center, the number of the initial cluster centers is increased by 1, and the calculation submodule is called until the number of the initial cluster centers is greater than the preset number of the initial cluster centers; The judgment submodule is used to judge whether the changes in the positions of adjacent centroids are equal. If they are equal, the iteration process is terminated; otherwise, the initial number of cluster centers is reset and the calculation submodule is called.

10. The 6G network signal transmission optimization method based on the Star Flash network according to claim 9 is characterized in that: The judgment method of the judgment submodule is specifically as follows: in, and They represent the changes in the positions of the nth centroid and the n+1th centroid respectively.

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