A mobile phone and satellite direct connection communication optimization method and device and storage medium

Abstract

This application discloses a method, apparatus, and storage medium for optimizing direct communication between a mobile phone and a satellite, relating to the field of satellite communication technology. The method includes: defining a particle vector related to direct communication between the mobile phone and the satellite, where the elements of the particle vector correspond to the uplink transmit power and uplink carrier frequency of the mobile phone, respectively; initializing a particle swarm based on the particle vector; constructing a fitness function, where the fitness value is related to transmission efficiency cost and energy consumption cost, wherein the transmission efficiency cost is used to evaluate the loss of data transmission efficiency, and the energy consumption cost is used to reflect the energy consumption cost of the mobile phone; iteratively optimizing the particle swarm using a particle swarm optimization algorithm to determine the optimal particle vector; and establishing a direct communication connection between the mobile phone and the satellite based on the optimal particle vector. Thus, this application achieves joint dynamic optimization of the mobile phone's uplink transmit power and uplink carrier frequency, reducing terminal energy consumption while ensuring the transmission efficiency of the satellite-to-ground link.

Description

Technical Field

[0001] This application relates to the field of satellite communication technology, and in particular to a method, apparatus, and storage medium for optimizing direct communication between a mobile phone and a satellite. Background Technology

[0002] With the rapid development of low-orbit satellite communication technology, direct connection between satellite and mobile phone has become a key technology path to achieve seamless communication across all regions. It can effectively solve the communication needs of remote areas, oceans, and airspace where ground communication networks have no coverage. It has irreplaceable application value in scenarios such as emergency communication, outdoor exploration, and ocean voyages.

[0003] However, this communication method is limited by various inherent conditions, making the conflict between data transmission efficiency and mobile phone battery life particularly prominent. Specifically, in remote mountainous areas, oceans, deserts, and other areas where terrestrial communication networks cannot cover, mobile terminals need to connect directly to low-Earth orbit satellites to achieve emergency communication, location reporting, or data transmission. In these scenarios, the mobile terminal's battery capacity is limited and cannot be charged in a timely manner. At the same time, the satellite-to-ground link suffers from free-space path loss of hundreds to thousands of kilometers, resulting in severe uplink signal attenuation. If the mobile phone transmits at too high a power, although reliable satellite reception can be guaranteed, it will rapidly consume power and shorten battery life; if it transmits at too low a power, the satellite may not be able to demodulate correctly, leading to communication failure. Therefore, it is necessary to minimize terminal power consumption and extend device operating time while ensuring link transmission efficiency.

[0004] For example, the invention with publication number CN121567175A, entitled "A Large-Scale Folded Spaceborne Mobile Phone Direct Satellite Communication Phased Array," includes a rear-stage DBF system and several antenna subarrays. The antenna subarrays include multiple antenna elements and a subarray-level DBF system. Each antenna element includes a transceiver antenna array, a radio frequency transceiver front-end, a multi-channel transceiver chip, and a multi-channel AD / DA chip connected in sequence. Each multi-channel AD / DA chip is connected to the subarray-level DBF system. The subarray-level DBF system is connected to the rear-stage DBF system.

[0005] For example, in the invention disclosed in CN118713727A, entitled "Communication Method, Satellite, Terminal, Device, and Storage Medium for Direct Connection of Terminal to Satellite," the method includes: receiving a first communication request sent by a source terminal; determining whether the source terminal and the target terminal are authorized to access the satellite core network; in response to the source terminal and the target terminal being authorized to access the satellite core network, determining whether the target communication mode is a dedicated network communication mode; in response to the target communication mode being a dedicated network communication mode, establishing a dedicated communication network between the source terminal and the target terminal based on itself as a jump node, and forwarding the first communication request to the target terminal to establish a communication connection between the source terminal and the target terminal.

[0006] There is currently no effective solution to the technical problem of the inability to dynamically balance transmission efficiency and terminal power consumption in direct communication between mobile phones and satellites, as mentioned above in the existing technologies. Summary of the Invention

[0007] The embodiments of this disclosure provide a method, apparatus, and storage medium for optimizing direct communication between mobile phones and satellites, so as to at least solve the technical problem in the prior art that the transmission efficiency and terminal power consumption cannot be dynamically balanced in direct communication between mobile phones and satellites.

[0008] According to one aspect of the present disclosure, a method for optimizing direct communication between a mobile phone and a satellite is provided, comprising: defining a particle vector related to the direct communication between the mobile phone and the satellite, wherein the elements of the particle vector correspond to the uplink transmission power and uplink carrier frequency of the mobile phone, respectively; initializing a particle swarm based on the particle vector; constructing a fitness function, wherein the fitness value of the fitness function is related to the transmission efficiency cost and the energy consumption cost, wherein the transmission efficiency cost is used to evaluate the loss of data transmission efficiency, and the energy consumption cost is used to reflect the energy consumption cost of the mobile phone; iteratively optimizing the particle swarm according to a particle swarm optimization algorithm to determine the optimal particle vector; and establishing a direct communication connection between the mobile phone and the satellite based on the optimal particle vector.

[0009] According to another aspect of the present disclosure, a storage medium is also provided, the storage medium including a stored program, wherein, when the program is executed, a processor performs any of the methods described above.

[0010] According to another aspect of the present disclosure, a mobile phone and satellite direct communication optimization device is also provided, comprising: a particle vector definition module for defining particle vectors related to mobile phone and satellite direct communication, wherein the elements of the particle vectors correspond to the uplink transmission power and uplink carrier frequency of the mobile phone, respectively; a particle swarm initialization module for initializing a particle swarm based on the particle vectors; a fitness function construction module for constructing a fitness function, wherein the fitness value of the fitness function is related to the transmission efficiency cost and the energy consumption cost, wherein the transmission efficiency cost is used to evaluate the loss of data transmission efficiency, and the energy consumption cost is used to reflect the energy consumption cost of the mobile phone; an iterative optimization module for iteratively optimizing the particle swarm according to a particle swarm optimization algorithm to determine the optimal particle vector; and a communication connection module for establishing a direct communication connection between the mobile phone and the satellite based on the optimal particle vector.

[0011] According to another aspect of the present disclosure, a mobile phone and satellite direct communication optimization device is also provided, comprising: a processor; and a memory connected to the processor, configured to provide the processor with instructions for processing the following steps: defining a particle vector related to the mobile phone and satellite direct communication, wherein the elements of the particle vector correspond to the uplink transmission power and uplink carrier frequency of the mobile phone, respectively; initializing a particle swarm based on the particle vector; constructing a fitness function, wherein the fitness value of the fitness function is related to the transmission efficiency cost and the energy consumption cost, wherein the transmission efficiency cost is used to evaluate the loss of data transmission efficiency, and the energy consumption cost is used to reflect the energy consumption cost of the mobile phone; iteratively optimizing the particle swarm according to a particle swarm optimization algorithm to determine the optimal particle vector; and establishing a direct communication connection between the mobile phone and the satellite based on the optimal particle vector.

[0012] This application first defines a particle vector related to direct satellite communication, where each element corresponds to the phone's uplink transmit power and uplink carrier frequency. Then, a particle swarm is initialized based on this particle vector, generating a preset number of particles. Next, a fitness function is constructed, whose value is related to transmission efficiency cost and energy consumption cost. The transmission efficiency cost is used to evaluate the loss of data transmission efficiency, while the energy consumption cost reflects the phone's energy consumption cost. Then, the particle swarm is iteratively optimized using a particle swarm optimization algorithm. In each iteration, the fitness value of each particle is calculated, and the individual optimal position of each particle and the global optimal position of the particle swarm are updated. After the iteration stops, the particle vector corresponding to the current global optimal position is determined as the optimal particle vector. Finally, the phone parses the optimal uplink transmit power and optimal uplink carrier frequency from the optimal particle vector and sends an uplink signal to the satellite based on these optimal values, establishing a direct communication connection between the phone and the satellite.

[0013] Thus, this application realizes the joint dynamic optimization of mobile phone uplink transmission power and uplink carrier frequency, effectively reducing terminal power consumption while ensuring satellite-to-ground link transmission efficiency, thereby solving the technical problem in the prior art that transmission efficiency and terminal power consumption cannot be dynamically balanced in direct communication between mobile phones and satellites. Attached Figure Description

[0014] The accompanying drawings, which are included to provide a further understanding of this disclosure and form part of this application, illustrate exemplary embodiments of this disclosure and are used to explain this disclosure, but do not constitute an undue limitation of this disclosure. In the drawings: Figure 1 This is a hardware structure block diagram of a computing device for implementing the method described in Embodiment 1 of this disclosure; Figure 2 This is a schematic diagram of a system optimized for direct communication between a mobile phone and a satellite according to Embodiment 1 of this disclosure; Figure 3 This is a flowchart illustrating the method for optimizing direct communication between a mobile phone and a satellite according to Embodiment 1 of this disclosure; Figure 4 This is a schematic diagram of the iterative process of the particle swarm optimization algorithm according to Embodiment 1 of this disclosure; Figure 5 This is a schematic diagram of the mobile phone and satellite direct communication optimization device according to Embodiment 2 of this disclosure; Figure 6 This is a schematic diagram of the mobile phone and satellite direct communication optimization device according to Embodiment 3 of this disclosure. Detailed Implementation

[0015] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this disclosure.

[0016] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0017] Example 1

[0018] According to this embodiment, a method embodiment for optimizing direct communication between mobile phones and satellites is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0019] The method embodiments provided in this example can be executed on mobile terminals, computer terminals, servers, or similar computing devices. Figure 1A hardware block diagram of a computing device for implementing an optimized method for direct communication between mobile phones and satellites is shown. Figure 1 As shown, a computing device may include one or more processors (processors may include, but are not limited to, microprocessors such as MCUs or programmable logic devices such as FPGAs), a memory for storing data, a transmission device for communication functions, and an input / output interface. The memory, transmission device, and input / output interface are connected to the processor via a bus. In addition, it may also include a display, keyboard, and cursor control device connected to the input / output interface. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, a computing device may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0020] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element in a computing device. As involved in the embodiments of this disclosure, the data processing circuits serve as processor control (e.g., selection of a variable resistor termination path connected to an interface).

[0021] The memory can be used to store software programs and modules of application software, such as the program instruction / data storage device corresponding to the mobile phone and satellite direct communication optimization method in this embodiment of the present disclosure. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby implementing the aforementioned mobile phone and satellite direct communication optimization method for the application. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the computing device via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0022] The transmission device is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the computing device's communication provider. In one example, the transmission device includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device may be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0023] The display can be, for example, a touchscreen liquid crystal display (LCD), which allows users to interact with the user interface of the computing device.

[0024] It should be noted here that, in some optional embodiments, the above... Figure 1 The computing device shown may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that... Figure 1 This is only one instance of a specific particular instance, and is intended to illustrate the types of components that may exist in the aforementioned computing devices.

[0025] Figure 2 A schematic diagram of a system for optimizing direct communication between a mobile phone and a satellite according to this embodiment is shown. (Reference) Figure 2 As shown, the system includes a satellite 101 and a mobile phone 102. The satellite 101 is used to establish a direct communication link with the mobile phone 102 and receive uplink signals sent by the mobile phone 102. The mobile phone 102 is used to determine the optimal uplink transmission power and the optimal uplink carrier frequency according to the particle swarm optimization algorithm, and to send communication data to the satellite 101.

[0026] Under the aforementioned operating environment, according to the first aspect of this embodiment, an optimization method for direct communication between a mobile phone and a satellite is provided. This method comprises... Figure 2 The mobile phone 102 shown in the figure implements this. Figure 3 A flowchart illustrating the method is shown below. (Refer to...) Figure 3 As shown, the method includes: S302: Define the particle vector related to direct communication between the mobile phone and the satellite. The elements of the particle vector correspond to the uplink transmit power and uplink carrier frequency of the mobile phone, respectively. S304: Initialize the particle swarm based on the particle vector; S306: Construct a fitness function. The fitness value of the fitness function is related to the transmission efficiency cost and the energy consumption cost. The transmission efficiency cost is used to evaluate the loss of data transmission efficiency, and the energy consumption cost is used to reflect the energy consumption cost of the mobile phone. S308: Iteratively optimize the particle swarm using the particle swarm optimization algorithm to determine the optimal particle vector; S310: Establish a direct communication connection between the mobile phone and the satellite based on the optimal particle vector.

[0027] Specifically, in an embodiment of the present invention, the mobile phone 102 first defines a particle vector related to direct communication with the satellite 101, wherein the first dimension of each particle vector corresponds to the uplink transmission power of the mobile phone 102. The second element corresponds to the uplink carrier frequency f of mobile phone 102 (corresponding to step S302).

[0028] Specifically, based on the definition of particle vectors, the uplink transmit power to be optimized in mobile phone 102 and uplink carrier frequency This mapping represents the variable dimension in the particle swarm optimization algorithm. Subsequently, the mobile phone 102 generates multiple particle vectors X1~X based on this mapping relationship. n Each particle vector (k=1~n) represents a set of candidate uplink transmit powers and uplink carrier frequency .

[0029] Next, the mobile phone 102 generates multiple particle vectors X1~X... n The particle swarm is initialized (corresponding to step S304). Specifically, the mobile phone 102 first generates a preset number n particles, each particle corresponding to an independent particle vector; that is, the number of particles and the number of particle vectors are the same. Next, the mobile phone 102 obtains multiple particles distributed at different uplink transmission powers. and uplink carrier frequency The initial particle vectors in the combination form the particle swarm.

[0030] Furthermore, the mobile phone 102 constructs a fitness function related to the particle swarm optimization algorithm to evaluate the quality of particle vectors. The fitness value of this fitness function is related to the transmission efficiency cost. and energy consumption costs Related. Among them, transmission efficiency cost. Used to evaluate the current uplink transmit power of mobile phone 102. and uplink carrier frequency The degree of loss in data transmission efficiency and energy consumption. Used to reflect the current uplink transmission power of mobile phone 102 and uplink carrier frequency The energy consumption cost is calculated (corresponding to step S306).

[0031] Thus, through this fitness function, mobile phone 102 can reduce the transmission efficiency cost of the communication connection between satellite 101 and mobile phone 102. and energy consumption costs This is quantified into a comparable numerical value. It should be noted that a smaller fitness value indicates less transmission efficiency loss and lower energy consumption cost. Conversely, a larger fitness value indicates greater transmission efficiency loss and higher energy consumption cost.

[0032] Furthermore, mobile phone 102 utilizes the initialized particle vectors X1~X n and fitness function The optimal particle vector is determined through iterative calculations using a particle swarm optimization algorithm (corresponding to step S308). Specifically, in each iteration, mobile phone 102 first determines the optimal particle vector based on each particle vector X1~X... n Corresponding fitness value (X1) to (X n Update the individual optimal position of each particle, where the individual optimal position is the particle vector corresponding to the minimum fitness value obtained by the particle in the historical iteration.

[0033] Then, phone 102 updates the global optimal position of the particle swarm based on the individual optimal positions of all particles. The global optimal position is the particle vector corresponding to the minimum fitness value obtained by all particles in previous iterations. Next, phone 102 calculates the particle vector for each particle in the next iteration using the particle swarm optimization algorithm. Finally, after the iteration stops, phone 102 determines the particle vector corresponding to the current global optimal position as the optimal particle vector. The process of iteratively optimizing the particle swarm using the particle swarm optimization algorithm will be explained later.

[0034] Furthermore, based on the optimal particle vector, mobile phone 102 establishes a direct communication connection with satellite 101 (corresponding to step S310). Specifically, mobile phone 102 parses the optimal uplink transmission power corresponding to the first dimension element from the optimal particle vector. The optimal uplink carrier frequency fbest corresponds to the second-dimensional element. Subsequently, the mobile phone 102 sets its own uplink transmit power to the optimal uplink transmit power. best, set the uplink carrier frequency to the optimal uplink carrier frequency fbest, and send uplink signals to satellite 101 to complete the establishment of a direct communication link with satellite 101.

[0035] As described in the background section, in remote mountainous areas, oceans, deserts, and other regions where terrestrial communication networks cannot reach, mobile terminals need to connect directly to low-Earth orbit satellites to achieve emergency communication, location reporting, or data transmission. In these scenarios, mobile terminals have limited battery capacity and cannot be charged in a timely manner. Furthermore, the satellite-to-ground link suffers from free-space path loss of hundreds to thousands of kilometers, resulting in severe uplink signal attenuation. If the phone transmits at excessively high power, while reliable satellite reception can be ensured, it will rapidly deplete the battery and shorten battery life. Conversely, transmitting at excessively low power may lead to incorrect demodulation by the satellite, resulting in communication failure. Therefore, it is necessary to minimize terminal power consumption and extend device operating time while ensuring link transmission efficiency.

[0036] In view of this, this application first defines a particle vector related to direct satellite communication, where the elements of the particle vector correspond to the uplink transmit power and uplink carrier frequency of the mobile phone, respectively. Then, a particle swarm is initialized based on this particle vector, generating a preset number of particles. Next, a fitness function is constructed, the value of which is related to the transmission efficiency cost and energy consumption cost, where the transmission efficiency cost is used to evaluate the loss of data transmission efficiency, and the energy consumption cost reflects the energy consumption cost of the mobile phone. Then, the particle swarm is iteratively optimized using a particle swarm optimization algorithm, calculating the fitness value of each particle in each iteration and updating the individual optimal position of each particle and the global optimal position of the particle swarm. After the iteration stops, the particle vector corresponding to the current global optimal position is determined as the optimal particle vector. Finally, the mobile phone parses the optimal uplink transmit power and optimal uplink carrier frequency from the optimal particle vector and sends an uplink signal to the satellite based on the optimal uplink transmit power and optimal uplink carrier frequency, establishing a direct communication connection between the mobile phone and the satellite.

[0037] Thus, this application realizes the joint dynamic optimization of mobile phone uplink transmission power and uplink carrier frequency, effectively reducing terminal power consumption while ensuring satellite-to-ground link transmission efficiency, thereby solving the technical problem in the prior art that transmission efficiency and terminal power consumption cannot be dynamically balanced in direct communication between mobile phones and satellites.

[0038] Optionally, the operation of initializing the particle swarm according to the particle vector includes: each particle vector corresponds to a particle in the particle swarm; and according to a preset first constraint, assigning two elements of each particle vector to random numbers within their respective preset value ranges for uplink transmit power and uplink carrier frequency, wherein the first constraint specifies that the uplink transmit power is within a predetermined power range and the uplink carrier frequency is within a predetermined frequency range.

[0039] Specifically, the mobile phone 102 first assigns a particle vector to each particle in the particle swarm. Each particle vector This corresponds to one particle. Then, the phone 102 targets the vector of each particle. According to the preset first constraint condition, the particle vector is... The two elements in the formula are each assigned a random number. The first constraint specifies that the uplink transmit power... The random number is within the predetermined power range, that is: .in, Used to indicate the minimum transmit power supported by mobile phone 102. Used to indicate the maximum transmit power supported by mobile phone 102.

[0040] Meanwhile, the first constraint also stipulates that the random number of the uplink carrier frequency f is within a predetermined frequency range, that is: .in, Used to indicate the minimum carrier frequency supported by mobile phone 102. This indicates the maximum carrier frequency supported by mobile phone 102.

[0041] Thus, through the above methods, the vector of each particle is made Each can obtain a set of initial candidate values ​​for uplink transmit power and uplink carrier frequency, and the initial particle swarm is formed by all particle vectors.

[0042] Optionally, according to a preset second constraint, a particle vector related to the direct communication between the mobile phone and the satellite is defined, wherein the second constraint is: the signal loss of the mobile phone is determined according to the uplink carrier frequency of the mobile phone and the link distance between the mobile phone and the satellite; the satellite receiving power is determined according to the uplink transmission power of the mobile phone and the signal loss of the mobile phone; and the satellite receiving power must be greater than or equal to the satellite receiving sensitivity threshold.

[0043] Specifically, mobile phone 102 defines a particle vector related to direct communication with satellite 101 based on a preset second constraint. This second constraint is: (1) in, Indicates satellite receiving power. This indicates the uplink transmit power of mobile phone 102. Indicates the satellite receiver sensitivity threshold. This represents the total signal loss of mobile phone 102 in the satellite-to-ground link, f represents the uplink carrier frequency in MHz, and d represents the link distance between mobile phone 102 and satellite 101 in km. Furthermore, The expression is: (2) Thus, mobile phone 102 can ensure the uplink emission power corresponding to the defined particle vector through this second constraint condition. The uplink carrier frequency f can meet basic communication requirements.

[0044] Optionally, the operation of constructing the fitness function includes: constructing the fitness function based on the transmission efficiency cost, the weight of the transmission efficiency cost, the energy consumption cost, and the weight of the energy consumption cost.

[0045] Specifically, mobile phone 102 constructs a system to evaluate the transmission efficiency cost corresponding to the particle vector. and energy consumption costs Fitness function of the combination's quality : (3) in, For the sake of transmission efficiency, For the cost of energy consumption, The weights for the cost of transmission efficiency The weight of energy consumption cost.

[0046] Specifically, mobile phone 102 uses the fitness function shown in formula (3) to reduce the transmission efficiency cost. and energy consumption costs Weighted summation yields a comprehensive evaluation value. Then, phone 102 optimizes by minimizing the fitness value, where the weights... and Used to adjust the relative importance between transmission efficiency and energy consumption, and to meet + =1. That is, when mobile phone 102 needs to prioritize transmission efficiency, it can increase... The value of can be increased when the phone 102 needs to prioritize reducing power consumption to extend battery life. The value of .

[0047] In other words, the fitness function It can guide the particle swarm towards the cost of transmission efficiency. Energy consumption cost Evolution towards the optimal direction of collaboration.

[0048] Optionally, the transmission efficiency cost is determined based on the amount of data to be transmitted by the mobile phone and the uplink carrier frequency, while the energy consumption cost is determined based on the uplink transmission power and the current remaining battery power of the mobile phone.

[0049] Specifically, mobile phone 102 determines the cost of transmission efficiency. The method is as follows: Mobile phone 102 obtains the current amount of data to be transmitted, D, and obtains the current candidate uplink carrier frequency, f. Then, mobile phone 102 calculates the normalized transmission efficiency cost based on the correspondence between the current amount of data to be transmitted, D, and the uplink carrier frequency, f. .Right now: (4) (5) In the formula, The amount of data to be transmitted. The maximum available bandwidth corresponding to the uplink carrier frequency f. This is the bandwidth ratio factor, which can be preset according to actual conditions. NOR ( ) indicates normalized calculation.

[0050] The larger the amount of data to be transmitted, D, or the lower the uplink carrier frequency, the greater the transmission efficiency cost for transmitting the same amount of data. The corresponding increase.

[0051] Accordingly, the energy consumption cost of the mobile phone 102 is determined. The method is as follows: Mobile phone 102 obtains the uplink transmit power of the current candidate. And obtain the current remaining battery level of phone 102. Then, mobile phone 102 determines the current uplink transmission power based on mobile phone 102. With current remaining power The ratio is used to calculate the normalized energy cost. .Right now: (6) Among them, uplink transmit power The higher the level, or the current remaining battery power. The lower the value, the lower the energy consumption cost. Increase accordingly NOR ( ) indicates normalized calculation.

[0052] Thus, through the above methods, mobile phone 102 reduces the transmission efficiency cost. and energy consumption costs As input to the fitness function, it is used to evaluate the cost of candidate transmission efficiency. and energy consumption costs The advantages and disadvantages of the combination.

[0053] Optionally, the operation of iteratively optimizing the particle swarm according to the particle swarm optimization algorithm to determine the optimal particle vector includes: updating each particle vector and its corresponding velocity coefficient in each iteration; calculating the fitness value of the fitness function of each particle based on the updated particle vector; updating the individual optimal position of each particle and the global optimal position of the particle swarm; and stopping the iteration when the number of iterations reaches the preset maximum number of iterations or the fitness value of the global optimal position meets the preset convergence threshold, and taking the current global optimal position as the optimal particle vector.

[0054] Specifically, in each iteration, mobile phone 102 first processes the particle vector X of each particle in the particle swarm. k and the velocity coefficient V corresponding to each particle k The update is performed. Based on the updated particle vectors, phone 102 calculates the fitness value for each particle. (X1)~ (X) n The method for updating the individual particle vectors and corresponding velocity coefficients of the particle swarm will be explained in detail later.

[0055] Next, phone 102 displays the fitness value of each particle in the current iteration (the t-th iteration). Fitness value relative to the particle's historical best position Compare. If < Then, mobile phone 102 will display the current particle vector X of the particle. k t Updated to the individual best position Xbest for this particle. k t Otherwise, mobile phone 102 will maintain its original optimal position.

[0056] Finally, the mobile phone 102 determines the particle vector corresponding to the minimum fitness value among the individual optimal positions of all particles as the global optimal position of the particle swarm. In this process, the mobile phone 102 needs to determine whether the current iteration count has reached the preset maximum iteration count, or whether the fitness value of the current global optimal position meets a preset convergence threshold. When either of these two conditions is met, the mobile phone 102 stops iterating and uses the current global optimal position as the optimal particle vector. Conversely, when neither condition is met, the mobile phone 102 continues to execute the next iteration.

[0057] Thus, through the above methods, mobile phone 102 can complete iterative optimization in a short time and determine the optimal uplink transmission power. And the uplink carrier frequency f, thereby meeting the real-time requirements of direct satellite-to-ground communication.

[0058] Optionally, the individual particle vectors and corresponding velocity coefficients of the particle swarm are updated according to the following formula: V k t+1 =w·V k t +c1·r1·(Xbest k t -X k t )+c2·r2·(Xbest t -X k t ); X k t+1 =X k t +V k t+1 ; Where X k t and V k t Let X and Y represent the particle determined in the t-th iteration, respectively. k and velocity coefficient V k ;X k t+1 and V k t+1 Let X and Y represent the particle determined in the (t+1)th iteration, respectively. k and velocity coefficient V k Xbest k t For particle X k The optimal position of the individual determined in the t-th iteration; Xbest t t is the global optimal position determined by the particle swarm in the t-th iteration; w is the inertia factor, which takes a non-negative value; c1 and c2 are learning factors, used to adjust the convergence speed of the particle swarm optimization algorithm; and r1 and r2 are random numbers between 0 and 1.

[0059] Specifically, the mobile phone 102 can update the individual particle vectors and corresponding velocity coefficients of the particle swarm according to the following formula: V k t+1 =w·V k t +c1·r1·(Xbest k t -X k t )+c2·r2·(Xbest t -X k t (7) Xk t+1 =X k t +V k t+1 (8) Specifically, Figure 4 This is a schematic diagram of the iterative process of the particle swarm optimization algorithm, for reference. Figure 4 As shown: First, mobile phone 102, based on randomly generated particle pairs Xbest k Xbest, V k Perform initialization (corresponding to step S402). Next, mobile phone 102 updates each particle X using formulas (7) and (8). k and velocity coefficient V k (Corresponding to step S404). Then, the updated particles are filtered according to the constraints (i.e., the first constraint and the second constraint) (corresponding to step S406). Afterwards, the mobile phone 102, based on the filtered particles, matches each particle X... k The corresponding optimal fitness value Jbest k And the optimal fitness value Jbest, determined among all particles, is updated. Further, the phone 102 is updated based on the updated Jbest... k And Jbest, update the individual's optimal position Xbest k The global optimal position Xbest is obtained (corresponding to step S408). Then, mobile phone 102 determines whether the current iteration meets the iteration termination condition (corresponding to step S410). If it does not meet the condition (N), it returns to step S404 to continue iterating. If it meets the condition (Y), it executes step S412 and outputs Xbest. The entire process involves iterative optimization until the global optimal position is obtained.

[0060] Thus, through the coordinated updating of velocity coefficient and particle vector, each particle can gradually approach the global optimal position under the joint guidance of its own historical optimal position and the historical optimal position of the group, thereby quickly converging to the optimal uplink transmit power and optimal uplink carrier frequency combination that satisfies the balance between transmission efficiency and energy consumption within a finite number of iterations.

[0061] In addition, refer to Figure 1 As shown, according to a second aspect of this embodiment, a storage medium is provided. The storage medium includes a stored program, wherein, when the program is executed, a processor performs any of the methods described above.

[0062] Therefore, according to this embodiment, this application realizes the joint dynamic optimization of mobile phone uplink transmission power and uplink carrier frequency, effectively reducing terminal power consumption while ensuring satellite-to-ground link transmission efficiency, thereby solving the technical problem in the prior art that transmission efficiency and terminal power consumption cannot be dynamically balanced in direct communication between mobile phones and satellites.

[0063] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0064] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0065] Example 2

[0066] Figure 5 A mobile phone and satellite direct communication optimization device according to this embodiment is shown, which corresponds to the method described according to Embodiment 1. (Reference) Figure 5 As shown, the device includes: a particle vector definition module 510, used to define particle vectors related to direct communication between the mobile phone and the satellite, wherein the elements of the particle vectors correspond to the uplink transmission power and uplink carrier frequency of the mobile phone, respectively; a particle swarm initialization module 520, used to initialize the particle swarm based on the particle vectors; a fitness function construction module 530, used to construct a fitness function, wherein the fitness value of the fitness function is related to the transmission efficiency cost and the energy consumption cost, wherein the transmission efficiency cost is used to evaluate the loss of data transmission efficiency, and the energy consumption cost is used to reflect the energy consumption cost of the mobile phone; an iterative optimization module 540, used to iteratively optimize the particle swarm according to the particle swarm optimization algorithm to determine the optimal particle vector; and a communication connection module 550, used to establish a direct communication connection between the mobile phone and the satellite based on the optimal particle vector.

[0067] Optionally, the operation of initializing the particle swarm according to the particle vector includes: each particle vector corresponds to a particle in the particle swarm; and according to a preset first constraint, assigning two elements of each particle vector to random numbers within their respective preset value ranges for uplink transmit power and uplink carrier frequency, wherein the first constraint specifies that the uplink transmit power is within a predetermined power range and the uplink carrier frequency is within a predetermined frequency range.

[0068] Optionally, according to a preset second constraint, a particle vector related to the direct communication between the mobile phone and the satellite is defined, wherein the second constraint is: the signal loss of the mobile phone is determined according to the uplink carrier frequency of the mobile phone and the link distance between the mobile phone and the satellite; the satellite receiving power is determined according to the uplink transmission power of the mobile phone and the signal loss of the mobile phone; and the satellite receiving power must be greater than or equal to the satellite receiving sensitivity threshold.

[0069] Optionally, the operation of constructing the fitness function includes: constructing the fitness function based on the transmission efficiency cost, the weight of the transmission efficiency cost, the energy consumption cost, and the weight of the energy consumption cost.

[0070] Optionally, the transmission efficiency cost is determined based on the amount of data to be transmitted by the mobile phone and the uplink carrier frequency, while the energy consumption cost is determined based on the uplink transmission power and the current remaining battery power of the mobile phone.

[0071] Optionally, the operation of iteratively optimizing the particle swarm according to the particle swarm optimization algorithm to determine the optimal particle vector includes: updating each particle vector and its corresponding velocity coefficient in each iteration; calculating the fitness value of the fitness function of each particle based on the updated particle vector; updating the individual optimal position of each particle and the global optimal position of the particle swarm; and stopping the iteration when the number of iterations reaches the preset maximum number of iterations or the fitness value of the global optimal position meets the preset convergence threshold, and taking the current global optimal position as the optimal particle vector.

[0072] Optionally, the individual particle vectors and corresponding velocity coefficients of the particle swarm are updated according to the following formula: V k t+1 =w·V k t +c1·r1·(Xbest k t -X k t )+c2·r2·(Xbest t -X k t ); X k t+1 =X kt +V k t+1 ; Where X k t and V k t Let X and Y represent the particle determined in the t-th iteration, respectively. k and velocity coefficient V k ;X k t+1 and V k t+1 Let X and Y represent the particle determined in the (t+1)th iteration, respectively. k and velocity coefficient V k Xbest k t For particle X k The optimal position of the individual determined in the t-th iteration; Xbest t t is the global optimal position determined by the particle swarm in the t-th iteration; w is the inertia factor, which takes a non-negative value; c1 and c2 are learning factors, used to adjust the convergence speed of the particle swarm optimization algorithm; and r1 and r2 are random numbers between 0 and 1.

[0073] Therefore, according to this embodiment, this application realizes the joint dynamic optimization of mobile phone uplink transmission power and uplink carrier frequency, effectively reducing terminal power consumption while ensuring satellite-to-ground link transmission efficiency, thereby solving the technical problem in the prior art that transmission efficiency and terminal power consumption cannot be dynamically balanced in direct communication between mobile phones and satellites.

[0074] Example 3

[0075] Figure 6 A mobile phone and satellite direct communication optimization device according to this embodiment is shown, which corresponds to the method described according to Embodiment 1. (Reference) Figure 6 As shown, the device includes: a processor 610; and a memory 620 connected to the processor 610, for providing the processor 610 with instructions to perform the following processing steps: defining a particle vector related to direct communication between the mobile phone and the satellite, wherein the elements of the particle vector correspond to the uplink transmission power and uplink carrier frequency of the mobile phone, respectively; initializing a particle swarm based on the particle vector; constructing a fitness function, wherein the fitness value of the fitness function is related to the transmission efficiency cost and the energy consumption cost, wherein the transmission efficiency cost is used to evaluate the loss of data transmission efficiency, and the energy consumption cost is used to reflect the energy consumption cost of the mobile phone; iteratively optimizing the particle swarm according to the particle swarm optimization algorithm to determine the optimal particle vector; and establishing a direct communication connection between the mobile phone and the satellite based on the optimal particle vector.

[0076] Optionally, the operation of initializing the particle swarm according to the particle vector includes: each particle vector corresponds to a particle in the particle swarm; and according to a preset first constraint, assigning two elements of each particle vector to random numbers within their respective preset value ranges for uplink transmit power and uplink carrier frequency, wherein the first constraint specifies that the uplink transmit power is within a predetermined power range and the uplink carrier frequency is within a predetermined frequency range.

[0077] Optionally, according to a preset second constraint, a particle vector related to the direct communication between the mobile phone and the satellite is defined, wherein the second constraint is: the signal loss of the mobile phone is determined according to the uplink carrier frequency of the mobile phone and the link distance between the mobile phone and the satellite; the satellite receiving power is determined according to the uplink transmission power of the mobile phone and the signal loss of the mobile phone; and the satellite receiving power must be greater than or equal to the satellite receiving sensitivity threshold.

[0078] Optionally, the operation of constructing the fitness function includes: constructing the fitness function based on the transmission efficiency cost, the weight of the transmission efficiency cost, the energy consumption cost, and the weight of the energy consumption cost.

[0079] Optionally, the transmission efficiency cost is determined based on the amount of data to be transmitted by the mobile phone and the uplink carrier frequency, while the energy consumption cost is determined based on the uplink transmission power and the current remaining battery power of the mobile phone.

[0080] Optionally, the operation of iteratively optimizing the particle swarm according to the particle swarm optimization algorithm to determine the optimal particle vector includes: updating each particle vector and its corresponding velocity coefficient in each iteration; calculating the fitness value of the fitness function of each particle based on the updated particle vector; updating the individual optimal position of each particle and the global optimal position of the particle swarm; and stopping the iteration when the number of iterations reaches the preset maximum number of iterations or the fitness value of the global optimal position meets the preset convergence threshold, and taking the current global optimal position as the optimal particle vector.

[0081] Optionally, the individual particle vectors and corresponding velocity coefficients of the particle swarm are updated according to the following formula: V k t+1 =w·V k t +c1·r1·(Xbest k t -X k t )+c2·r2·(Xbest t -X k t ); X k t+1 =X kt +V k t+1 ; Where X k t and V k t Let X and Y represent the particle determined in the t-th iteration, respectively. k and velocity coefficient V k ;X k t+1 and V k t+1 Let X and Y represent the particle determined in the (t+1)th iteration, respectively. k and velocity coefficient V k Xbest k t For particle X k The optimal position of the individual determined in the t-th iteration; Xbest t t is the global optimal position determined by the particle swarm in the t-th iteration; w is the inertia factor, which takes a non-negative value; c1 and c2 are learning factors, used to adjust the convergence speed of the particle swarm optimization algorithm; and r1 and r2 are random numbers between 0 and 1.

[0082] Therefore, according to this embodiment, this application realizes the joint dynamic optimization of mobile phone uplink transmission power and uplink carrier frequency, effectively reducing terminal power consumption while ensuring satellite-to-ground link transmission efficiency, thereby solving the technical problem in the prior art that transmission efficiency and terminal power consumption cannot be dynamically balanced in direct communication between mobile phones and satellites.

[0083] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0084] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0085] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0086] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0087] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0088] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0089] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing direct communication between mobile phones and satellites, characterized in that, include: Define a particle vector related to direct communication between a mobile phone and a satellite, wherein the elements of the particle vector correspond to the uplink transmit power and uplink carrier frequency of the mobile phone, respectively; Initialize the particle swarm based on the particle vector; A fitness function is constructed, wherein the fitness value of the fitness function is related to the transmission efficiency cost and the energy consumption cost, wherein the transmission efficiency cost is used to evaluate the loss of data transmission efficiency, and the energy consumption cost is used to reflect the energy consumption cost of the mobile phone; The particle swarm is iteratively optimized using a particle swarm optimization algorithm to determine the optimal particle vector; and Based on the optimal particle vector, a direct communication connection is established between the mobile phone and the satellite.

2. The method according to claim 1, characterized in that, The operation of initializing the particle swarm based on the particle vector includes: Each particle vector corresponds to a particle in the particle swarm; and According to the preset first constraint, the two elements of each particle vector are respectively assigned random numbers within their preset ranges for the uplink transmit power and the uplink carrier frequency. The first constraint condition stipulates that the uplink transmit power is within a predetermined power range and the uplink carrier frequency is within a predetermined frequency range.

3. The method according to claim 1, characterized in that, Based on the preset second constraint, a particle vector related to direct communication between the mobile phone and the satellite is defined, wherein the second constraint is: The signal loss of the mobile phone is determined based on the uplink carrier frequency of the mobile phone and the link distance between the mobile phone and the satellite; The satellite receiving power is determined based on the uplink transmission power of the mobile phone and the signal loss emitted by the mobile phone; and The satellite receiving power must be greater than or equal to the satellite receiving sensitivity threshold.

4. The method according to claim 1, characterized in that, The operations for constructing the fitness function include: The fitness function is constructed based on the transmission efficiency cost, the weight of the transmission efficiency cost, the energy consumption cost, and the weight of the energy consumption cost.

5. The method according to claim 4, characterized in that, The transmission efficiency cost is determined based on the amount of data to be transmitted by the mobile phone and the uplink carrier frequency, while the energy consumption cost is determined based on the uplink transmission power and the current remaining battery power of the mobile phone.

6. The method according to claim 1, characterized in that, The operation of iteratively optimizing the particle swarm according to the particle swarm optimization algorithm to determine the optimal particle vector includes: In each iteration, the particle vectors and corresponding velocity coefficients of the particle swarm are updated. Calculate the fitness value of each particle's fitness function based on the updated particle vector; Update the individual optimal position of each particle and the global optimal position of the particle swarm; and When the number of iterations reaches the preset maximum number of iterations or the fitness value of the global optimal position meets the preset convergence threshold, the iteration stops, and the current global optimal position is taken as the optimal particle vector.

7. The method according to claim 6, characterized in that, The particle vectors and corresponding velocity coefficients of the particle swarm are updated according to the following formula: V k t+1 =w V k t +c1 r1 (Xbest k t -X k t )+c2·r2·(Xbest t -X k t ); X k t+1 =X k t +V k t+1 ; Where X k t and V k t Let X and Y represent the particle determined in the t-th iteration, respectively. k and velocity coefficient V k ;X k t+1 and V k t+1 Let X and Y represent the particle determined in the (t+1)th iteration, respectively. k and velocity coefficient V k Xbest k t For particle X k The optimal position of the individual determined in the t-th iteration; Xbest t is the global optimal position determined by the particle swarm in the t-th iteration; w is the inertia factor, which takes a non-negative value; c1 and c2 are learning factors, used to adjust the convergence speed of the particle swarm optimization algorithm; and r1 and r2 are random numbers between 0 and 1.

8. A storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, the method described in any one of claims 1 to 7 is performed by a processor.

9. A device for optimizing direct communication between mobile phones and satellites, characterized in that, include: The particle vector definition module is used to define particle vectors related to direct communication between mobile phones and satellites. The elements of the particle vectors correspond to the uplink transmission power and uplink carrier frequency of the mobile phone, respectively. The particle swarm initialization module is used to initialize the particle swarm based on the particle vector. The fitness function construction module is used to construct a fitness function. The fitness value of the fitness function is related to the transmission efficiency cost and the energy consumption cost. The transmission efficiency cost is used to evaluate the loss of data transmission efficiency, and the energy consumption cost is used to reflect the energy consumption cost of the mobile phone. The iterative optimization module is used to iteratively optimize the particle swarm according to the particle swarm optimization algorithm to determine the optimal particle vector; and The communication connection module is used to establish a direct communication connection between the mobile phone and the satellite based on the optimal particle vector.

10. A device for optimizing direct communication between mobile phones and satellites, characterized in that, include: processor; as well as A memory, connected to the processor, for providing the processor with instructions to perform the following processing steps: Define a particle vector related to direct communication between a mobile phone and a satellite, wherein the elements of the particle vector correspond to the uplink transmit power and uplink carrier frequency of the mobile phone, respectively; Initialize the particle swarm based on the particle vector; A fitness function is constructed, wherein the fitness value of the fitness function is related to the transmission efficiency cost and the energy consumption cost, wherein the transmission efficiency cost is used to evaluate the loss of data transmission efficiency, and the energy consumption cost is used to reflect the energy consumption cost of the mobile phone; The particle swarm is iteratively optimized using a particle swarm optimization algorithm to determine the optimal particle vector; and Based on the optimal particle vector, a direct communication connection is established between the mobile phone and the satellite.

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