Method and system for determining deployment position of relay communication equipment

Abstract

The embodiment of the invention provides a method and system for determining the deployment position of relay communication equipment. The method for determining the deployment position of the relay communication equipment comprises the following steps: determining terminal positions, the number of planned deployment relay communication equipment, candidate deployment positions and associated candidate positions corresponding to the terminal positions; constructing a full-coverage target function by taking the maximum terminal position covered by the relay communication equipment after actual deployment as a target, and constructing a quantity constraint function based on the equipment quantity and the selection identifier of each candidate deployment position; integrating the full-coverage objective function and the number constraint function to obtain a global optimization function; and performing optimization solution on the global optimization function by adopting a quantum computer to obtain a solution result, and determining whether the relay communication equipment is deployed at each candidate deployment position or not based on the solution result. According to the scheme provided by the embodiment of the invention, the fast determination of the deployment position of the relay communication equipment can be realized by utilizing the resolving speed advantage of the quantum computer and the advantage that the physical machine can avoid a local optimal result.

Description

Technical Field

[0001] The present disclosure relates to the field of coordinated optimization technology, and in particular to a method and system for determining a deployment location of relay communication equipment. Background Art

[0002] In the event of a base station failure and communication interruption due to sudden natural disasters, emergency communications need to be deployed and full coverage of emergency communications should be achieved as much as possible. To achieve the above goals, it is necessary to reasonably allocate the deployment location of relay communication equipment used for emergency communications and optimize the networking as much as possible.

[0003] The related art proposes a method for determining the deployment location of relay communication equipment using artificial intelligence and conventional optimization algorithms; however, the aforementioned method can only be deployed in classical computers and is subject to the limitations of the computing power of classical computers. When the value is too large, the computational complexity expands rapidly and the computational results cannot be obtained quickly. In addition, due to insufficient computing power and algorithm limitations, only local optimal results can be obtained, rather than global optimal results. Summary of the invention

[0004] In order to solve the problem that the calculation of the deployment position of the existing relay equipment is time-consuming and the calculation results cannot be obtained in time, the embodiments of the present disclosure provide a new method and system for determining the deployment position of relay communication equipment.

[0005] In a first aspect, an embodiment of the present disclosure provides a method for determining a deployment location of a relay communication device, including: Determine the locations of multiple terminals to be covered and the number of relay communication devices to be deployed and candidate deployment positions, and determining associated candidate positions corresponding to each terminal position based on a communication distance threshold of the relay communication device, the terminal position and the candidate deployment positions, wherein the associated candidate position is a candidate deployment position where the corresponding relay communication device covers the corresponding terminal position after the relay communication device is deployed; Based on multiple terminal locations and corresponding selection identifiers of associated candidate locations, a full coverage objective function is constructed so that the terminal locations covered by the relay communication device after actual deployment are at most the target, wherein the selection identifier is used to identify whether the corresponding associated candidate location is deployed with a relay communication device; Based on the number of devices and the selection identifiers of the candidate deployment locations, and construct a quantity constraint function, wherein the quantity constraint function is used to constrain the number of selected candidate deployment locations to Function of 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 to the quadratic unconstrained binary function; A quantum computer is used to optimize and solve the global optimization function to obtain a solution result, and based on the solution result, it is determined whether to deploy relay communication equipment at each candidate deployment location.

[0006] Optionally, the method further includes: determining a coverage status identifier corresponding to the terminal position based on a selection identifier of an associated candidate position corresponding to each terminal position; the coverage status identifier is used to determine whether the corresponding terminal position is covered by the relay communication device; Based on the coverage status identifier of the terminal position and the corresponding selection identifier of the associated candidate position, a coverage overlap minimization constraint function is constructed with the goal of minimizing the number of relay communication devices covering each terminal position; 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.

[0007] Optionally, the coverage status identifier based on the terminal position and the corresponding selection identifier of the associated candidate position are used to make the number of relay communication devices covering each terminal position At least construct a coverage overlap minimization constraint function for the objective, including: Based on the coverage status identifier of the terminal position, the corresponding selection identifier of the associated candidate position and the set relaxation coefficient, a coverage overlap minimization constraint function is constructed with the goal of minimizing the number of relay communication devices covering each terminal position.

[0008] Optionally, the method further includes: determining a pairwise direct communication identifier based on the communication distance between each candidate deployment location and the communication distance threshold of the relay communication device, wherein the pairwise direct communication identifier is used to identify whether the relay communication device can directly communicate with each other when the relay communication device is deployed at both candidate deployment locations; Based on the selection identifier of each candidate deployment location, the pairwise direct identification and the number of devices Construct a fully connected constraint function; 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 connectivity constraint function to obtain a global optimization function.

[0009] Optionally, the selection identifier based on each candidate deployment location, the pairwise direct access identifier and the number of devices Construct a fully connected constraint function, including: The total number is data packets are injected into each candidate deployment location, the number of incoming data packets at the candidate deployment location where the relay communication device is selected is 1 more than the number of outgoing data packets, and the number of data packets flowing in one direction between any two candidate deployment locations that can communicate directly after the relay communication device is deployed is at most Based on the selection identifier, pairwise direct identifier and number of devices of each candidate deployment location, Construct a fully connected constraint function.

[0010] Optionally, the selection identifier, pairwise direct identification and number of devices of each candidate deployment location are selected. Construct a fully connected constraint function, including: The total number is The data packets are injected into each candidate deployment location to construct the first connectivity constraint function; The number of incoming data packets at the candidate deployment location where the communication device is deployed is one more than the number of outgoing data packets, and a second connectivity constraint function is constructed according to the pairwise direct identification, the selection identification of each candidate deployment location, the number of randomly incoming data packets at each candidate deployment location, and the number of randomly outgoing data packets; The maximum number of packets that can flow in one direction is as constraints, the number of inbound data packets and outbound data packets at each candidate deployment position are binary-encoded respectively, and a third connectivity constraint function is constructed based on the binary data at each position of the binary coding and the selection identifier at each candidate deployment position; The first full connectivity constraint function, the second full connectivity constraint function and the third full connectivity constraint function are weightedly summed to obtain the full connectivity constraint function.

[0011] Optionally, the first connectivity constraint function is ; The second connectivity constraint function is: ; The third connectivity constraint function is ; in, To inject into The number of packets at candidate deployment locations, For The candidate deployment locations flow into the The number of packets at candidate deployment locations, For The candidate deployment locations flow into the The number of packets at candidate deployment locations, For the candidate deployment locations and The pairwise direct identification of candidate deployment locations, For the The selection identifier of the candidate deployment location, and for The binary data at each position after binary encoding, and for The binary data at each position after binary encoding.

[0012] Optionally, the integrating the full coverage objective function and the quantity constraint function to obtain a global optimization function includes: performing weighted summation of the full coverage objective function and the quantity constraint function according to predetermined weighted weights to obtain the global optimization function.

[0013] In a second aspect, the present disclosure provides a device for determining a deployment location of a relay communication device, characterized in that it includes: Constraint determination unit, used to determine the number of devices of multiple terminal locations to be covered and the relay communication devices planned to be deployed and candidate deployment positions, and determining associated candidate positions corresponding to each terminal position based on a communication distance threshold of the relay communication device, the terminal position and the candidate deployment positions, wherein the associated candidate position is a candidate deployment position where the corresponding relay communication device covers the corresponding terminal position after the relay communication device is deployed; A function construction unit is used to construct a full coverage target function based on multiple terminal locations and corresponding selection identifiers of associated candidate locations so that the terminal locations covered by the relay communication device after actual deployment are at most the target, wherein the selection identifier is used to identify whether the corresponding associated candidate location is deployed with a relay communication device; based on the number of the devices and the selection identifiers 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; wherein the quantity constraint function is 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; A quantum computing loading unit, used for loading a solution result obtained by optimizing and solving the global optimization function using a quantum computer; The deployment position determination unit is used to determine the deployment position of each relay communication device based on the solution result.

[0014] In a third aspect, an embodiment of the present disclosure provides a system for determining the deployment location of relay communication equipment, comprising a first computer based on gate circuits and a second computer based on quantum computing principles; the first computer comprises a memory and a processor, the memory stores a computer program, and when the computer program is loaded by the processor, the processor executes the method for determining the deployment location of the relay communication equipment as described above to obtain an integrated objective function, and calls the second computer to solve the integrated objective function to obtain a solution result, and determines whether the relay communication equipment is deployed at each candidate deployment location based on the solution result.

[0015] By adopting the embodiment of the present disclosure, after constructing a global optimization function based on known parameters (including the terminal locations to be covered, the number of relay communication devices, and candidate deployment locations), a quantum computer can be used to solve the global optimization problem, and after determining the solution results, the optimal relay communication device deployment location can be determined based on the solution results. By adopting the embodiment of the present disclosure, the speed advantage of quantum computers can be used to quickly determine the deployment location of relay communication devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art description. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work, including: Figure 1 is a flow chart of a method for deploying a relay communication device according to an embodiment of the present disclosure; Figure 2 It is a structural diagram of a device for determining a deployment position of a relay communication device provided in an embodiment of the present disclosure; Figure 3 It 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

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] S110: Determine multiple terminal locations to be covered, the number of relay communication devices planned to be deployed, and candidate deployment locations.

[0023] 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.

[0024] 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.

[0025] To facilitate the use of formulas in the following text, the terminal position is Indicates that the set of terminal positions is express.

[0026] Relay communication equipment is a communication equipment that directly communicates with user terminals (in some cases, it may also be through other relay communication equipment or other types of communication equipment) to achieve indirect connection between user terminals and other user terminals or other communication equipment. In practical applications, relay communication equipment can be base stations, repeaters and other equipment.

[0027] In some embodiments, the relay communication device may use a first communication method to connect to the user terminal, but use a second communication method to communicate with other devices, thereby realizing the interconnection between the user terminal and other communication devices. GSM , CDMA It is connected to the user terminal by communication methods such as satellite communication and optical fiber communication.

[0028] In some other embodiments, the relay communication device and the user terminal, and the relay communication device and the relay communication device communicate in the same communication mode (for example, using GSM , CDMA Communication mode communication), at this time, the relay communication equipment can realize mutual communication between the covered user terminals by forming a fully connected network.

[0029] In some applications, the relay communication equipment is a communication equipment that can be quickly moved and deployed. For example, the relay communication equipment can be carried by vehicles, drones, ships and other types of vehicles to achieve rapid movement and rapid deployment.

[0030] It should be noted here that the number of devices of the relay communication device It is predetermined and will not be changed during the implementation of the method in this embodiment.

[0031] The candidate deployment location is predetermined and can be used to deploy the relay communication device. To facilitate the use of formulas in the following text, the candidate deployment location is ( ) indicates that the candidate deployment location set composed of candidate deployment locations adopts express.

[0032] 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, and accordingly, only one relay communication device needs to be deployed in one candidate deployment location. According to the above 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 the ability to cover a large area, and the number of candidate deployment locations is The number of devices is much larger than that of communication devices .

[0033] S120: Determine associated candidate positions corresponding to each terminal position based on the communication distance threshold of the relay communication device, the terminal position and the candidate deployment positions.

[0034] The communication distance threshold of the relay communication device is the distance from the farthest terminal device (or other communication device) to the relay communication device that can be covered while ensuring the communication quality. The communication distance threshold of the relay communication device is predetermined based on the model of the relay communication device, the transmit and receive power and other parameters.

[0035] The associated candidate locations corresponding to each terminal location are candidate deployment locations that can achieve communication coverage at the terminal location after the relay communication device is deployed. 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. Correspondingly, all candidate deployment locations within the aforementioned circle are associated candidate locations.

[0036] According to the above-mentioned symbol definition, in the present disclosure example, the corresponding terminal position The associated candidate position is adopted Based on the previous analysis, it can be determined that Candidate deployment locations A subset of .

[0037] S130: constructing a full coverage objective function based on selection identifiers of multiple terminal locations and corresponding associated candidate locations so that the number of terminal locations covered by the relay communication device after actual deployment is at most the target.

[0038] The selection flag is used to identify whether the corresponding associated candidate location is deployed with an intermediate communication device. For example, in a specific application, select the identifier 1 or 0; a candidate position for association The selection flag is 0, indicating that this candidate coverage location is (hypothetically) selected to deploy relay communication equipment; a certain associated candidate location The selection flag is 1, indicating that this candidate coverage location is not selected and no relay communication equipment will be deployed at this candidate coverage location.

[0039] According to the analysis above, when determining the selection mark of a certain associated candidate position After that, that is, after determining whether a certain associated candidate position is deployed with a relay communication device, it can be determined whether the terminal position corresponding to the associated candidate position is covered.

[0040] Specifically, if the selection mark at a certain associated candidate position Set to 1, each terminal location associated with this associated candidate location can be covered by the relay communication device, and the corresponding coverage status indicator of this terminal location 1. The selection identifier of multiple candidate deployment locations associated with a terminal location All are set to 1, indicating the coverage status of this terminal location is also 1. If the selection flags of all candidate deployment locations associated with a terminal location All are set to 0, indicating the coverage status of this terminal position is 0. After determining the coverage status identifier corresponding to each terminal position, the communication coverage status of all terminal positions is determined.

[0041] In coverage status mark On the premise that 1 represents that the terminal position is covered by communication, the aforementioned goal of making the relay communication device actually cover the most terminal positions can be to add up the coverage status identifiers of all terminal positions and make the sum the maximum as the goal. In other words, the aforementioned goal can be adopted Representation.

[0042] After determining the aforementioned goal of covering the maximum number of terminal devices after the actual deployment of the relay communication device, a full coverage objective function can be constructed accordingly. In some embodiments of the present disclosure, the full coverage objective function adopts Characterization. Based on the above formula, it can be determined is a function with a minimum value. Why is Set it to the function with the minimum value, and do a detailed analysis later.

[0043] S140: Constructing a quantity constraint function based on the number of devices and the selection identifiers of each candidate deployment location.

[0044] As analyzed above, the number of relay communication devices planned to be deployed to cover terminal location communications is , and the aforementioned Relay communication devices must be used, one more or one less will not work. Correspondingly, among all candidate deployment locations, only candidate deployment locations are selected, that is, there is only one The selection flag of each candidate deployment location should be set to 1.

[0045] According to the previous condition constraints, the quantity constraint function constructed in the embodiment of the present disclosure is: .because 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 used. According to the previous analysis and the form of the quantity constraint function, it can be known that the quantity constraint function It is a function that can ideally obtain the minimum value.

[0046] S150: Integrate the full coverage objective function and the quantity constraint function to obtain a global optimization function.

[0047] After obtaining the aforementioned full coverage objective function and quantity constraint function, a global optimization function for characterizing the post-deployment conditional characteristics of the relay communication equipment can be obtained based on the aforementioned full coverage objective function and quantity constraint function.

[0048] In some embodiments of the present disclosure, the integration of the full coverage objective function and the quantity constraint function may be to perform weighted summation of the full coverage objective function and the quantity constraint function to obtain a global optimization function. and quantity constraint function The expression of the global optimization function ,in and is the corresponding weight coefficient, and Both are greater than 0.

[0049] It can be imagined that, because the quantity constraint function is a quadratic unconstrained binary function, middle It can only be 0 or 1, so the global optimization function is also a quadratic unconstrained binary function.

[0050] Of course, if the quantity constraint function is a function equivalent to a quadratic unconstrained binary function, then the global optimization function is also a function equivalent to a quadratic unconstrained binary function. Here, being equivalent to a binary unconstrained binary function means converting a non-quadratic unconstrained binary function into one or more quadratic unconstrained binary functions through various transformations.

[0051] Here is Formula used In the embodiment of the present disclosure, the global optimization function The derivative function is set based on the minimization objective, and the corresponding sum can be integrated so that the global optimization function is set to the minimization function. Setting it to the function with the minimum value also makes it possible to set the global optimization function to the function with the minimum value.

[0052] S160: Using a quantum computer to optimize and solve the global optimization function to obtain a solution result, and determining whether to deploy relay communication equipment at each candidate deployment location based on the solution result.

[0053] 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 the present disclosure can be a computer built according to various annealing mechanisms for the purpose of solving the Ising model. For example, a 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 Analog Ising machine, memristor array Ising machine, spatial light modulation SLM Ising machine, etc., is not particularly limited in the embodiments of the present disclosure. That is to say, as long as a specific quantum mechanics mechanism is adopted, any quantum computer that can solve the Ising model can be used to solve the aforementioned global optimization function.

[0054] Because the global optimization function is a quadratic unconstrained binary function or a function equivalent to it in form, and the quadratic unconstrained binary function (or quadratic unconstrained binary model) can be converted into the Ising model through a simple transformation, the quantum computer built based on the Ising model can also optimize and solve the global optimization function to obtain the solution result.

[0055] In the 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 as Based on the above form, two rotation unitary matrices are determined: and ; C represents the Hamiltonian, represents the real parameter associated with the Hamiltonian, represents the driving Hamiltonian, represents the real parameter associated with the driving Hamiltonian.

[0056] In some specific applications of the present disclosure, the quantum computer used is a hybrid quantum computer based on degenerate optical parametric oscillation, which is essentially a coherent Ising machine. Specifically, the aforementioned hybrid quantum computer includes an optical part and an electrical part, wherein the optical part includes a laser, an amplifier, a periodically polarized lithium niobate crystal and an optical fiber loop. The laser 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 a frequency-doubled laser, and the laser is used as a pump source to synchronously pump a fiber loop. PPLN Crystals form degenerate optical parametric oscillations, 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 the specific implementation, it is often used). FPGA or ASIC , conventional CPU or GPU). The laser output from the optical fiber loop and the fundamental frequency laser are measured by a phase detector to determine the phase measurement of the output light, and the measurement results are digitally encoded data. The data processing part measures and feedback controls the optical pulse based on the encoded data.

[0057] As analyzed above, the aforementioned hybrid quantum computer uses laser pulses in optical fibers as quantum bits for calculations. In the process of degenerate optical parametric oscillation, the pump light is incident on the nonlinear optical crystal and split into two beams of light. The two beams have the same polarization direction and half the frequency of the pump light. They are in a compressed state and can be used as a quantum bit. By gradually increasing the power of the pump light and exceeding 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 phase can be set to ±1 of the spin, realizing the physical and chemical characterization of the Ising model, and can be used to solve the Ising model accordingly.

[0058] In the disclosed embodiment, after the global optimization function is converted into the Ising model, the solution result can be determined according to the following steps: a quantum circuit is built based on the Ising model according to the quantum approximate optimization algorithm, and the parameters in the circuit are initialized to achieve the initialization of the quantum state. Then the quantum circuit is run to obtain the quantum state, and the expected value of the global optimization function is calculated using the quantum state. For the same set of parameters , Repeat the measurement many times to obtain the distribution of quantum states. After obtaining the distribution of quantum states, the settlement result is determined based on the distribution probability.

[0059] Because the computing speed of quantum computers far exceeds that of classical computers based on gate circuits, and the mechanism of quantum computers can avoid falling into the problem of local optimality from a principle level, after using this solution method to construct a global optimization function based on known parameters (including the terminal locations to be covered, the number of relay communication devices, and candidate deployment locations), quantum computers can be used to solve the global optimization problem and determine the solution results.

[0060] According to the analysis of the global optimization function in the previous article, the data to be solved is , that is, determine whether the selection flag of each candidate deployment location is 0 or 1. If it is 1, it is determined that the deployment of relay communication equipment at the corresponding candidate deployment location can obtain the global optimal result. , it can also be determined whether it is necessary to deploy a relay communication device at the corresponding candidate deployment location.

[0061] In some embodiments of the present disclosure, in addition to the aforementioned S110-S160, the method for determining the deployment location of the relay communication device may also include the following S170-S180 before executing the aforementioned S150.

[0062] S170: Determine the coverage status identifier corresponding to the terminal position based on the selection identifier of the associated candidate position corresponding to each terminal position.

[0063] As analyzed above, the coverage status flag is used to indicate whether the corresponding terminal location is covered by the relay communication device. After that, that is, after determining whether a relay communication device is deployed at a certain associated candidate position, it is possible to determine whether the terminal position corresponding to the associated candidate position is covered, that is, to determine the coverage status flag. .

[0064] S180: Based on the coverage status identifier of the terminal position and the corresponding selection identifier of the associated candidate position, a coverage overlap minimization constraint function is constructed with the goal of minimizing the number of relay communication devices covering each terminal position.

[0065] According to the communication principle, in order to ensure better communication quality, it is necessary to evenly distribute the relay communication devices and reduce the overlapping coverage areas while ensuring full coverage as much as possible. Based on the above objectives, some embodiments of the present disclosure consider constructing an overlap minimization constraint function to achieve even distribution of relay communication devices as much as possible.

[0066] In the 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 (under the premise of ensuring that both have terminal locations covered at the same time), the more terminal locations they cover at the same time. No matter how many relay communication devices cover a certain terminal location, its corresponding coverage status flag can only be 1, and from the perspective of the relay communication device, the aforementioned terminal location is covered. times; accordingly, based on the perspective of the relay communication device The number of terminal positions overlapped by the relay communication device can be determined from the terminal position coverage status identifier 1, and the coverage overlap minimization constraint function can be constructed. According to the above ideas, in some embodiments of the present disclosure, the coverage overlap minimization constraint function can be adopted. Representation.

[0067] Considering that the coverage overlap minimization constraint function is an inequality constraint function, in order to ensure the optimization of the coverage overlap minimization constraint function, a relaxation coefficient is introduced in some embodiments. The corresponding coverage overlap minimization constraint function is: According to the above formula, the coverage overlap minimization constraint function is also a quadratic unconstrained binary function.

[0068] When executing the aforementioned S170-S180, the aforementioned S150 is specifically the following S151.

[0069] S151: Integrate the full coverage objective function, the quantity constraint function and the coverage overlap minimization constraint function to obtain a global optimization function.

[0070] In the 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 the global optimization function, which can be expressed as: .

[0071] In the above 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, thereby realizing the connectivity of terminal devices at the covered terminal locations. 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 two-to-two communication connectivity of user terminals located at the terminal locations, it is necessary to realize full connectivity between the relay communication devices. The full connectivity of the aforementioned relay communication devices is not the direct connectivity between the relay communication devices, but the direct connectivity between the relay communication devices or the connectivity through other relay communication devices as intermediate connection nodes.

[0072] In order to achieve full connectivity between relay communication devices as much as possible, in some embodiments, before executing the aforementioned S150, the following S190-S200 may also be executed.

[0073] S190: Determine a pairwise direct communication identifier based on the communication distance between each candidate deployment location and the communication distance threshold of the relay communication device.

[0074] The two-to-two direct communication mark is used to indicate whether the relay communication devices can communicate directly when the relay communication devices are deployed at two candidate deployment locations. In the specific implementation, if candidate deployment locations and If the distance between candidate deployment locations is less than the communication distance threshold of the relay communication device, then is 1; on the contrary, if candidate deployment locations and If the distance between the candidate deployment locations is greater than or equal to the communication distance threshold of the relay communication device, then is 0.

[0075] S200: Based on the selection identifier, pairwise direct identifier, and number of devices for each candidate deployment location Construct a fully connected constraint function.

[0076] After determining the pairwise direct identification, the candidate deployment location selection identification and the number of devices can be used to determine the pairwise direct identification. Construct a fully connected constraint function.

[0077] In some embodiments of the present disclosure, in order to conveniently determine whether a fully connected network can be quickly constructed after the relay communication devices are deployed at each candidate deployment location, and then construct a fully connected constraint function, the following conditions are defined: (1) There is a super node (which can be understood as a virtual global node) that can connect to the relay communication devices at each candidate deployment location, and the total number of nodes injected into each relay communication device in a random manner is (i.e., the number of devices); (2) the number of incoming packets at the candidate deployment location where the relay communication device is selected is greater than the number of outgoing packets. Based on the above conditional limitation (1), the following inferred conditional limitation (3) can be obtained: the maximum number of packets flowing in one direction between any two candidate deployment locations that can be directly connected after the relay communication device is deployed is . Accordingly, based on the pairwise direct identification, the selection identification of the candidate deployment location and the number of devices The construction of the full connectivity constraint function is based on the aforementioned assumptions.

[0078] In some embodiments, the method for constructing a full connectivity constraint function based on the aforementioned assumptions includes the following S210 - S240 .

[0079] S210: Total Data packets are injected into each candidate deployment location to construct the first connectivity constraint function.

[0080] According to the total Data packets are randomly injected into each candidate deployment location, and the equation can be obtained ,in To the The number of packets sent by the candidate deployment location, When the selection flag of the candidate deployment location is 0 , in When the selection flag of the candidate deployment location is 1 is less than or equal to integer (it should be noted that in this case may be 0).

[0081] In a specific application, the first connectivity constraint function obtained based on the above equation is: The aforementioned first connectivity constraint function is a binary unconstrained binary function.

[0082] S220: When the number of incoming data packets at the candidate deployment location selected to deploy the communication device is 1 more than the number of outgoing data packets, a second connectivity constraint function is constructed according to pairwise direct identification, the selection identification 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.

[0083] If the relay communication devices deployed at the candidate deployment locations form full connectivity, the equation can be constructed based on the number of incoming packets being one more than the number of outgoing packets at the candidate deployment locations where the communication devices are deployed. Of course, when no relay communication equipment is deployed at the candidate deployment location, the aforementioned equation still holds true.

[0084] In a specific application, the second connectivity constraint function constructed based on the above equation is: The aforementioned second connectivity constraint function is a binary unconstrained binary function.

[0085] S230: The maximum number of packets flowing in one direction is The number of inbound and outbound data packets at each candidate deployment location is binary-encoded, and a third connectivity constraint function is constructed based on the binary data at each binary-encoded location and the selection identifier at each candidate deployment location.

[0086] In the specific implementation, the maximum number of packets transferred in one direction is And the selection identifier of the candidate deployment location can construct the inequality as well as .

[0087] According to the above inequality, binary coding can be obtained as well as ,in and for The binary data at each position after binary encoding, and for The binary data at each position after binary encoding.

[0088] According to the above equation, we can get the third constraint function .

[0089] Here we analyze the role of the third full connectivity constraint function. According to the conditions for achieving full connectivity mentioned above, it is necessary to simulate the number of data packets transmitted between each candidate deployment location to verify whether a fully connected network can be built. Therefore, it is necessary to use the number of devices The constraints limit the flow of data packets between the candidate deployment locations, and then combine the aforementioned first and second connectivity constraint functions to determine whether the deployment of relay communication equipment at certain candidate deployment locations forms a fully connected network. Because quantum computers are needed to calculate the global optimization function, and the global optimization function must ultimately be a binary function that can be converted into a quadratic unconstrained form, and It is still in the form of decimal representation. It needs to be converted into binary and then constructed into a binary unconstrained binary function before it can be represented at the physical machine level and its numerical state (whether a fully connected network is formed) can be simulated.

[0090] S240: 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.

[0091] After obtaining the aforementioned , and After that, the weighted sum of the above three connectivity constraint functions can be obtained to obtain the full connectivity constraint function ,in , and is a predetermined weight coefficient.

[0092] On the premise of executing the aforementioned S190-S200, S150 in the foregoing embodiment may be S152.

[0093] S152: Integrate the full coverage objective function, the quantity constraint function and the full connectivity constraint function to obtain a global optimization function.

[0094] In the 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 can be expressed as: .

[0095] In some embodiments, it is necessary to simultaneously consider the need to form a fully connected network due to the functional limitations of the relay communication equipment itself, and to ensure the communication quality by deploying the relay communication equipment 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 as follows: .

[0096] The foregoing article provides a method for determining the deployment location of a relay communication device based on the terminal location, the number of relay communication devices, and the candidate deployment locations at a specific moment. In actual applications, due to the movement of the user terminal, the terminal location at different moments changes due to the user's movement, causing the deployment location of the relay communication device determined based on historical data to no longer be 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 position to determine the deployment location of the relay communication device. Similarly, when the relay communication device needs to exit the network due to a fault, insufficient power, etc., or when a new relay communication device joins the network, the deployment location of the relay communication device can be re-determined according to the foregoing method.

[0097] In addition to providing the aforementioned method for determining a deployment location of a relay communication device, an embodiment of the present disclosure also provides an apparatus for determining a deployment location of a relay communication device. Figure 2 Schematic diagram of the structure of the apparatus for determining the deployment position of relay communication equipment provided by the embodiment of the present disclosure. Figure 2 As shown, the apparatus 200 for determining a deployment position of a relay communication device includes a constraint determination unit 201 , a function construction unit 202 , a quantum computing loading unit 203 and a deployment position determination unit 204 .

[0098] The constraint determination unit 201 is used to determine the number of devices of multiple terminal locations to be covered and the relay communication devices to be deployed. and candidate deployment positions, and based on the communication distance threshold of the relay communication device, the terminal position and the candidate deployment positions, determine the associated candidate positions corresponding to each terminal position, the associated candidate positions being the candidate deployment positions where the corresponding relay communication devices cover the corresponding terminal positions after the relay communication devices are deployed.

[0099] The function construction unit 202 is used to construct a full coverage target function based on multiple terminal locations and corresponding associated candidate location selection identifiers so that the terminal locations covered by the relay communication device after actual deployment are at most the target, and the selection identifier is used to identify whether the corresponding associated candidate location is deployed with a relay communication device; based on the number of devices and the selection identifiers 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; wherein the quantity constraint function is used to constrain the number of selected candidate deployment locations to The global optimization function is a quadratic unconstrained binary function or a function equivalent to a quadratic unconstrained binary function; The quantum computing loading unit 203 is used to load the solution result obtained by optimizing and solving the global optimization function using a quantum computer.

[0100] The deployment position determining unit 204 is used to determine the deployment position of each relay communication device based on the solution result.

[0101] In some embodiments, the constraint determination unit 201 is further used to determine the coverage status identifier corresponding to the terminal position based on the selection identifier of the associated candidate position corresponding to each terminal position; the coverage status identifier is used to determine whether the corresponding terminal position is covered by the relay communication device. Correspondingly, the function construction unit 202 is also used to construct a coverage overlap minimization constraint function based on the coverage status identifier of the terminal position and the selection identifier of the corresponding associated candidate position, so as to minimize the number of relay communication devices covering each terminal position; and integrate the full coverage objective function, the quantity constraint function and the coverage overlap minimization constraint function to obtain a global optimization function.

[0102] In some embodiments, the function construction unit 202 constructs a coverage overlap minimization constraint function based on the coverage status identifier of the terminal device, the selection identifier of the corresponding associated candidate position and the set relaxation coefficient to minimize the number of relay communication devices covering each terminal position.

[0103] In some embodiments, the constraint determination unit 201 is further used to determine a pairwise direct communication identifier based on the communication distance between each candidate deployment location and the communication distance threshold of the relay communication device. The pairwise direct communication identifier is used to identify whether the relay communication device can communicate directly when the two candidate deployment locations are both deployed with relay communication devices. Correspondingly, the function construction unit 202 is also used to determine the pairwise direct communication identifier based on the selection identifier, pairwise direct communication identifier and device quantity of each candidate deployment location. Constructing a full connectivity constraint function; and integrating the full coverage objective function, the quantity constraint function and the full connectivity constraint function to obtain a global optimization function.

[0104] In some embodiments, the function construction unit 202 takes the total number as The data packets are randomly injected into each candidate deployment location, the number of incoming data packets at the candidate deployment location where the relay communication device is selected is 1 more than the number of outgoing data packets, and the number of data packets flowing in one direction between any two candidate deployment locations that can communicate directly after the relay communication device is deployed is at most Based on the selection identifier, pairwise direct identifier and number of devices of each candidate deployment location, Construct a fully connected constraint function.

[0105] In some embodiments, the function construction unit 202 constructs the full connectivity constraint function using the following method: The data packets are randomly injected into each candidate deployment position to construct the first Unicom constraint function; the number of incoming data packets at the candidate deployment position where the communication device is selected is 1 more than the number of outgoing data packets, and the number of random incoming data packets and the number of random outgoing data packets at each candidate deployment position are used to construct the second Unicom constraint function; the maximum number of unidirectionally transferred data packets is The number of inbound and outbound data packets at each candidate deployment location is binary-encoded, and a third connectivity constraint function is constructed based on the binary data at each binary-encoded location and the selection identifier at each candidate deployment location. The first, second and third full connectivity constraint functions are weightedly summed to obtain a full connectivity constraint function.

[0106] In some embodiments, the first connectivity constraint function is ; The second connectivity constraint function is ; The third connectivity constraint function is .

[0107] in, To inject into The number of packets at candidate deployment locations, For The candidate deployment locations flow into the The number of packets at candidate deployment locations, For The candidate deployment locations flow into the The number of packets at candidate deployment locations, For the candidate deployment locations and The pairwise direct identification of candidate deployment locations, For the The selection identifier of the candidate deployment location, and for The binary data at each position after binary encoding, and for The binary data at each position after binary encoding.

[0108] The disclosed embodiment also provides a system for determining a deployment location of a relay communication device. Figure 3 Schematic diagram of the system for determining the deployment location of a relay communication device provided by an embodiment of the present disclosure. Figure 3The relay communication device deployment location determination system 300 includes a first computer 301 and a second computer 302. The first computer 301 is a computer based on gate circuits, and the second computer 302 is a second computer 302 based on quantum computing principles (specifically capable of solving the Ising model). The first computer 301 includes a memory 3011 and a processor 3012. The memory 3011 stores a computer program. When the computer program is loaded by the processor 3012, the processor 3012 executes the aforementioned relay communication device deployment location determination method 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 each candidate deployment location is to deploy a relay communication device based on the solution result.

[0109] An embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing a method for determining a deployment location of a relay communication device.

[0110] The embodiments of the present disclosure also provide a computer-readable medium storing a computer program. After the computer program is executed by a computing device (such as a relay communication device deployment location determination system), the computing device can execute the aforementioned relay communication device deployment location determination method.

[0111] The above are only specific embodiments of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may 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 will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for determining a deployment location of a relay communication device, characterized in that: include: Determine the locations of multiple terminals to be covered and the number of relay communication devices to be deployed and candidate deployment positions, and determining associated candidate positions corresponding to each terminal position based on a communication distance threshold of the relay communication device, the terminal position and the candidate deployment positions, wherein the associated candidate position is a candidate deployment position where the corresponding relay communication device covers the corresponding terminal position after the relay communication device is deployed; Based on multiple terminal locations and corresponding selection identifiers of associated candidate locations, a full coverage objective function is constructed so that the terminal locations covered by the relay communication device after actual deployment are at most the target, wherein the selection identifier is used to identify whether the corresponding associated candidate location is deployed with a relay communication device; Based on the number of devices and the selection identifiers of the candidate deployment locations, and construct a quantity constraint function, wherein the quantity constraint function is used to constrain the number of selected candidate deployment locations to Function of 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 to the quadratic unconstrained binary function; A quantum computer is used to optimize and solve the global optimization function to obtain a solution result, and based on the solution result, it is determined whether to deploy relay communication equipment at each candidate deployment location.

2. The method according to claim 1, characterized in that: The method further comprises: Determine the coverage status identifier corresponding to the terminal position based on the selection identifier of the associated candidate position corresponding to each terminal position; the coverage status identifier is used to determine whether the corresponding terminal position is covered by the relay communication device; Based on the coverage status identifier of the terminal position and the corresponding selection identifier of the associated candidate position, a coverage overlap minimization constraint function is constructed with the goal of minimizing the number of relay communication devices covering each terminal position; 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.

3. The method according to claim 2, characterized in that The coverage status identifier based on the terminal position and the corresponding selection identifier of the associated candidate position are used to make the number of relay communication devices covering each terminal position At least construct a coverage overlap minimization constraint function for the objective, including: Based on the coverage status identifier of the terminal position, the corresponding selection identifier of the associated candidate position and the set relaxation coefficient, a coverage overlap minimization constraint function is constructed with the goal of minimizing the number of relay communication devices covering each terminal position.

4. The method according to any one of claims 1 to 3, characterized in that: Also includes: Determine a pairwise direct communication flag based on the communication distance between each candidate deployment location and the communication distance threshold of the relay communication device, wherein the pairwise direct communication flag is used to identify whether the relay communication devices can communicate directly when the relay communication devices are deployed at two candidate deployment locations; Based on the selection identifier of each candidate deployment location, the pairwise direct identification and the number of devices k Construct a fully connected constraint function; 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 connectivity constraint function to obtain a global optimization function.

5. The method according to claim 4, characterized in that The selection identifier based on each candidate deployment location, the pairwise direct access identifier and the number of devices Construct a fully connected constraint function, including: The total number is data packets are injected into each candidate deployment location, the number of incoming data packets at the candidate deployment location where the relay communication device is selected is 1 more than the number of outgoing data packets, and the number of data packets flowing in one direction between any two candidate deployment locations that can communicate directly after the relay communication device is deployed is at most Based on the selection identifier, pairwise direct identifier and number of devices of each candidate deployment location, Construct a fully connected constraint function.

6. The method according to claim 5, characterized in that According to the selection identifier of each candidate deployment location, the pairwise direct identification and the number of devices Construct a fully connected constraint function, including: The total number is k The data packets are injected into each candidate deployment location to construct the first connectivity constraint function; The number of incoming data packets at the candidate deployment location where the communication device is deployed is one more than the number of outgoing data packets, and a second connectivity constraint function is constructed according to the pairwise direct identification, the selection identification of each candidate deployment location, the number of randomly incoming data packets at each candidate deployment location, and the number of randomly outgoing data packets; The maximum number of packets that can flow in one direction is k as constraints, the number of inbound data packets and outbound data packets at each candidate deployment position are binary-encoded respectively, and a third connectivity constraint function is constructed based on the binary data at each position of the binary coding and the selection identifier at each candidate deployment position; The first full connectivity constraint function, the second full connectivity constraint function and the third full connectivity constraint function are weightedly summed to obtain the full connectivity constraint function.

7. The method according to claim 6, characterized in that: The first connectivity constraint function is: ; The second connectivity constraint function is: ; The third connectivity constraint function is ; in, To inject into The number of packets at candidate deployment locations, For The candidate deployment locations flow into the The number of packets at candidate deployment locations, For The candidate deployment locations flow into the The number of packets at candidate deployment locations, For the candidate deployment locations and The pairwise direct identification of candidate deployment locations, For the The selection identifier of the candidate deployment location, and for The binary data at each position after binary encoding, and for The binary data at each position after binary encoding.

8. The method according to any one of claims 1 to 3, characterized in that: The integrating the full coverage objective function and the quantity constraint function to obtain a global optimization function includes: The full coverage objective function and the quantity constraint function are weightedly summed according to predetermined weights to obtain the global optimization function.

9. A device for determining a deployment location of a relay communication device, characterized in that: include: Constraint determination unit, used to determine the number of devices of multiple terminal locations to be covered and the relay communication devices planned to be deployed and candidate deployment positions, and determining associated candidate positions corresponding to each terminal position based on a communication distance threshold of the relay communication device, the terminal position and the candidate deployment positions, wherein the associated candidate position is a candidate deployment position where the corresponding relay communication device covers the corresponding terminal position after the relay communication device is deployed; A function construction unit is used to construct a full coverage target function based on multiple terminal locations and corresponding selection identifiers of associated candidate locations so that the terminal locations covered by the relay communication device after actual deployment are at most the target, wherein the selection identifier is used to identify whether the corresponding associated candidate location is deployed with a relay communication device; based on the number of the devices and the selection identifiers of the candidate deployment locations, constructing a quantity constraint function; and integrating the full coverage objective function and the quantity constraint function to obtain a global optimization function; The quantity constraint function is used to constrain the number of selected candidate deployment locations to The global optimization function is a quadratic unconstrained binary function or a function equivalent to the quadratic unconstrained binary function; A quantum computing loading unit, used for loading a solution result obtained by optimizing and solving the global optimization function using a quantum computer; The deployment position determination unit is used to determine the deployment position of each relay communication device based on the solution result.

10. A system for determining the deployment location of relay communication equipment, 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, the memory stores a computer program, and when the computer program is loaded by the processor, the processor executes the method for determining the deployment position of relay communication equipment as described in any one of claims 1 to 7 to obtain an integrated objective function, and calls the second computer to solve the integrated objective function to obtain a solution result, and determines whether to deploy relay communication equipment at each candidate deployment position based on the solution result.

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