Relay Communication Device Deployment Location Determination Method and System

Abstract

Embodiments of the present disclosure provide a method and system for determining the deployment location of relay communication devices. The method for determining the deployment location of relay communication devices includes: determining the terminal locations, the number of devices for which relay communication devices are planned to be deployed, the candidate deployment locations, and the associated candidate locations corresponding to each terminal location; constructing a full-coverage objective function with the goal of maximizing the number of terminal locations covered after the actual deployment of the relay communication devices, and constructing a quantity constraint function based on the number of devices and the selection indicators of each candidate deployment location; integrating the full-coverage objective function and the quantity constraint function to obtain a global optimization function; using a quantum computer to perform optimization calculations on the global optimization function to obtain a calculation result, and determining whether to deploy relay communication devices at each candidate deployment location based on the calculation result. The solution of the embodiments of the present disclosure can utilize the advantage of the fast calculation speed of the quantum computer and the advantage that the physical machine can avoid local optimal results to quickly determine the deployment location of the relay communication devices.

Description

Technical Field

[0001] The present disclosure relates to the field of overall optimization technology, and particularly to a method and system for determining the deployment location of relay communication devices. Background Art

[0002] In the case of communication interruption caused by the failure of base station functions due to sudden natural disasters or other reasons, it is necessary to deploy emergency communication and achieve full-range coverage of emergency communication as much as possible. To achieve the foregoing objectives, it is necessary to reasonably allocate the deployment locations of relay communication devices for emergency communication and achieve optimized networking as much as possible.

[0003] In the related art, a method for determining the deployment location of relay communication devices using artificial intelligence and conventional optimization algorithms is proposed; however, the foregoing method can only be deployed for use in a classical computer, and is limited by the computing power of the classical computer. After considering factors and the number of relay communication devices is large, the computational complexity rapidly expands and the calculation result cannot be obtained quickly, and only a local optimal result may be obtained due to insufficient computing power and algorithm limitations, and the global optimal result is not obtained. Summary of the Invention

[0004] To solve the problem that the calculation of the deployment location of existing relay devices is time-consuming and the calculation result cannot be obtained in time, an embodiment of the present disclosure provides a new method and system for determining the deployment location of relay communication devices.

[0005] In a first aspect, an embodiment of the present disclosure provides a method for determining the deployment location of relay communication devices, including:

[0006] Determine multiple terminal locations to be covered, the number of relay communication devices planned to be deployed and candidate deployment locations, and based on the communication distance threshold of the relay communication devices, the terminal locations, and the candidate deployment locations, determine the associated candidate locations corresponding to each terminal location, where the associated candidate location is a candidate deployment location where the corresponding relay communication device covers the corresponding terminal location after the relay communication device is deployed;

[0007] Based on the selection indicators of multiple terminal locations and the corresponding associated candidate locations, construct a full-coverage objective function with the goal of covering the most terminal locations after the relay communication devices are actually deployed, where the selection indicator is used to indicate whether a relay communication device is deployed at the corresponding associated candidate location;

[0008] Based on the number of devices and the selection indicators of each candidate deployment location, construct a quantity constraint function, where the quantity constraint function is a function used to constrain the number of selected candidate deployment locations to be ;

[0009] Integrate the full-coverage objective function and the quantity constraint function to obtain a global optimization function; the global optimization function is a quadratic unconstrained binary function or a function equivalent in form to a quadratic unconstrained binary function;

[0010] Use a quantum computer to perform optimization calculations on the global optimization function to obtain a calculation result, and determine whether to deploy relay communication devices at each candidate deployment location based on the calculation result.

[0011] Optionally, the method further includes: determining a coverage status identifier corresponding to the terminal location based on the selection identifiers of the associated candidate locations corresponding to each terminal location; the coverage status identifier is used to determine whether the corresponding terminal location is covered by a relay communication device;

[0012] Based on the coverage status identifier of the terminal location and the selection identifier of the corresponding associated candidate location, construct a coverage overlap minimization constraint function with the goal of minimizing the number of relay communication devices covering each terminal location;

[0013] The integrating the full-coverage objective function and the quantity constraint function to obtain a global optimization function includes: integrating the full-coverage objective function, the quantity constraint function, and the coverage overlap minimization constraint function to obtain a global optimization function.

[0014] Optionally, the constructing a coverage overlap minimization constraint function based on the coverage status identifier of the terminal location and the selection identifier of the corresponding associated candidate location with the goal of minimizing the number of relay communication devices covering each terminal location includes:

[0015] Based on the coverage status identifier of the terminal location, the selection identifier of the corresponding associated candidate location, and a set relaxation coefficient, construct a coverage overlap minimization constraint function with the goal of minimizing the number of relay communication devices covering each terminal location.

[0016] Optionally, the method further includes: determining a pairwise direct connection identifier based on the communication distance between each pair of candidate deployment locations and the communication distance threshold of the relay communication device, where the pairwise direct connection identifier is used to identify whether the relay communication devices can be directly communicatively connected when relay communication devices are deployed at both of the two candidate deployment locations;

[0017] Based on the selection identifiers of each candidate deployment location, the pairwise direct connection identifier, and the number of devices construct a full-connection constraint function;

[0018] The integrating the full-coverage objective function and the quantity constraint function to obtain a global optimization function includes: integrating the full-coverage objective function, the quantity constraint function, and the full-connection constraint function to obtain a global optimization function.

[0019] Optionally, based on the selection identifiers, pairwise direct connection identifiers, and the number of devices for each candidate deployment location Construct a fully connected constraint function, including:

[0020] With a total of packets being injected into each candidate deployment location, at the candidate deployment location where a relay communication device is selected for deployment, the number of incoming packets is 1 more than the number of outgoing packets, and the maximum number of unidirectionally flowing packets between candidate deployment locations that can communicate directly after any two relay communication devices are deployed is packets as a prerequisite, construct a fully connected constraint function according to the selection identifiers, pairwise direct connection identifiers, and the number of devices for each candidate deployment location Construct a fully connected constraint function.

[0021] Optionally, constructing the fully connected constraint function according to the selection identifiers, pairwise direct connection identifiers, and the number of devices for each candidate deployment location includes:

[0022] With a total of packets being injected into each candidate deployment location, construct a first connectivity constraint function;

[0023] With the number of incoming packets being 1 more than the number of outgoing packets at the candidate deployment location where a communication device is selected for deployment, construct a second connectivity constraint function according to the pairwise direct connection identifiers, the selection identifiers for each candidate deployment location, the number of randomly incoming packets, and the number of randomly outgoing packets at each candidate deployment location;

[0024] With the maximum number of unidirectionally flowing packets being packets as a constraint, perform binary encoding on the number of incoming and outgoing packets at each candidate deployment location respectively, and construct a third connectivity constraint function based on the binary data at each location and the selection identifiers for each candidate deployment location;

[0025] Perform weighted summation on the first fully connected constraint function, the second fully connected constraint function, and the third fully connected constraint function to obtain the fully connected constraint function.

[0026] Optionally, the first connectivity constraint function is ;

[0027] The second connectivity constraint function is ;

[0028] The third connectivity constraint function is ;

[0029] wherein, is the packet injected into the The number of data packets at each candidate deployment location, is the number of data packets flowing from the th candidate deployment location to the th candidate deployment location, is the number of data packets flowing from the th candidate deployment location to the th candidate deployment location, is the pairwise direct connection flag between the th candidate deployment location and the th candidate deployment location, is the selection flag for the th candidate deployment location, and is the binary data at each position after binary encoding, and is the binary data at each position after binary encoding.

[0030] Optionally, integrating the full-coverage objective function and the quantity constraint function to obtain a global optimization function includes: performing weighted summation on the full-coverage objective function and the quantity constraint function according to a pre-determined weighting weight to obtain the global optimization function.

[0031] In a second aspect, an apparatus for determining a deployment location of a relay communication device provided by an embodiment of the present disclosure is characterized by including:

[0032] A constraint determination unit, configured to determine a plurality of terminal locations to be covered, the number of relay communication devices planned to be deployed and candidate deployment locations, and based on the communication distance threshold of the relay communication device, the terminal location and the candidate deployment location, determine the associated candidate locations corresponding to each terminal location, where the associated candidate location is a candidate deployment location at which the corresponding relay communication device covers the corresponding terminal location after the relay communication device is deployed;

[0033] A function construction unit, configured to construct a full-coverage objective function based on the selection flags of a plurality of terminal locations and the corresponding associated candidate locations, with the goal of covering the most terminal locations after the relay communication device is actually deployed, where the selection flag is used to indicate whether the corresponding associated candidate location deploys a relay communication device; based on the number of devices and the selection flags of each candidate deployment location, construct a quantity constraint function; and integrate the full-coverage objective function and the quantity constraint function to obtain a global optimization function; where the quantity constraint function is used to constrain the number of selected candidate deployment locations to be The function, where the global optimization function is a quadratic unconstrained binary function or a function equivalent to the quadratic unconstrained binary function;

[0034] A quantum computing loading unit for loading the solution result obtained by optimizing and calculating the global optimization function using a quantum computer;

[0035] A deployment location determination unit for determining the deployment locations of each relay communication device based on the solution result.

[0036] In a third aspect, an embodiment of the present disclosure provides a system for determining the deployment location of relay communication devices, including a first computer based on a gate circuit and a second computer based on the principle of quantum computing; the first computer includes a memory and a processor, and when the computer program stored in the memory is loaded by the processor, the processor executes the method for determining the deployment location of relay communication devices as described above to obtain an integrated objective function, and calls the second computer to calculate the integrated objective function to obtain a solution result, and determines whether to deploy relay communication devices at each candidate deployment location based on the solution result.

[0037] By adopting the solution of the embodiment of the present disclosure, after constructing a global optimization function based on known parameters (including the positions of terminals to be covered, the number of relay communication devices, and candidate deployment locations), a quantum computer can be used to calculate the global optimization problem, and after determining the solution result, the optimal deployment location of the relay communication device can be determined based on the solution result. By adopting the solution of the embodiment of the present disclosure, the advantage of the calculation speed of the quantum computer can be utilized to quickly determine the deployment location of the relay communication device. Description of the Drawings

[0038] The drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure.

[0039] To more clearly illustrate the technical solutions in the embodiments of the present disclosure or in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts, where:

[0040] Figure 1 is a flowchart of the method for determining the deployment location of relay communication devices provided by the embodiment of the present disclosure;

[0041] Figure 2 is a schematic structural diagram of the device for determining the deployment location of relay communication devices provided by the embodiment of the present disclosure;

[0042] Figure 3It is a structural diagram of a system for determining a deployment location of relay communication equipment provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0043] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0044] The term "including" and its variations used in this document are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions of other terms will be given in the description below. In this document, relational terms such as "first" and "second" are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0045] In order to solve the problem that the method for determining the deployment location of relay communication equipment is slow to calculate or cannot obtain a global optimization result due to the limitations of existing methods and classical computer computing power, an embodiment of the present disclosure provides a new method for determining the deployment location of relay communication equipment.

[0046] Figure 1 1 is a flow chart of a method for deploying a relay communication device according to an embodiment of the present disclosure. Figure 1 As shown, the relay communication device deployment location method provided by the embodiment of the present disclosure includes S110-S160.

[0047] S110: Determine multiple terminal locations to be covered, the number of relay communication devices planned to be deployed, and candidate deployment locations.

[0048] The terminal location to be covered is where user terminals may be deployed ( UserEnd, UE The terminal location to be covered may have a user terminal deployed therein, or may not have a user terminal deployed therein, which is not limited in the embodiments of the present disclosure.

[0049] In some applications, in order to meet the communication needs of existing user terminals within the deployment range as much as possible, the location of the terminal to be covered can be determined according to the location of the user terminal.

[0050] To facilitate the use of formulas in the following text, the terminal position is It is represented that the set of terminal positions adopts to represent.

[0051] A relay communication device is a communication device that directly communicates with a user terminal (in some cases, it may also be through other relay communication devices or other types of communication devices) to indirectly connect the user terminal with other user terminals or other communication devices. In practical applications, the relay communication device can be a base station, a repeater, etc.

[0052] In some embodiments, the relay communication device can be connected to the user terminal by using a first communication method, but communicates with other devices by using a second communication method, thereby realizing the interconnection between the user terminal and other communication devices. For example, the relay station communication device can adopt GSM , CDMA and other communication methods to connect to the user terminal, and uses communication methods such as satellite communication and optical fiber communication to connect to other communication devices.

[0053] In some other embodiments, the relay communication device communicates with the user terminal and between relay communication devices by using the same communication method (for example, by using GSM , CDMA communication method for communication). At this time, the relay communication device can form a fully connected network to realize the mutual communication of the covered user terminals.

[0054] In some applications, the relay communication device is a communication device that can move and be deployed quickly. For example, the relay communication device can be carried by vehicles, drones, ships and other types of vehicles to realize quick movement and quick deployment.

[0055] It should be noted here that the number of relay communication devices is pre-determined and will not be changed during the implementation process of the method in this embodiment.

[0056] The candidate deployment location is pre-determined and is a location where a relay communication device can be deployed. For the convenience of expressing with formulas later, the candidate deployment location is represented by ( ), and the set of candidate location deployment locations composed of candidate deployment locations is represented by to represent.

[0057] In the embodiments of the present disclosure, it is assumed that the relay communication device has sufficient communication bandwidth to support the user terminals in its coverage area. Correspondingly, only one relay communication device needs to be deployed at a candidate deployment location. According to the foregoing assumption, it can be determined that the number of candidate deployment locations is at least the number of relay communication devices . In practical applications, each relay communication device has a communication coverage ability in a large area, and the number of corresponding candidate deployment locations is much larger than the number of communication devices. .

[0058] S120: Based on the communication distance threshold of the relay communication device, the terminal location, and the candidate deployment location, determine the associated candidate locations corresponding to each terminal location.

[0059] The communication distance threshold of the relay communication device is the distance from the farthest terminal device (or other communication device) that can be covered while ensuring communication quality to the relay communication device during communication. The communication distance threshold of the relay communication device is determined in advance according to parameters such as the model and transceiver power of the relay communication device.

[0060] The associated candidate locations corresponding to each terminal location are the candidate deployment locations that can achieve communication coverage at the terminal location after deploying the relay communication device. Assuming that the coverage area of the relay communication device is a planar area, a circle can be drawn with the terminal location as the center and the communication distance threshold of the relay communication device as the radius. All candidate deployment locations within the aforementioned circle are associated candidate locations.

[0061] According to the aforementioned symbol definitions, in the example of the present disclosure, the associated candidate locations corresponding to the terminal location are represented by . Based on the foregoing analysis, it can be determined that is a subset of the candidate deployment locations .

[0062] S130: Based on multiple terminal locations and the selection identifiers of the corresponding associated candidate locations, construct a full-coverage objective function with the goal of maximizing the number of terminal locations covered after the actual deployment of the relay communication device.

[0063] The selection identifier is used to identify whether an intermediate communication device is deployed at the corresponding associated candidate location, and the selection identifier is represented by . For example, in a specific application, the selection identifier is 1 or 0; the selection identifier of a certain associated candidate location is 0, indicating that this candidate coverage location is (hypothetically) selected for deploying the relay communication device; the selection identifier of a certain associated candidate location is 1, indicating that this candidate coverage location is not selected and no relay communication device will be deployed at this candidate coverage location.

[0064] According to the foregoing analysis, when determining the selection identifier of a certain associated candidate location After that, that is, after determining whether to deploy a relay communication device at a certain associated candidate location, it is possible to determine whether the terminal location corresponding to this associated candidate location is covered.

[0065] Specifically, if the selection flag at a certain associated candidate location is set to 1, each terminal location associated with this associated candidate location can be covered by the relay communication device, and correspondingly, the coverage status flag of this terminal location is 1. When the selection flags of multiple candidate deployment locations associated with a certain terminal location are all set to 1, the coverage status flag of this terminal location is also 1. If the selection flags of all candidate deployment locations associated with a certain terminal location are all set to 0, the coverage status flag of this terminal location is 0. After determining the coverage status flags corresponding to each terminal location, the communication coverage of all terminal locations is determined.

[0066] On the premise that the coverage status flag being 1 indicates that the terminal location is covered by communication, for the foregoing purpose of maximizing the number of terminal locations covered after the actual deployment of the relay communication device, it can be to add up the coverage status flags of all terminal locations and take the maximum sum as the goal. That is to say, the foregoing goal can be represented by representation.

[0067] After determining the foregoing goal of maximizing the number of terminal devices covered after the actual deployment of the relay communication device, accordingly, a full-coverage objective function can be constructed. In some embodiments of the present disclosure, the full-coverage objective function is represented by representation. Based on the foregoing formula, it can be determined that is a function with the minimum value. Specifically, why is set as a function with the minimum value will be specifically analyzed later.

[0068] S140: Construct a quantity constraint function based on the number of devices and the selection flags of each candidate deployment location.

[0069] As analyzed above, the number of relay communication devices planned to be deployed to cover the communication of terminal locations is , and the foregoing relay communication devices must be used, one more or one less is not allowed. Correspondingly, only candidate deployment locations among all candidate deployment locations are selected, that is, there are and only selection flags of candidate deployment locations should be set to 1.

[0070] According to the foregoing conditional constraints, the quantity constraint function constructed in the embodiments of the present disclosure is Since is either 0 or 1, it can be determined that the aforementioned quantity constraint function is a quadratic unconstrained binary function. Of course, in practical applications, the aforementioned quantity constraint function is not limited to the form of a quadratic unconstrained binary function, and a function equivalent to the form of a quadratic unconstrained binary function can also be adopted. According to the analysis in the previous paragraph and the form of the quantity constraint function, the quantity constraint function is a function that can obtain the minimum value under ideal conditions.

[0071] S150: Integrate the full-coverage objective function and the quantity constraint function to obtain a global optimization function.

[0072] After obtaining the aforementioned full-coverage objective function and quantity constraint function, based on the aforementioned full-coverage objective function and quantity constraint function, a global optimization function for characterizing the conditional characteristics after the deployment of relay communication devices can be obtained.

[0073] In some embodiments of the present disclosure, integrating the full-coverage objective function and the quantity constraint function may be to perform weighted summation on the full-coverage objective function and the quantity constraint function to obtain a global optimization function. According to the full-coverage objective function and the quantity constraint function determined in the previous paragraph, the global optimization function where and are corresponding weight coefficients, and are both greater than 0.

[0074] It can be conceived that since the quantity constraint function is a quadratic unconstrained binary function, in can only be 0 or 1, so the global optimization function is also a quadratic unconstrained binary function.

[0075] Of course, if the quantity constraint function is a function equivalent to the form of a quadratic unconstrained binary function, then the global optimization function is also a function equivalent to the form of a quadratic unconstrained binary function. What is meant by being equivalent to the form of a binary unconstrained binary function here is to convert a non-quadratic unconstrained binary function into one or more quadratic unconstrained binary functions through various transformations.

[0076] Here, an explanation is given to the formula using . In the embodiments of the present disclosure, in the global optimization function is a derivative function with a minimum target setting, and correspondingly, it can be integrated and summed so that the global optimization function is set as a minimization function. By setting as a function with a minimum value, the global optimization function can also be set as a function with a minimum value.

[0077] S160: Use a quantum computer to optimize and solve the global optimization function to obtain a solution result, and determine whether to deploy relay communication devices at each candidate deployment location based on the solution result.

[0078] A quantum computer is a computer that can perform mathematical or logical operations based on the laws of quantum mechanics. The quantum computer used in the implementation of this disclosure can be a computer constructed according to various annealing mechanisms with the goal of solving the Ising model. For example, the quantum computer can be a superconducting quantum annealing Ising machine, a coherent Ising machine (including an optical oscillation coherent Ising machine, an electrical oscillation coherent Ising machine, an optoelectronic oscillation coherent Ising machine), CMOS a simulated Ising machine, a memristor array Ising machine, a spatial light modulation SLM Ising machine, etc., and the embodiments of this disclosure do not make specific limitations. That is, as long as a specific quantum mechanical mechanism is used and a quantum computer capable of solving the Ising model can be used to solve the aforementioned global optimization function.

[0079] Because the global optimization function is a quadratic unconstrained binary function or a function equivalent in form thereto, and the quadratic unconstrained binary function (or the quadratic unconstrained binary model) can be converted into the Ising model through simple conversion, a quantum computer constructed based on the Ising model can also be used to optimize and solve the global optimization function to obtain a solution result.

[0080] In specific implementation, the quantum approximate optimization algorithm can also be used to convert the aforementioned global optimization function into the Ising model, and the corresponding global optimization function can be maximized into the form. Based on the aforementioned form, two rotation unitary matrices are determined: and ; C represents the Hamiltonian, represents a real number parameter related to the Hamiltonian, represents the driving Hamiltonian, represents a real number parameter related to the driving Hamiltonian.

[0081] In some specific applications of this disclosure, the quantum computer used is a hybrid quantum computer constructed based on degenerate optical parametric oscillation, and its essence is a coherent Ising machine. Specifically, the aforementioned hybrid quantum computer includes an optical part and an electrical part, where the optical part includes a laser, an amplifier, a periodically poled lithium niobate crystal, and an optical fiber loop. The laser among them is a pulsed laser, and an amplifier is configured at the output end. The laser emitted by the pulsed laser and amplified by the amplifier is processed by the lithium niobate crystal to obtain frequency-doubled laser, and the laser is used as a pump source to synchronously pump PPLNThe crystal forms a degenerate optical parametric oscillation, and hundreds of oscillation pulses can exist simultaneously in the fiber loop. The electrical part includes a phase detector, an analog-to-digital / digital-to-analog conversion part, and a data processing part (in specific implementations, FPGA or ASIC is often used. In some applications, conventional CPU or GPU can also be used). The laser output from the fiber loop and the fundamental frequency laser are measured by the phase detector to determine the phase measurement of the output light, and the measurement results are digitally encoded into data. The data processing part performs measurements and feedback control of the optical pulses based on the encoded data.

[0082] As previously analyzed, the aforementioned hybrid quantum computer uses laser pulses in the fiber as qubits for computing. During the degenerate optical parametric oscillation process, the pump light is incident on the nonlinear optical crystal and splits into two beams of light. The polarization directions of the two beams of light are the same, the frequency is half of the pump light, and they are in a squeezed state, which can be used as a qubit. Gradually increasing the power of the pump light, when it exceeds the oscillation threshold, the generated light becomes a coherent state, and the phase of the light is divided into two states (phase 0 state and π state). At this time, the corresponding phase can be set to ±1 of the spin to realize the physical characterization of the Ising model, and accordingly, it can be used to solve the Ising model.

[0083] In the embodiments of the present disclosure, after converting the global optimization function into the Ising model, the solution result can be determined according to the following steps: Based on the Ising model, a quantum circuit is built according to the quantum approximate optimization algorithm, and the parameters in the circuit are initialized, thereby initializing the quantum state. Subsequently, the quantum route is run to obtain the quantum state, and the expectation value of the global optimization function is calculated using the quantum state. For the same set of parameters , Repeat the measurement multiple times to obtain the distribution of the quantum state. After obtaining the quantum state distribution, the settlement result is determined according to the distribution probability.

[0084] Because the computing speed of the quantum computer far exceeds that of the classical computer based on gate circuits, and the mechanism of the quantum computer can avoid getting stuck in the local optimum problem at the principle level, so after constructing the global optimization function based on the known parameters (including the positions of the terminals to be covered, the number of relay communication devices, and the candidate deployment positions) using the method of this solution, the quantum computer can be used to solve the global optimization problem and determine the solution result.

[0085] According to the previous analysis of the global optimization function, the data to be solved is , that is, to determine whether the selection identifier of each candidate deployment position is 0 or 1. If it is 1, it is determined that deploying the relay communication device at the corresponding candidate deployment position can obtain the global optimal result. Accordingly, according to the solution corresponding to each candidate deployment position , it is also possible to determine whether it is necessary to deploy a relay communication device at a corresponding candidate deployment location.

[0086] In some embodiments of the present disclosure, in addition to including the foregoing S110 - S160, before performing the foregoing S150, the relay communication device deployment location determination method may further include the following S170 - S180.

[0087] S170: Determine the coverage status identifier corresponding to the terminal location based on the selection identifiers of the associated candidate locations corresponding to each terminal location.

[0088] As analyzed above, the coverage status identifier is used to indicate whether the corresponding terminal location is covered by the relay communication device. After determining the selection identifier of a certain associated candidate location , that is, after determining whether to deploy a relay communication device at a certain associated candidate location, it is possible to determine whether the terminal location corresponding to this associated candidate location is covered, that is, to determine the coverage status identifier .

[0089] S180: Based on the coverage status identifier of the terminal location and the selection identifier of the corresponding associated candidate location, construct a coverage overlap minimization constraint function with the goal of minimizing the number of relay communication devices covering each terminal location.

[0090] According to the communication principle, in order to ensure better communication quality, it is necessary to make the relay communication devices evenly distributed and reduce the overlapping coverage area as much as possible on the premise of satisfying full coverage as much as possible. Based on the foregoing goal, some embodiments of the present disclosure consider constructing an overlap minimization constraint function to achieve the even distribution of relay communication devices as much as possible.

[0091] In specific implementation, the coverage overlap minimization constraint function is constructed based on the following idea: If the distance between two relay communication devices is smaller (on the premise of ensuring that they have terminal locations covered simultaneously), the more terminal locations they cover simultaneously. No matter how many relay communication devices cover a certain terminal location, its corresponding coverage status identifier can only be 1, and from the perspective of the relay communication device, the foregoing certain terminal location is covered times; correspondingly, based on the determination from the perspective of the relay communication device and the coverage status identifier 1 of the terminal location, the number of overlapping covered terminal locations of the relay communication devices can be determined in reverse, and thus the coverage overlap minimization constraint function can be constructed. According to the foregoing idea, in some embodiments of the present disclosure, the coverage overlap minimization constraint function can be represented by characterize.

[0092] Considering that the coverage overlap minimization constraint function is originally an inequality constraint function, in order to ensure the optimization of the coverage overlap minimization constraint function, a set relaxation coefficient is introduced in some embodiments, and the corresponding coverage overlap minimization constraint function is . According to the foregoing formula, the coverage overlap minimization constraint function is also a quadratic unconstrained binary function.

[0093] In the case of executing the foregoing S170 - S180, the foregoing S150 is specifically S151 as follows.

[0094] S151: Integrate the full - coverage objective function, the quantity constraint function, and the coverage overlap minimization constraint function to obtain a global optimization function.

[0095] In specific implementation, the full - coverage objective function, the quantity constraint function, and the coverage overlap minimization constraint function can be weighted and summed to obtain a global optimization function, which is expressed by the formula as .

[0096] In the previous embodiments, it is assumed that the relay communication device can communicate with other relay communication devices through the second communication method, based on communication methods such as satellite optical fiber, etc., so as to realize the connection of the terminal devices at the terminal positions covered. However, in some embodiments, the relay communication device can only communicate with other relay communication devices using the first communication method. In this case, in order to realize the pairwise communication connection of the user terminals located at the terminal positions, it is necessary to realize the full connection between the relay communication devices. The full connection of the foregoing relay communication devices is not the pairwise direct connection between the relay communication devices, but the relay communication devices are directly connected or connected through other relay communication devices as intermediate connection nodes.

[0097] In order to achieve the full connection between the relay communication devices as much as possible, in some embodiments, before executing the foregoing S150, the following S190 - S200 can also be executed.

[0098] S190: Determine pairwise direct - connection identifiers based on the communication distances between pairwise candidate deployment positions and the communication distance threshold of the relay communication device.

[0099] The pairwise direct - connection identifiers are used to identify whether the relay communication devices can be directly communicated and connected in the case where relay communication devices are deployed at two candidate deployment positions. For the convenience of using formulas to express later, the pairwise direct - connection identifiers are represented by . In specific implementation, if the distance between the th candidate deployment position and the th candidate deployment position is less than the communication distance threshold of the relay communication device, then is 1; on the contrary, if the distance between the th candidate deployment position and the If the distance from a candidate deployment location is greater than or equal to the communication distance threshold of the relay communication device, then is 0.

[0100] S200: Based on the selection identifiers, pairwise direct connection identifiers, and the number of devices of each candidate deployment location Construct a fully connected constraint function.

[0101] After determining the pairwise direct connection identifiers, subsequently, based on the pairwise direct connection identifiers, the selection identifiers of the candidate deployment locations, and the number of devices Construct a fully connected constraint function.

[0102] In some embodiments of the present disclosure, to conveniently determine whether a fully connected network can be quickly constructed after deploying relay communication devices at each candidate deployment location, and thus construct a fully connected constraint function, the following conditional limitations are made: (1) There is a super node (which can be understood as a virtual global node) that can be connected to the relay communication devices at each candidate deployment location, and it injects a total of (i.e., the number of devices) data packets into each relay communication device in a randomly injected manner; (2) At the candidate deployment locations where relay communication devices are selected for deployment, the number of incoming data packets is more than the number of outgoing data packets. Based on the foregoing conditional limitation (1), the following presumptive conditional limitation (3) can be obtained: The maximum number of unidirectionally transferred data packets between any two candidate deployment locations that can be directly connected after deploying relay communication devices is . Correspondingly, based on the pairwise direct connection identifiers, the selection identifiers of the candidate deployment locations, and the number of devices Constructing a fully connected constraint function is based on the foregoing assumption as a premise to construct a fully connected constraint function.

[0103] In some embodiments, the method for constructing a fully connected constraint function based on the foregoing assumption includes S210 - S240 as follows.

[0104] S210: Inject a total of data packets into each candidate deployment location to construct a first connectivity constraint function.

[0105] According to a total of data packets being randomly injected into each candidate deployment location, the equation can be obtained, where is the number of data packets sent to the th candidate deployment location. When the selection identifier of the th candidate deployment location is 0 , and when the selection identifier of the th candidate deployment location is 1 is less than or equal to an integer (it should be noted that in this case may be 0).

[0106] In a specific application, the first connectivity constraint function obtained based on the foregoing equation is , that is, , specifically , where and are the binary values at each position after binary encoding, is the number of data packets injected into the th candidate deployment location. The foregoing first connectivity constraint function is a binary unconstrained binary function.

[0107] S220: Based on the fact that the number of incoming data packets at the candidate deployment location where the communication device is selected for deployment is 1 more than the number of outgoing data packets, construct a second connectivity constraint function according to the pairwise direct connection identifier, the selection identifier of each candidate deployment location, the number of randomly incoming data packets and the number of randomly outgoing data packets at each candidate deployment location.

[0108] If the relay communication devices deployed at the candidate deployment locations form a fully connected network, according to the fact that the number of incoming data packets at the candidate deployment location where the communication device is selected for deployment is 1 more than the number of outgoing data packets, the equation can be constructed. Of course, in the case where no relay communication device is deployed at the candidate deployment location, the foregoing equation still holds.

[0109] In a specific application, the second connectivity constraint function constructed based on the foregoing equation is , that is , specifically , where and are the binary data at each position after binary encoding, is the number of data packets flowing from the th candidate deployment location to the th candidate deployment location; and are the binary data at each position after binary encoding, is the number of data packets flowing from the th candidate deployment location to the th candidate deployment location, is the pairwise direct connection identifier between the th candidate deployment location and the th candidate deployment location, is the The selection identifier of the candidate deployment locations. The aforementioned second connectivity constraint function is a binary unconstrained binary function.

[0110] S230: Using the maximum number of unidirectionally flowing data packets as a constraint, respectively perform binary encoding on the number of incoming and outgoing data packets at each candidate deployment location, and construct a third connectivity constraint function based on the binary data at each location and the selection identifier at each candidate deployment location.

[0111] In specific implementation, using the maximum number of unidirectionally flowing data packets as and the selection identifier of the candidate deployment location, an inequality can be constructed and .

[0112] Performing binary encoding according to the aforementioned inequality can obtain and , where and are the binary data at each location after binary encoding, and are the binary data at each location after binary encoding.

[0113] According to the aforementioned equation, the third connectivity constraint function can be obtained.

[0114] Here, the function of the third fully connected constraint function is analyzed. According to the condition limitations for achieving full connectivity in the previous text, it is necessary to simulate the number of data packets transmitted between each candidate deployment location, and then verify whether a fully connected network can be constructed. Therefore, it is necessary to use the number of devices as a constraint to limit the data packet flow situation between each candidate deployment location, and then combine the aforementioned first connectivity constraint function and second connectivity constraint function to determine whether a fully connected network is formed by deploying relay communication devices at some candidate deployment locations. Also, because a quantum computer needs to be used to calculate the global optimization function, and the global optimization function ultimately needs to be finally converted into the form of a quadratic unconstrained binary function, and and are still in decimal representation form, they need to be binary-converted and then constructed into a binary unconstrained binary function before they can be represented at the physical machine level, and then the numerical state (whether a fully connected network is formed) can be simulated.

[0115] S240: Perform weighted summation on the first fully connected constraint function, the second fully connected constraint function, and the third fully connected constraint function to obtain the fully connected constraint function.

[0116] After obtaining the foregoing , and , the weighted sum of the foregoing three connectivity constraint functions can be used to obtain the full connectivity constraint function , where , and are pre-determined weight coefficients.

[0117] On the premise of executing the foregoing S190 - S200, S150 in the previous embodiment can be S152.

[0118] S152: Integrate the full coverage objective function, the quantity constraint function, and the full connectivity constraint function to obtain the global optimization function.

[0119] In specific implementation, the full coverage objective function, the quantity constraint function, and the full connectivity constraint function can be weighted and summed to obtain the global optimization function, which is expressed by the formula as .

[0120] In some embodiments, it is necessary to simultaneously consider the need to form a full connectivity network due to the functional limitations of the relay communication device itself, and the problem of ensuring communication quality by deploying the relay communication devices as evenly as possible. Accordingly, the full coverage objective function, the quantity constraint function, the full connectivity remainder function, and the overlap minimization constraint function can be weighted and summed to obtain the full connectivity optimization function, which is expressed by the formula as .

[0121] The foregoing provides a method for determining the deployment location of relay communication devices for determining the terminal location, the number of relay communication devices, and the candidate deployment locations at a specific moment. In practical applications, due to the movement of the user terminal, the terminal location at different moments changes due to user movement, resulting in the deployment location of the relay communication devices determined based on historical data no longer being optimal. At this time, the terminal location can be re-determined, and the foregoing method can be executed again based on the re-determined intermediate location to determine the deployment location of the relay communication devices. Similarly, when the relay communication devices need to withdraw from the network due to failures, insufficient power, etc., or when new relay communication devices join the network, the deployment location of the relay communication devices can be re-determined according to the foregoing method.

[0122] In addition to providing the foregoing method for determining the deployment location of relay communication devices, the embodiments of the present disclosure also provide a device for determining the deployment location of relay communication devices. Figure 2 is a schematic structural diagram of the device for determining the deployment location of relay communication devices provided by the embodiments of the present disclosure. As Figure 2As shown in the figure, the relay communication device deployment location determination device 200 includes a constraint determination unit 201, a function construction unit 202, a quantum computing loading unit 203, and a deployment location determination unit 204.

[0123] The constraint determination unit 201 is configured to determine multiple terminal locations to be covered, the number of relay communication devices planned to be deployed and candidate deployment locations, and based on the communication distance threshold of the relay communication device, the terminal locations, and the candidate deployment locations, determine the associated candidate locations corresponding to each terminal location. The associated candidate location is a candidate deployment location where the corresponding relay communication device covers the corresponding terminal location after the relay communication device is deployed at it.

[0124] The function construction unit 202 is configured to construct a full coverage objective function with the goal of maximizing the number of terminal locations covered after the relay communication devices are actually deployed, based on multiple terminal locations and the selection indicators of the corresponding associated candidate locations. The selection indicator is used to indicate whether a relay communication device is deployed at the corresponding associated candidate location; based on the number of devices and the selection indicators of each candidate deployment location, construct a quantity constraint function; and integrate the full coverage objective function and the quantity constraint function to obtain a global optimization function. The quantity constraint function is a function used to constrain the number of selected candidate deployment locations to be The global optimization function is a quadratic unconstrained binary function or a function equivalent to the quadratic unconstrained binary function;

[0125] The quantum computing loading unit 203 is configured to load the solution result obtained by optimizing and solving the global optimization function using a quantum computer.

[0126] The deployment location determination unit 204 is configured to determine the deployment locations of each relay communication device based on the solution result.

[0127] In some embodiments, the constraint determination unit 201 is further configured to determine the coverage status indicator corresponding to the terminal location based on the selection indicator of the associated candidate location corresponding to each terminal location. The coverage status indicator is used to determine whether the corresponding terminal location is covered by the relay communication device. Correspondingly, the function construction unit 202 is further configured to construct a coverage overlap minimization constraint function with the goal of minimizing the number of relay communication devices covering each terminal location, based on the coverage status indicator of the terminal location and the selection indicator of the corresponding associated candidate location; and integrate the full coverage objective function, the quantity constraint function, and the coverage overlap minimization constraint function to obtain a global optimization function.

[0128] In some embodiments, the function construction unit 202 constructs a coverage overlap minimization constraint function with the goal of minimizing the number of relay communication devices covering each terminal location, based on the coverage status identifier of the terminal device, the selection identifier of the corresponding associated candidate location, and a set relaxation coefficient.

[0129] In some embodiments, the constraint determination unit 201 is further configured to determine a two-way direct connection identifier based on the communication distance between each pair of candidate deployment locations and the communication distance threshold of the relay communication device. The two-way direct connection identifier is used to identify whether the relay communication devices can be directly communicatively connected when relay communication devices are deployed at both of the two candidate deployment locations. Correspondingly, the function construction unit 202 is further configured to construct a full connectivity constraint function based on the selection identifier of each candidate deployment location, the two-way direct connection identifier, and the number of devices ; and integrate the full coverage objective function, the quantity constraint function, and the said full connectivity constraint function to obtain a global optimization function.

[0130] In some embodiments, the function construction unit 202 uses a total number of data packets to be randomly injected into each candidate deployment location. At the candidate deployment locations where relay communication devices are selected for deployment, the number of incoming data packets is 1 more than the number of outgoing data packets, and the maximum number of unidirectionally flowing data packets between any two candidate deployment locations that can be directly communicatively connected after deploying relay communication devices is premised, and constructs a full connectivity constraint function according to the selection identifier of each candidate deployment location, the two-way direct connection identifier, and the number of devices .

[0131] In some embodiments, the function construction unit 202 constructs a full connectivity constraint function by the following method: randomly injecting a total number of data packets into each candidate deployment location to construct a first connectivity constraint function; with the number of incoming data packets being 1 more than the number of outgoing data packets at the candidate deployment locations where communication devices are selected for deployment, constructing a second connectivity constraint function according to the two-way direct connection identifier, the selection identifier of each candidate deployment location, the number of randomly incoming data packets and the number of randomly outgoing data packets at each candidate deployment location; with the maximum number of unidirectionally flowing data packets being as a constraint, respectively performing binary encoding on the number of incoming data packets and the number of outgoing data packets at each candidate deployment location, and constructing a third connectivity constraint function based on the binary data at each location and the selection identifier of each candidate deployment location; and performing weighted summation on the first full connectivity constraint function, the second full connectivity constraint function, and the third full connectivity constraint function to obtain a full connectivity constraint function.

[0132] In some embodiments, the first connectivity constraint function is , specifically ; The second connectivity constraint function is , specifically ; The third connectivity constraint function is .

[0133] Among them, and are the binary values at each position after binary encoding, is the number of data packets injected into the th candidate deployment location, and are the binary data at each position after binary encoding, is the number of data packets flowing from the th candidate deployment location to the th candidate deployment location; and are the binary data at each position after binary encoding, is the number of data packets flowing from the th candidate deployment location to the th candidate deployment location; is the pairwise direct connection flag between the th candidate deployment location and the th candidate deployment location, is the selection flag of the th candidate deployment location.

[0134] The embodiments of the present disclosure also provide a system for determining the deployment location of relay communication devices. Figure 3 is a schematic structural diagram of the system for determining the deployment location of relay communication devices provided by the embodiments of the present disclosure. Referring to Figure 3 , the system 300 for determining the deployment location of relay communication devices includes a first computer 301 and a second computer 302. The first computer 301 is a computer based on logic gates, and the second computer 302 is a second computer 302 based on the principle of quantum computing (specifically capable of solving the Ising model). The first computer 301 includes a memory 3011 and a processor 3012. When the computer program stored in the memory 3011 is loaded by the processor 3012, the processor 3012 executes the aforementioned method for determining the deployment location of relay communication devices to obtain an integrated objective function, and calls the second computer 302 to solve the integrated objective function to obtain a solution result, and determines whether to deploy relay communication devices at each candidate deployment location based on the solution result.

[0135] Embodiments of the present disclosure include a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes program code for executing a method for determining a deployment location of a relay communication device.

[0136] Embodiments of the present disclosure further provide a computer-readable medium, which stores a computer program. After the computer program is run on a computing device (such as a system for determining a deployment location of a relay communication device), the computing device can be caused to execute the foregoing method for determining a deployment location of a relay communication device.

[0137] The foregoing are only specific embodiments of the present disclosure, enabling those skilled in the art to understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but rather will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for determining the deployment location of a relay communication device, characterized in that, Including: Determine the positions of multiple terminals to be covered and the number of relay communication devices planned to be deployed and the candidate deployment locations, and based on the communication distance threshold of the relay communication devices, the terminal locations, and the candidate deployment locations, determine the associated candidate locations corresponding to each terminal location. Determine the two-way direct connection flags based on the communication distances between every two candidate deployment locations and the communication distance threshold of the relay communication devices. The associated candidate location is a candidate deployment location where, after deploying a relay communication device, the corresponding relay communication device can cover the corresponding terminal location. The two-way direct connection flags are used to identify whether the relay communication devices can be directly communicatively connected when relay communication devices are deployed at both of the two candidate deployment locations; Based on the selection identifiers of multiple terminal locations and corresponding associated candidate locations, so as to construct a full-coverage objective function with the goal that the maximum number of terminal locations covered after the actual deployment of the relay communication device is the target. The selection identifier is used to identify whether a relay communication device is deployed at the corresponding associated candidate location; Based on the number of said devices and the selection identifier of each candidate deployment location, construct a quantity constraint function, where the quantity constraint function is a function for constraining the number of selected candidate deployment locations to be ; Inject data packets with a total number of k into each candidate deployment location to construct the first connectivity constraint function; with the number of data packets flowing into the candidate deployment location where the communication device is selected being 1 more than the number of data packets flowing out, construct the second connectivity constraint function according to the pairwise direct connection identifiers, the selection identifiers of each candidate deployment location, the number of randomly flowing-in data packets and the number of randomly flowing-out data packets at each candidate deployment location; with the maximum number of unidirectionally flowing data packets being k as the constraint, respectively perform binary encoding on the number of data packets flowing into and out of each candidate deployment location, and construct the third connectivity constraint function based on the binary data at each location and the selection identifiers of each candidate deployment location; perform weighted summation on the first fully connected constraint function, the second fully connected constraint function and the third fully connected constraint function to obtain the fully connected constraint function; Integrate the full-coverage objective function, the quantity constraint function, and the full-connectivity constraint function to obtain a global optimization function; the global optimization function is a quadratic unconstrained binary function or a function equivalent in form to a quadratic unconstrained binary function; Use a quantum computer to perform optimization calculation on the global optimization function to obtain a calculation result, and determine whether a relay communication device is deployed at each candidate deployment location based on the calculation result.

2. The method according to claim 1, characterized in that: The method further includes: Determine the coverage status identifier corresponding to the terminal location based on the selection identifier of the associated candidate location corresponding to each terminal location; the coverage status identifier is used to determine whether the corresponding terminal location is covered by the relay communication device; Based on the coverage status identifier of the terminal location and the selection identifier of the corresponding associated candidate location, construct a coverage overlap minimization constraint function with the goal of minimizing the number of relay communication devices covering each terminal location; The integrating the full-coverage objective function, the quantity constraint function, and the full-connectivity constraint function to obtain a global optimization function includes: integrating the full-coverage objective function, the quantity constraint function, the full-connectivity constraint function, and the coverage overlap minimization constraint function to obtain the global optimization function.

3. The method according to claim 2, wherein The constructing a coverage overlap minimization constraint function based on the coverage status identifier of the terminal location and the selection identifier of the corresponding associated candidate location with the goal of minimizing the number of relay communication devices covering each terminal location includes: Based on the coverage status identifier of the terminal location, the selection identifier of the corresponding associated candidate location, and a set relaxation coefficient, construct a coverage overlap minimization constraint function with the goal of minimizing the number of relay communication devices covering each terminal location.

4. The method according to any one of claims 1-3, wherein: The first connection constraint function is; The second connectivity constraint function is ； The third connectivity constraint function is ; Wherein, and are the binary values at each position after binary encoding, is the number of data packets injected into the and are the binary data at each position after binary encoding, is the number of data packets flowing from the th candidate deployment location to the and are the binary data at each position after binary encoding, is the number of data packets flowing from the th candidate deployment location to the is the pairwise direct connection flag between the th candidate deployment location and the is the selection flag of the 5. The method according to any one of claims 1 to 3, characterized in that, The integrating the full-coverage objective function, the quantity constraint function, and the full-connectivity constraint function to obtain a global optimization function includes: Perform weighted summation on the full-coverage objective function, the quantity constraint function, and the full-connectivity constraint function according to a pre-determined weighting weight to obtain the global optimization function.

6. A device for determining the deployment location of a relay communication device, characterized in that Including: A constraint determination unit, configured to determine a plurality of terminal positions to be covered and the number of relay communication devices to be deployed as planned and candidate deployment positions, and based on the communication distance threshold of the relay communication device, the terminal positions, and the candidate deployment positions, determine associated candidate positions corresponding to each terminal position, and determine pairwise direct connection identifiers based on the communication distances between each pair of candidate deployment positions and the communication distance threshold of the relay communication device. The associated candidate position is a candidate deployment position where, after deploying a relay communication device, the corresponding relay communication device can cover the corresponding terminal position. The pairwise direct connection identifier is used to identify whether the relay communication devices can be directly communicatively connected when relay communication devices are deployed at two candidate deployment positions; A function construction unit is configured to construct a full-coverage objective function for a target such that, after the relay communication device is actually deployed, the maximum number of terminal locations covered is based on multiple terminal locations and selection identifiers corresponding to associated candidate locations, where the selection identifier is used to indicate whether a relay communication device is to be deployed at the corresponding associated candidate location; based on the number of devices and the selection identifiers of each candidate deployment location, construct a quantity constraint function; the quantity constraint function is a function for constraining the number of selected candidate deployment locations to be ; Inject into each candidate deployment location with a total number of k data packets to construct the first connectivity constraint function; with the number of incoming data packets at the candidate deployment location where the communication device is selected being 1 more than the number of outgoing data packets, construct the second connectivity constraint function according to the pairwise direct connection identifier, the selection identifier of each candidate deployment location, the number of randomly incoming data packets and the number of randomly outgoing data packets at each candidate deployment location; with the maximum number of unidirectionally flowing data packets being k as a constraint, respectively perform binary encoding on the number of incoming data packets and the number of outgoing data packets at each candidate deployment location, and construct the third connectivity constraint function based on the binary data at each location and the selection identifier of each candidate deployment location; perform weighted summation on the first complete connectivity constraint function, the second complete connectivity constraint function and the third complete connectivity constraint function to obtain the complete connectivity constraint function; Integrate the full-coverage objective function, the quantity constraint function, and the full-connectivity constraint function to obtain a global optimization function; the global optimization function is a quadratic unconstrained binary function or a function equivalent to a quadratic unconstrained binary function; A quantum computing loading unit for loading a calculation result obtained by performing optimization calculation on the global optimization function using a quantum computer; A deployment location determination unit for determining the deployment location of each relay communication device based on the calculation result.

7. A system for determining the deployment location of a relay communication device, characterized in that, It includes a first computer and a second computer based on the principle of quantum computing; the first computer includes a memory and a processor, and the memory stores a computer program. When the computer program is loaded by the processor, the processor is caused to execute the method for determining the deployment location of the relay communication device according to any one of claims 1-5 to obtain a global optimization function, and to call the second computer to solve the global optimization function to obtain a solution result, and to determine whether to deploy a relay communication device at each candidate deployment location based on the solution result.

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