A method for optimizing frequency allocation to cells in a mobile communication network.

Abstract

The present invention relates to a computer-implemented method for optimizing the allocation of frequencies (f) to cells (C) of a mobile communication network (1). The cells (C) are distributed for communication in the mobile communication network (1). A set of unplanned cells (2) in the mobile communication network (1) is designated. For each unplanned cell (2), a set of frequencies (f) potentially assigned to this unplanned cell (2) is designated. A frequency interference probability (p) of selected cell pairs is calculated, each cell pair defining a relationship between the unplanned cell (2) and another cell (c) in the mobile communication network (1). Then, stress function terms are formulated, each term linking the calculated frequency interference probability (p) of the respective cell pair to a frequency relationship between the cells (2, c) of the respective cell pair. Using a quantum-inspired processor (7), an optimized allocation of frequencies (f) is determined, whereby for each unplanned cell (2), a subset of frequencies (f) is selected from the respective set of frequencies such that the stress function is minimized.

Description

Technical Field

[0001] The present invention relates to a computer-implemented method for optimizing the allocation of frequencies to cells of a mobile communication network, the cells being distributed for communication within the mobile communication network. The present invention also relates to a quantum-inspired processor configured to execute such a method, as well as a computer program implemented to execute such a method.

Background Art

[0002] The communication demand regarding traffic in mobile communication networks is constantly increasing. More and more devices and applications are pushing mobile communication to new peaks. Furthermore, the increasing demand for digitized and decentralized mobile work, as well as the increasing streaming demand, are other major factors contributing to this trend. With the requirement for increased network capacity, the increasing amount of data transported through mobile communication networks poses significant challenges to service providers.

[0003] Mobile communication networks, particularly mobile phone networks, such as 2G networks like GSM (registered trademark) or TETRA, include a topology of distributed "cells" for transmitting, receiving, and relaying mobile communication traffic and control data within the network via radio frequency communication. Each cell functions as a mobile communication node covering a certain area for the mobile communication network, thereby using one or more communication frequencies. Mobile communication participants, such as mobile phones and other cellular or wireless communication devices, connect to their respective cells for mobile communication with other participants within the communication network.

[0004] Depending on the location and characteristics of the cell's surroundings (e.g., urban or rural area), and the number and density of participating devices, cells have different sets of frequencies allocated to them to ensure they can handle the volume and amount of communication traffic. Frequencies are used for data or conversation transmission, control channels, etc. Therefore, to avoid congestion and degradation of the user experience in mobile communication networks, mobile phone providers are interested in making the best possible use of the available frequency spectrum.

[0005] The capacity of a mobile communication network can be increased by densely allocating frequencies from the mobile network operator's frequency spectrum to cells. However, this can have the drawback of neighboring cells' frequencies exceeding the required distance requirements. For example, if the frequencies of neighboring or related cells are equal or adjacent in the frequency spectrum, they will interfere with each other. This can lead to signal interference, degradation of communication quality, or communication failure.

[0006] One possible solution to the frequency allocation problem in cellular communication networks involves a two-step approach. The first step involves a preliminary allocation based on conditions that must be met (hard constraints), such as conditions for neighboring cells. The second step consists of sequentially iterative brute-force local optimization of unsaturated cells. For each such cell, the ring of directly neighboring cells is replanned. If a satisfactory solution is not found, the procedure can be repeated from the first step of ring generation using a wider ring, which is the union of all cells from the ring of directly adjacent cells. This ring-based cell acquisition procedure can be performed recursively. However, such conventional approaches have drawbacks: local optimization in unsaturated cells exacerbates interference in other cells in the network, or even leads to certain cells not being allocated the required number of frequencies or only being allocated insufficiently. Furthermore, such conventional approaches have the drawback that the optimization techniques applied so far rapidly reach their limits. [Overview of the project] [Problems that the invention aims to solve]

[0007] Therefore, the object of this disclosure is to provide an improved technique that allows for the optimized allocation of requested frequencies to cells in a mobile communications network, thereby avoiding intra-cell and inter-cell interference and improving optimized frequency use within the network. [Means for solving the problem]

[0008] This problem is solved by the method described in claim 1. Further implementations are described in the dependent claims and the following description.

[0009] This method is a computer-implemented procedure for optimizing the allocation of frequencies to cells in a mobile communications network, where cells are distributed for communication within the network.

[0010] Hereafter, "unplanned cells" will be referred to as mobile network cells defined by their antenna coverage area, and they must obtain one or more frequencies allocated to them.

[0011] This method includes the following steps: The step of specifying a set of unplanned cells in the mobile communication network; For each unplanned cell, the step of specifying the set of frequencies that could potentially be assigned to that unplanned cell; The steps include: calculating the frequency interference probability of selected cell pairs, wherein each cell pair defines the relationship between an unplanned cell and another cell in the mobile communication network; The step of formulating terms of the stress function, wherein each term relates the calculated frequency interference probability of each cell pair to the frequency relationship between the cells of that cell pair; - A step in determining an optimized frequency assignment for each unplanned cell by using a quantum-inspired processor, by selecting a subset of frequencies from the respective set of frequencies such that the stress function is minimized.

[0012] This method reliably addresses the problem of allocating frequencies to unplanned (or slated to be planned) cells within a communication network, thereby allowing most (or ideally all) unplanned cells to be allocated a necessary subset of selected frequencies according to their frequency demands. This allocation satisfies certain proximity conditions between adjacent cells. Optionally, overall frequency interference between cells within the mobile communication network is minimized.

[0013] By applying this method, for each given unplanned cell, the optimal allocation of a subset of frequencies from the set of frequencies potentially allocated to that cell can be selected. This selection is made automatically so as to minimize intra- and inter-cell frequency interference while still improving dense frequency utilization across the entire frequency spectrum available in the network.

[0014] Each subset of frequencies reflects a certain required number of frequencies per cell, or a certain required frequency in the frequency spectrum per cell. In other words, this means that for a particular required number of frequencies per cell, a subset of the cell's frequency spectrum, of its size (=density), must be determined. Here, frequency is defined, for example, as a discrete frequency band or channel in a GSM network. Here, cell is defined, for example, as area coverage of mobile communications by one or more antennas for wireless communication. The area coverage of cells to which an antenna extends can vary depending on the transmit power characteristics and placement of the antenna, as well as the surrounding topology.

[0015] An example mobile communications GSM network consists of thousands of GSM cells and tens or hundreds of frequency bands or channels, with a small number of channels (approximately <10, e.g., 2-6) used per cell.

[0016] In this context, frequency relationships describe a frequency-dependent expression that defines the (potential) relationship between one or more frequencies of one cell in each defined cell pair and one or more frequencies of the other cell. For example, a frequency relationship describes certain combinations of selected frequencies for both cells in a cell pair that may result in interference between the cells of the cell pair, each having a calculated frequency interference probability.

[0017] In this context, a cell pair describes the relationship between each cell from a set of unplanned cells and another cell in the mobile communications network. The other cell may be a cell from a set of unplanned cells to which a frequency has been assigned. Alternatively, the other cell may be a cell in the mobile communications network that is not in the selected set of unplanned cells. This could be, for example, a previously planned cell with a pre-assigned frequency, or a cell with a fixed frequency that may result from a previous assignment.

[0018] For example, a frequency assignment FA can be described as a mapping from a set of cells to a power set of frequencies (a set of subsets). The interference probability for each cell pair can be defined as a real-valued function of FA × FA that assigns a probability of disturbance to each cell pair of the "frequency-assigned cells". For frequency assignments as a whole, the interference probability can be defined (for example, as the sum of the pairwise interference probabilities for all off-diagonal pairs of FA × FA). This can be interpreted not as a probability itself (for example, the value can be > 1), but as the degree of disturbance in the entire network. This method can be implemented to search for the one with the smallest interference probability among all possible sets of frequency assignment FAs. Further constraints may be considered as optional, and these will be discussed further below.

[0019] The stress function (cost function) for the optimization problem described above can be constructed by calculating the frequency interference probabilities of selected cell pairs, formulating terms in the stress function, and thereby linking each term to the calculated frequency interference probabilities of each cell pair and the frequency relationships between the cells in that pair. Such a stress function imposes a penalty on frequency relationships that result in significant interference between cells. The stress function, and thus the underlying optimization problem, can be formulated as a quadratic unconstrained binary polynomial.

[0020] In this way, the optimized frequency assignment to unplanned cells is determined by selecting a subset of frequencies from each set of frequencies for each unplanned cell such that the stress function is minimized. The minimum value of the stress function is preferably a global minimum, but may also be a local minimum.

[0021] Therefore, this method has the technical effect and advantage of optimized cell coverage by the required number of frequencies for optimal frequency utilization per cell, depending on the cell's location and surrounding characteristics, as well as the required demand derived from the expected number and density of communication participants. Furthermore, interference can be minimized in the most optimal way. Thus, the user experience can be improved, and degradation of communication quality or communication failures can be avoided. In addition, the number of cells required can be reduced by optimized frequency allocation, thereby lowering the cost of cell installation and maintenance. Moreover, this method has the effect and advantage of determining / calculating the solution for optimized frequency allocation to unplanned cells very quickly, thereby enabling the possibility of very rapid replanning / reallocation of frequencies to each cell.

[0022] The underlying optimization problems can be extremely complex. This is not simply because the frequency of one cell can affect the frequencies of other cells. The problem is also complex due to the many practical constraints that must be considered. As more constraints are implemented, such problems become more complex and difficult to solve. This is problematic or difficult when a solution is needed quickly, for example, when frequency reallocation to certain cells is required in response to an unexpected network failure or during maintenance. However, even considering further practical constraints such as neighbor relations between cells, or dense clusters of cells within a so-called "sector," such as an urban area or several antennas on a rooftop within a city, such calculations become difficult to solve. The methods described herein demonstrate their strengths compared to conventional approaches as the underlying problem becomes more complex. In other words, for complex optimization problems that take into account the practical constraints described above, the methods described herein have a significant advantage over conventional techniques.

[0023] One advantage of the procedure described here is that it is an automated solution to optimization problems that do not involve manual adjustments such as the number of rings in conventional approaches as described above. Because more required frequencies can be reliably allocated to more cells in the network, the execution time is shorter and the quality of the solution is better compared to current techniques. The procedure described is a hybrid approach between a pre-processing step (formation of a set of unplanned cells and / or the respective sets of frequencies that should be potentially allocated to each unplanned cell) and a solution-finding step using a quantum-inspired processor.

[0024] The methods described herein utilize techniques inspired by quantum computing. The computation of an optimized solution to a stress function for determining an optimized frequency assignment to all cells in a set of unplanned cells is performed by a so-called quantum-inspired processor. In the context of this disclosure, a quantum-inspired processor is defined as a processor that solves a so-called "Ising model" or equivalent quadratic unconstrained binary problem. For example, this is a processor configured to solve an optimization problem by quantum annealing or quantum annealing emulation. Such a processor is based, for example, on conventional hardware technology, for example, on complementary metal-oxide-semiconductor (CMOS) technology. An example of such a quantum-inspired processor is Fujitsu's digital annealer. Alternatively, any other quantum processor may be used in the methods described herein, and in the future, technologies based on actual qubit technology may also be used. Further examples of such quantum-inspired processors include DWave's (e.g., 5000Q) quantum annealer, as well as quantum gate computers (IBM, Rigetti, OpenSuperQ, IonQ, or Honeywell) and their future successors, or alternative QC designs that utilize quantum optimization algorithms such as Quantum Approximate Optimization Algorithms (QAOA) or Variational Quantum Eigensolvers (VQE).

[0025] In other words, a quantum-inspired processor as defined herein is a processor that realizes the concept of minimizing a so-called quadratic unconstrained binary optimization (QUBO) function, whether it is a special processor based on classical technology, a quantum gate computer, or a quantum annealer.

[0026] In at least one implementation, this method further includes the following steps: The step of specifying frequency variables, wherein each frequency variable is associated with one cell in a mobile communication network and one frequency for that cell, The steps include: formulating the frequency relationship in the stress function term as a combination of selected frequency variables; The step involves using a quantum-inspired processor to calculate the stress function term and set the frequency variables so that the stress function is minimized.

[0027] The frequency for the cell to which each frequency variable is associated is, for example, one frequency from the set of frequencies that could potentially be assigned to the cell if the cell is not yet planned. Alternatively, such a frequency is, for example, one frequency already assigned to the cell if the cell is already planned. In the latter case, the value of the frequency variable is known a priori, depending on whether the frequency is assigned to the cell (value 1) or not (value 0). Such fixed variables do not need to be optimized by the quantum-idea processor, but they can lead to constraints for unplanned cells and thus may be part of some linear terms in the stress function. The actual number of frequency variables to be optimized is only those that come from unplanned cells. For example, for n cells, each having m frequencies, at least n × m frequency variables may be specified.

[0028] In this way, by setting frequency variables for each unplanned cell, the optimal frequency allocation can be calculated individually so that the stress function is minimized. This provides a highly flexible and elegant implementation of variable frequency allocation. Thus, different subsets of frequencies can be selected for different cells to avoid significant increases in inter-cell disturbances or interference within the network. Furthermore, dense frequency utilization can be achieved in an optimized manner across cells distributed throughout the network.

[0029] The formulation of frequency relations in the stress function term as a combination of selected frequency variables allows for the calculation of the optimal (minimum) stress function for all unplanned cells in a flexible manner, taking into account various frequency scenarios that can result in different disturbance interference behaviors between cells. In this way, an optimized selection of different subsets of frequencies for all unplanned cells can be achieved to minimize the cost function of the optimization problem described above. Thus, the influence of a selected frequency in one cell on other (potentially assigned) frequencies in other cells can be mitigated. This allows for a very high degree of freedom in frequency assignment, but is still very complex to solve. The optimized selection of each subset of frequencies for all unplanned cells is performed by the quantum-inspired processor, as described above.

[0030] In at least one implementation of this method, only calculated frequency interference probabilities below a predetermined threshold are considered in the stress function term. Therefore, optimization considers only interference probabilities that do not exceed a certain limit. This prevents certain frequencies from being assigned to certain cells (which could lead to unacceptable interference between these cells). For example, interference probabilities exceeding a threshold are avoided by a penalty term that is forced to zero for the optimal solution by a large prefactor.

[0031] In at least one implementation, this method is carried out taking into account frequency demand conditions such that each subset of frequencies allocated to an unplanned cell has a specified number of frequencies. One frequency is defined as the control channel frequency, and the other frequencies are defined as the traffic channel frequencies. In different implementations, the control channel frequency may be exactly one or at least one frequency for each cell. The number of frequencies allocated to each cell is pre-selected or predetermined, for example, taking into account the topology of the mobile communications network, the location and / or cellular characteristics of each cell (which depend on the power transmission performance and / or topology around the cell), and the number and density of participants who wish to dial that cell for mobile communications.

[0032] In accordance with frequency demand requirements, each cell must be allocated one control channel frequency. One control channel frequency is required for each cell and serves the purpose of providing a control channel for the cell, through which signaling and control information is exchanged between the cell and other cells, or between the cell and other network elements, terminals, or participants. When a mobile communication network is implemented according to the GSM standard, such a control channel frequency is, for example, a so-called broadcast channel (BCCH) frequency according to the GSM standard. In addition, other frequencies allocated to the cell according to frequency demand requirements are defined as traffic channel frequencies. These serve the purpose of providing one or more traffic channels for the cell, through which communication traffic such as data or voice is transmitted and exchanged between the cell and other cells, or between the cell and other network elements, terminals, or participants. When a mobile communication network is implemented according to the GSM standard, such a traffic channel frequency is, for example, a so-called transmission channel (TCH) frequency according to the GSM standard.

[0033] In mobile communication networks, control channels play a more fundamental role than traffic channels in controlling inter-cell communication. In some implementations, control channels are configured to transmit communication traffic in addition to signaling and control information. In other implementations, control channels are configured to transmit only signaling and control information.

[0034] In at least one implementation, this method is carried out considering a frequency combination distance condition, where for each unplanned cell, a forbidden frequency relation is defined such that the frequencies of a subset of the frequencies assigned to that cell have a certain frequency relation over a certain channel distance. For example, the channel distance is 2 or greater. Thus, the frequency combination distance condition has the effect of minimizing or completely avoiding intra-cell frequency interference, since the frequencies used by each cell must satisfy a certain channel distance from one another.

[0035] In at least one implementation, the method is performed considering cell proximity conditions, thereby defining forbidden frequency relationships between cells having determined (handover) proximity relationships. Neighborhood relationships are particularly relevant to neighboring cells (e.g., selected cell pairs or multiple cells) that provide overlap in their area coverage. This is important in terms of so-called handovers between neighboring cells. When a participant moves away from the antenna (center) of one cell during ongoing use of the mobile communication network, its transmission level decreases. As soon as the participant reaches the area of ​​the next neighboring cell, the participant must be handed over from the first cell to the second cell in order to continue using the mobile communication network. In such a scenario, if certain frequency relationships are used, inter-cell frequency interference between such neighboring cells increases beyond a critical limit. In such cases, the necessary calculations for the handover may be disturbed by the interference, leading to errors or failures, and the handover may fail, putting the participant at risk of facing communication interruptions within the mobile communication network. Therefore, to avoid significant frequency interference between these cells and, in particular, to prevent the potential drawback of handover failure, cell proximity conditions define forbidden frequency relationships between neighboring cells.

[0036] In at least one implementation, this method is carried out considering cell-sector conditions, where for cells having a determined sector relationship, a forbidden frequency relationship is defined between these cells. A cell-sector relationship relates, for example, to cells in a given densely populated installation area or location, where multiple antennas of different cells are installed in close proximity to each other, for example, on a rooftop, in a street, or in a densely populated urban area. Here, inter-cell interference can become significant due to the dense installation and overlap of different cells. Therefore, in a sector, forbidden frequency relationships between cells are crucial for preventing communication interference. Cell-sector conditions can be more restrictive than cell-neighbor conditions, meaning that cell-sector conditions define stricter rules regarding forbidden frequency relationships than cell-neighbor conditions.

[0037] In at least one implementation, the method is carried out considering frequency interference probability conditions, thereby defining forbidden frequency relationships between cell pairs in a mobile communications network where the frequency interference probability exceeds a predetermined threshold. In this way, inter-cell frequency interference can be kept within acceptable limits throughout the network, preventing adverse effects that could, in the worst case, lead to communication failure. Even if the overall frequency allocation results in an increase in interference probability between some cells in the network or within certain areas of the network after all unplanned cells have been planned and their subsets of frequencies have been allocated, local and global interference probabilities can still be kept within acceptable limits.

[0038] In at least one implementation of this method, the frequency relationships of the types described above distinguish between same-channel frequency relationships and adjacent-channel frequency relationships. Same-channel frequency relationships consider the frequencies of the same channel. Adjacent-channel frequency relationships consider the frequencies of adjacent channels. Adjacent channels mean directly adjacent frequency channels. For example, channels f3 and f4 are adjacent channels, while channels f3 and f5 are not.

[0039] In this implementation, same-channel frequencies and adjacent-channel frequencies are considered to contribute to interference within a single cell or between different cell pairs. Generally, same-channel frequencies have a high impact on interference because they use the same channel. When adjacent-channel frequencies are used, their impact on interference is not as high as that of same-channel frequencies. However, even between adjacent-channel frequencies, interference can be a problem because they have a small frequency distance (narrow frequency band spacing). For example, if two or more of the frequencies assigned to a single cell are same-channel frequencies or adjacent-channel frequencies, high interference will be a problem, significantly affecting the communication quality of that cell or causing communication failure. Similarly, if two cells with a certain relationship, such as the neighbor relationship or sector relationship described above, use two or more same-channel or adjacent-channel frequencies, interference with respect to these cells may increase significantly, which can also affect communication quality or cause communication failure. Furthermore, if two cells use two or more identical or adjacent channel frequencies, and the calculated frequency interference probability of at least one of these cells with respect to the other exceeds a predetermined threshold for one or more of these frequencies, the interference may increase beyond acceptable limits, potentially degrading communication quality.

[0040] Therefore, the assignment of same-channel or adjacent-channel frequencies within a cell to a cell pair that has a certain cell relationship or a certain frequency probability exceeding an acceptable limit should be avoided. Otherwise, if the interference is within an acceptable limit, the assignment of same-channel or adjacent-channel frequencies to a cell pair may be permitted.

[0041] The distinction between same-channel frequency relationships and adjacent-channel frequency relationships allows for different definitions of degrees of freedom in frequency allocation for different intra-cell and inter-cell frequency scenarios, depending on the degree of interference that can be tolerated. For example, different prohibited frequency relationships can be defined, as described above, for different frequency combination distance conditions, cell proximity conditions, cell-sector conditions, or frequency interference probability conditions, each including different combinations of same-channel frequency relationships and adjacent-channel frequency relationships.

[0042] In at least one implementation of this method, the frequency relationship distinguishes between control channel frequencies and traffic channel frequencies. Control channel frequencies and traffic channel frequencies may be defined as described above. Depending on the frequency allocation within or between cells, and depending on the relationships between each pair of cells, control channel frequencies and traffic channel frequencies may each have different importance with respect to the adverse effects caused by frequency interference. For example, if the control channel plays a more fundamental role than the traffic channel, the respective frequency relationships may be defined such that the optimized frequency allocation keeps the effects of interference on control channel frequencies within a smaller limit than the acceptable interference limit for traffic channel frequencies.

[0043] Therefore, the distinction between control channel frequencies and traffic channel frequencies in each frequency relationship allows for different definitions of degrees of freedom in frequency allocation for different intra-cell and inter-cell frequency scenarios, depending on the different importance of the control channel and traffic channel, and whether or not the degree of interference to both is acceptable. For example, different forbidden frequency relationships can be defined for different frequency combination distance conditions, cell proximity conditions, cell-sector conditions, or frequency interference probability conditions, each involving different combinations of control channel frequencies and traffic channel frequencies, as described above.

[0044] In at least one implementation that takes into account at least one of the frequency demand conditions, frequency combination distance conditions, cell proximity conditions, cell-sector conditions, or frequency interference probability conditions described above, the method further includes the following steps: The step of specifying frequency variables, wherein each frequency variable is associated with a cell in a mobile communication network and a frequency for that cell. The step of formulating the frequency relationship of each condition as a term of the selected frequency variable, The step involves using a quantum-inspired processor to calculate the frequency relationships of each condition and setting the frequency variables so that each frequency relationship becomes zero.

[0045] In this way, each condition can be flexibly modeled and optimized by setting the frequency variables using a quantum-inspired processor so that each condition is satisfied, if possible. This provides an elegant implementation of each condition as a (hard) constraint for the optimization problem formulated in the stress function. By considering such constraints in the formulation or calculation of the stress function, solutions to the optimization problem that violate the above conditions can be penalized. This makes it possible to find an appropriate optimized solution while considering the practical constraints of the actual network conditions of the communication network. The frequency variables used in formulating the frequency relationships of each condition may be, in part or in whole, the frequency variables used in formulating the terms of the stress function as described above.

[0046] In at least one implementation of this method, the calculated frequency interference probability for each cell pair distinguishes between the same-channel frequency interference probability and the adjacent-channel frequency interference probability. The same-channel frequency interference probability is calculated for the same channel frequencies between the cells of each cell pair, while the adjacent-channel frequency interference probability is calculated for the adjacent channel frequencies between the cells of each cell pair. In this way, different interference probabilities can be calculated and considered depending on the different interference characteristics of the same-channel or adjacent-channel frequencies, as described above in the context of same-channel or adjacent-channel frequency relationships.

[0047] In at least one implementation of this method, the stress function is formulated as a quadratic unconstrained binary optimization (QUBO) function. This QUBO function acts as an "input" for a quantum-inspired processor that solves this optimization problem for an optimized frequency assignment according to the method described above. Generally speaking, a QUBO is a quadratic polynomial in binary variables that are represented in the quantum-inspired processor as bits or qubits (hereinafter referred to as qubits [Q-bits]). In the context of the optimization problem of this disclosure, the QUBO function represents the sum of the potential contributions of the calculated frequency interference probabilities of each cell pair as a function of different qubits. Each qubit represents a selection of one frequency for one cell and can achieve a value of "0" or a value of "1" (or both with a certain probability). To solve the optimization problem, the quantum-inspired processor is run through different settings of different qubits to find such a solution that minimizes the optimization problem. Thus, the QUBO representation of the optimization problem has elegant properties with respect to the quantum-inspired computation applied here. For example, the frequency variables described above are formulated in the form of such qubits.

[0048] In at least one implementation of this method, the stress function and one or more of the conditions described above are combined into a global QUBO function. Thus, the global QUBO function can take into account one or more of the constraints described above and the optimization target. In this regard, one or more of the above constraints can be weighted within the QUBO function as soft constraints. In this regard, soft constraints are desirable but do not need to be satisfied if the cost function achieves a substantially better value. This has the advantage that the QUBO function can be fine-tuned to some extent by relying on optimizing the frequency assignment to cells across the entire network with respect to interference probabilities, or by focusing the optimization problem on either satisfying one or more of the above (soft) constraints.

[0049] In at least one implementation, the method further includes the following steps: The steps include: specifying a subset of unplanned cells from the set of unplanned cells, The steps include: performing the method for a subset of the unplanned cells; The steps include updating the cells within the subset to planned cells using their determined optimized frequency allocations, The step of sequentially and iteratively performing the method for the remaining unplanned cells in a set of unplanned cells until all unplanned cells in the set of unplanned cells have been processed.

[0050] In this way, a kind of decomposition strategy can be followed. This is advantageous, or even necessary, for handling the described method despite the limited hardware performance of quantum-inspired processors for solving optimization problems. As mentioned above, let us assume a number of necessary frequency variables to formulate the optimization problem and / or one or more of the additional conditions / constraints. Today, the capabilities of current quantum-inspired processors are still limited. Therefore, very complex optimization problems must be decomposed into several partial solutions that can be processed sequentially and iteratively to find the optimal solution. In each iterative step, one partial solution is found by the quantum-inspired processor.

[0051] For example, hardware limitations arise, as mentioned above, under consideration of the QUBO formulation of the optimization problem, and the number of bit variables (qubits) required to formulate the optimization problem depends, for example, on the number of cells and the number of frequencies (potentially allocated or already planned) to each cell. Thus, for n unplanned cells, each having m frequencies, at least n × m qubits must be specified. The number of qubits for already planned cells can be set to a constant. If, for each frequency, control channel and traffic channel characteristics are further distinguished in their respective frequency relationships, then n × m qubits must be specified for control channel frequency allocation and n × m qubits for traffic channel frequency allocation, so the number of required qubits increases further to 2 × n × m.

[0052] For example, considering a GSM communication network that may include thousands of GSM cells to be planned and tens or hundreds of frequency bands or channels, the number of qubits required can quickly reach hundreds of thousands of dimensions. Further considering that a typical quantum-inspired processor today can solve optimization problems on the order of 10,000 bit variables (qubits), the overall optimization problem must be solved sequentially and iteratively with the help of problem decomposition by the means described above.

[0053] According to an exemplary strategy for specifying / constructing a subset of unplanned frequencies, the following steps are performed: In the first step, candidate unplanned cells are sorted / prioritized in descending order according to the number of certain constraints or cell relationships with already planned cells. For example, one or more unplanned cells are considered first if they have the most cell relationships with already planned cells or if, due to constraints with already planned cells, they have the frequency allocation procedure that is most affected in terms of potential frequency interference. This has the advantage that one or more unplanned cells whose planning (frequency allocation) is most constrained by interference characteristics with already planned cells in the network are considered first. These unplanned cells are then included first in the subset of unplanned cells.

[0054] In the second step, further candidate unplanned cells are selected according to certain constraints or cell relationships relating to cells already included in the subset of unplanned cells. This has the advantage that in the second step, further unplanned cells that are most likely to be affected by the plan (frequency allocation) in terms of interference characteristics with respect to cells already present in the subset of unplanned cells are considered. These unplanned cells are also subsequently included in the subset of unplanned cells.

[0055] The first and / or second step is performed sequentially and iteratively until the upper limit of the frequency variables (qubits) that can be computed at once by the quantum-inspired processor is reached. The implementation method described above is then performed on the constructed subset of unplanned cells. After the frequency assignments have been computed for the subset of cells, these are updated to planned cells using their determined optimized frequency assignments. The method is then performed sequentially and iteratively for the remaining unplanned cells in the set of unplanned cells until all unplanned cells have been processed.

[0056] The problems described above are also solved by the quantum idea processor described in the attached claims. The quantum idea processor is configured to perform one or more steps of the method described above. According to an exemplary implementation, the quantum idea processor is a digital annealing processing unit. This unit may be specifically configured to perform quantum annealing or quantum annealing emulation as described above. The quantum idea processor may be any of the types described above.

[0057] Furthermore, the above-described problems can also be solved by a computer program, which, when executed by one or more processors, includes instructions causing each of the one or more processors to perform one or more steps of the above-described method. At least one of these processors is, for example, a quantum-inspired processor as described above. Other processors may be configured to process the above-described method, or the preparation or sequential iteration steps for said method, by executing the computer program. The computer program may be stored in a computer-readable storage medium.

[0058] Furthermore, the aforementioned challenges can also be addressed by a workplace for a network planner configured to verify the optimized frequency allocation determined by the method described above. Such a workplace would have, for example, verification means configured for (automated or semi-automated) verification of the optimized frequency allocation determined by the method described above. This would help the network planner verify the optimization results found by the method described above. The verification means may be implemented in software and / or hardware. For example, the workplace may communicate with or connect to a system equipped with a quantum-inspired processor that performs the method described above. The results can then be carried over to the workplace.

[0059] Furthermore, the aforementioned problems can also be solved by an interface device having one or more interfaces to cells in a mobile communication network, where the cells are distributed for communication within the mobile communication network, and the interface device is configured to automatically deploy the optimized frequency allocation determined by the method described above to the cells of the mobile communication network. In this way, the optimized frequency allocation determined by the method described above can be deployed (automatically or semi-automatically) to multiple cells in each mobile communication network. For example, the interface device may communicate with or connect to the aforementioned workplace or a system having a quantum-idea processor that performs the method described above. The results can then be taken into account by the interface device.

[0060] Furthermore, as a preparatory measure for one or more of the steps described above for a computer-implemented procedure, an interface can be implemented or used to read parameters from the communication network before each optimization and input such parameters into the computer-implemented optimization procedure described. In this way, continuous closed-loop optimization is possible. Parameters include, for example, the network configuration, adjacency information or relationships of each cell in the network, available frequencies in the network, and the expected frequency demand of each cell.

[0061] Any aspect, feature, effect, and measure described individually or in combination with others in the context of the methods described above can be applied individually or in combination with others to the described aspect, feature, effect, and measure in the context of the quantum-idea processor, computer program, workplace, and interface described above, or similar expressions can be found therein, and vice versa. [Brief explanation of the drawing]

[0062] The present invention will be further described below with reference to several drawings and with consideration to several implementations. [Figure 1] This shows an exemplary configuration of a communication network having multiple cells. [Figure 2] Figure 1 shows an example of frequency allocation to one cell in the network. [Figure 3] Figure a shows an exemplary schematic diagram of cell neighborhood conditions between exemplary pairs of cells in the network according to Figure 1. Figure b shows an exemplary schematic diagram of cell-sector conditions between exemplary cells in the network according to Figure 1. [Figure 4] Figure 1 shows an illustrative schematic diagram of the frequency interference probability conditions between exemplary pairs of network cells. [Figure 5] This section presents an exemplary mathematical formulation of a partial optimization problem. [Figure 6] This section presents an exemplary mathematical formulation of a partial optimization problem. [Figure 7] This section presents an exemplary mathematical formulation of a partial optimization problem. [Figure 8] This section presents an exemplary mathematical formulation of a partial optimization problem. [Figure 9] This section presents an exemplary mathematical formulation of a partial optimization problem. [Figure 10] This section presents an exemplary mathematical formulation of a partial optimization problem. [Figure 11] This section presents an exemplary mathematical formulation of a partial optimization problem. [Figure 12] A schematic diagram illustrating a frequency allocation algorithm is shown. [Modes for carrying out the invention]

[0063] Figure 1 shows an exemplary configuration of a portion of a communication network 1 having multiple cells. For example, communication network 1 is a GSM network and contains approximately 50,000 GSM cells. Cells c1 to c5 are illustrated in Figure 1. Each cell c1 to c5 has antennas a1 to a5. Thus, each cell c1 to c5 spans the coverage area of ​​mobile radio communication provided by its respective antennas a1 to a5. The area coverage of cells c1 to c5 spanned by antennas a1 to a5 can vary depending on the transmit power characteristics and installation of antennas a1 to a5, as well as the surrounding topology.

[0064] In the exemplary configuration of Figure 1, each of cells c2–c5 provides a communication node for participant 3 (mobile phones and other cellular or wireless communication devices) that can connect to each cell via wireless communication over certain frequencies. Each cell c2–c5 covers a certain area for the mobile communication network. Frequencies are discrete frequency bands or channels. For example, communication network 1 has 100 frequency channels, with approximately 3–5 channels used per cell. If hopping is used, the number of channels used per cell could be, for example, 1–12. In the exemplary configuration of Figure 1, each of cells c2–c5 uses a certain frequency assigned to each cell. The frequencies are assigned to cells c2–c5 in such a way that interference (signal disturbance) between cells c2–c5 (see arrows between cells c2–c5 in Figure 1) can be avoided or at least kept as low as possible.

[0065] In contrast to cells c2-c5, cell c1 in Figure 1 does not have a frequency already assigned to it. This means that cell c1 is an unplanned cell 2, while the other cells c2-c5 are each planned cells 6. However, cell c1 should be assigned some frequency so that interference (signal disturbance) between cell c1 and the other cells c2-c5 can be avoided, or at least kept as low as possible; therefore, cell c1 is also a planned cell.

[0066] In this regard, a frequency allocation procedure is performed in which an automated, computer-implemented frequency allocation is carried out to unplanned cells 2 within the communication network 1. The optimization goal of such frequency allocation is to minimize frequency interference between each cell of the communication network 1 as much as possible, while still allocating the required number of frequency channels to as many unplanned cells as possible.

[0067] The number of frequency channels required for each cell depends on the number of mobile communication participants 3 who dial into network 1 and attempt to use the communication topology of communication network 1. As shown in the exemplary configuration in Figure 1, cells c1 and c5 are associated with the minimum number of participants 3. For example, cells c1 and c5 are located in rural areas with no dense communication demand. Cell c4 requires more participants 3 than cells c1 and c5. For example, cell c4 is located in a town with low communication demand. In contrast, cells c2 and c3 each have the maximum number of participants 3 who wish to use communication network 1. Cells c2 and c3 are located in cities with dense urban infrastructure, for example. Thus, in the exemplary configuration in Figure 1, cells c1 and c5 require the minimum number of frequency channels, and cells c2 and c3 require the maximum number of frequency channels.

[0068] With respect to cell c1, frequencies are allocated to this unplanned cell 2 such that cell c1 causes as little frequency interference as possible to other cells in communication network 1, while still obtaining the number of channels necessary to provide mobile communication nodes to participants 3 requesting communication within cell c1. In other words, the optimization problem to be solved with respect to cell c1 is to determine the optimized frequency allocation to cell c1 (and potentially to other cells c2-c5, if frequency reallocation is considered for those cells c2-c5) within communication network 1. This means that for each cell c1 (and potentially other cells), a subset of frequencies is selected from each set of potential frequencies such that a mathematically formulated quadratic stress function (the core optimization problem) is minimized. This serves the purpose of selecting each subset of frequencies that meets the required frequency demands of each unplanned cell 2 in network 1, depending on the number of participants 3 per cell, and has the technical effect of minimizing intra-cell and / or inter-cell frequency interference, while nevertheless improving high-density frequency use across the entire available frequency spectrum within network 1. This has the effect of improving the user experience, avoiding degradation or failure of communication quality, and thereby still enabling as many participants as possible to join and use communication network 1. The subset of frequencies allocated to each unplanned cell can be selected from a predetermined set of frequencies that can potentially be allocated to unplanned cell 2, cell c1 (and potentially other cells) as an example in Figure 1.

[0069] To achieve the above-mentioned advantageous effects, a computer-implemented algorithmic method is implemented to optimize frequency allocation to unplanned cells 2 within the communication network 1. This will be further explained below. In addition to such a core optimization problem, additional suboptimal optimization problems are considered that represent conditions (constraints) that must be satisfied in the optimization process. These constraints take into account certain intra-cell and inter-cell frequency relationships, as well as certain cell relationships, that affect frequency allocation to each cell in terms of optimized minimization of frequency interference and satisfying the required frequency demand for each unplanned cell 2. Such conditions and constraints are described below.

[0070] Figure 2 shows an exemplary configuration of cell c3 to which a predetermined frequency has been assigned. Cell c3 is thereby applicable to other cells in the communication network 1 (particularly with respect to unplanned cell 2 to which a frequency should be assigned) and satisfies certain conditions that must also be met by those cells.

[0071] Figure 2 illustrates the distinction between two basic types of frequencies used within a cell in communication network 1. These two types of frequencies are distinguished as so-called broadcast channel (BCCH) frequencies and traffic channel (TCH) frequencies. In the exemplary configuration described here, one BCCH frequency must be assigned to each cell. The BCCH frequency is required for each cell and serves the purpose of providing a control channel for the cell, through which signaling and control information is exchanged between that cell and other cells, or between that cell and other network elements, terminals, or participants. The TCH frequency serves the purpose of providing each cell with one or more traffic channels, through which communication traffic such as data or voice is transmitted and exchanged between that cell and other cells, or between that cell and other network elements, terminals, or participants. The BCCH frequency may also be configured to transmit a portion of the communication traffic in the communication network in addition to signaling and control information. Alternatively, the BCCH frequency may be configured to transmit only signaling and control information. For example, signal transmission from different participating devices 3 within network 1 (see Figure 1) is configured as time-multiplexed transmission, where each participant using its respective frequency in its respective cell is assigned a time slot (e.g., within a range of milliseconds) during which the signaling assigned to that participant is transmitted.

[0072] As exemplarily shown for cell c3 in FIG. 2, each cell in communication network 1 must meet the specific frequency requirements of that respective cell. As described above, such frequency requirements depend on the number of participants 3 dialing into communication network 1 and may also depend on the topology of mobile communication network 1, the position of each cell and / or cellular characteristics (depending on the power transmission, performance and / or topology around the cell). The frequency requirements define the number of frequencies to be allocated to each cell. This number can be preselected or predetermined depending on the parameters described above. As exemplarily shown in FIG. 2, cell c3 has a frequency requirement of 4, which means that 4 frequencies are allocated to cell c3. As explained, one frequency is the BCCH frequency. In the example of FIG. 2, frequency f18 is allocated as the BCCH frequency of cell c3. Also, the remaining of the allocated frequencies, here 3 frequencies, are allocated as TCH frequencies. In the example of FIG. 2, frequencies f1, f3, and f99 are allocated to cell c3 as TCH frequencies.

[0073] As further shown in FIG. 2, a certain frequency variable x associated with one cell c in mobile communication network 1 and one frequency f for this cell c depends on whether the characteristic of the frequency is the BCCH frequency or the TCH frequency. cf is defined. Here, the frequency variable x cf takes the value "0" when the respective frequency f is not allocated to the respective cell c, and takes the value "1" when the respective frequency f is allocated to the respective cell c. As exemplarily shown in FIG. 2, since frequency f18 is allocated to cell c3 as the BCCH frequency, the frequency variable x c3f18 BCCH = 1. As described above, since frequencies f1, f3, f99 are allocated to cell c3 as TCH frequencies, the other three frequency variables are also x c3f1 TCH = 1, x c3f3 TCH = 1, x c3f99 TCH= is set to 1. All other frequency variables x for cell c3 are also set. c3f This is "0". Such a formulation using frequency variables is useful for the purpose of the mathematical formulation of the constraints described above and the core optimization problem described below.

[0074] In addition to the frequency demand condition, cell c3, as illustrated in Figure 2, must also satisfy the so-called frequency combination distance condition. This means that for each cell c, and especially for each unplanned cell 2, a prohibited frequency relation is defined such that each frequency f assigned to the cell has a certain channel distance. In a configuration as illustrated with respect to Figure 2, such channel distances may, for example, have a value ≥ 2. This means that each frequency f assigned to each cell must have a frequency distance ≥ 2 with respect to other frequencies f in the cell. This requirement describes the prohibition of so-called same-channel frequencies and adjacent-channel frequencies for each cell. Same-channel frequencies relate to frequencies f in the same channel, and adjacent-channel frequencies relate to frequencies f in adjacent channels. This serves the purpose of avoiding intra-cell interference between frequencies assigned to each cell. As illustrated with respect to cell c3 in Figure 2, the frequency combination distance condition is also satisfied, as all frequencies f1, f3, f18, and f99 have a distance of 2 or more.

[0075] Figure 3a illustrates additional conditions that must be met as additional constraints. Figure 3a shows the so-called cell neighborhood condition, which means that for cells c having a determined neighborhood relationship 4, a prohibited frequency relationship is defined between these cells c. As illustrated in Figure 3a, cells c1 and cN are considered to be in neighborhood 4. Thus, such neighborhood relationship 4 here describes neighboring cells that provide overlap in their area coverage. For cells c in neighborhood 4, certain combinations of frequencies are prohibited from being assigned to each cell pair. As illustrated in Figure 3a, the BCCH frequency of cell c1 must not be the same channel (co) or adjacent channel (adj) frequency of the BCCH frequency of cell cN. Furthermore, the BCCH frequency of cell c1 must not be the same channel or adjacent channel frequency of the TCH frequency of cell cN. Furthermore, the TCH frequency of cell c1 must not be the same channel or adjacent channel frequency of the BCCH frequency of cell cN. Last but not least important, the TCH frequency of cell c1 must not be the same channel frequency as the TCH frequency of cell cN. Nevertheless, in this last case, the TCH frequency of cell c1 can be an adjacent channel frequency to another TCH frequency of cell cN. In other words, the TCH frequencies of neighboring cells must not be the same channel frequency, but they can be adjacent channel frequencies.

[0076] Cell proximity conditions serve to ensure reliable handovers for certain mobile communication participants moving between the area coverages of neighboring cells. If a participant moves away from the antenna (center) of one cell, for example cell c1 in Figure 3a, during ongoing use of the mobile communication network, their transmission level decreases. As soon as the participant reaches the area of ​​the next neighboring cell, here for example cell cN, the participant must be handed over from cell c1 to cell cN. For this purpose, continuous signal measurements varying between the frequencies of cells c1 and cN are required. If interference between cells c1 and cN is high, such measurements will fail, and consequently, the handover may also fail, which would lead to a communication abort. The frequency relationships defined by the cell proximity conditions described above ensure that frequency interference between neighboring cells, the exemplary c1 and cN in Figure 3, is kept within acceptable limits so that handovers between neighboring cells are reliably guaranteed, or the risk of handover failure is kept below an acceptable limit.

[0077] Figure 3b illustrates a further condition called the cell-sector condition. Here, exemplarily, cells c1, c2, and c3 are located within a sector 5. Sector 5 defines, for example, a certain densely populated installation area or location, where, in this example, multiple antennas of different cells c1-c3 are installed in close proximity to each other, for example, on a rooftop, in a street, or in a densely populated urban area. The cell-sector condition can define more restrictive frequency relationships than the cell-neighbor condition, as explained with respect to Figure 3a above. This means that the cell-sector condition in Figure 3b can define stricter rules regarding prohibited frequency relationships between each cell (cell pair) than the cell-neighbor condition.

[0078] With respect to Figure 3b, as illustrated, the BCCH and TCH frequencies of each cell, in this case cell c1, must not be the same channel (co) frequency or adjacent channel (adj) frequency of any other cell, in this case cell c2 or c3. This means that all frequencies of cell c1 must have a frequency distance of 2 or more to all other frequencies of cells c2 and c3. Thus, the cell-sector condition serves to avoid inter-cell interference that can be significant due to the close placement and overlap of different cells within sector 5.

[0079] Figure 4 illustrates further conditions that must be met. These further conditions are the so-called frequency interference probability conditions, which define a forbidden frequency relationship between cells in a cell pair within a mobile communication network where the frequency interference probability p exceeds a predetermined threshold t. In the illustrative diagram of Figure 4, a cell pair consisting of cells c1 and cN is considered. Also, the co-channel frequency interference probability p is defined. c1cN co and adjacent channel frequency interference probability p c1cN adj These are distinguished. Furthermore, a threshold t for BCCH frequency is also defined. BCCH And the threshold t for the TCH frequency TCH The following are distinguished. The same-channel frequency interference probability and the adjacent-channel frequency interference probability, or both, are one or more of the thresholds, and p c1cN co ,p c1cN adj ≧t BCCH ,t TCH In this case, certain frequency relationships are prohibited. This is because one or both of the respective frequency interference probabilities are at their respective threshold t. BCCHDepending on whether the above is true, it means that the BCCH frequency of cell c1 must not be the BCCH frequency of cell cN with respect to either the same channel frequency or the adjacent channel frequency. Furthermore, the BCCH frequency of cell c1 is such that one or both of the respective frequency interference probabilities are at their respective threshold t BCCH Depending on whether the above is true, the TCH frequency of cell cN must not be the same as the TCH frequency of cell cN with respect to either the same channel frequency or the adjacent channel frequency. Similarly, the TCH frequency of cell c1 must be such that one or both of the respective frequency interference probabilities are at their respective thresholds t TCH Depending on whether the above is true, the BCCH frequency of cell cN must not be the same channel frequency or adjacent channel frequency. Finally, the TCH frequency of cell c1 is such that one or both of the respective frequency interference probabilities are at their respective thresholds t TCH Depending on whether the above conditions are met, the TCH frequency of cell cN must not be the same channel frequency or the adjacent channel frequency. Similar constraints apply to the swapped roles of c1 and cN and the interference probability p. cNc1 co ,p cNc1 adj Applies to this.

[0080] In this way, inter-cell frequency interference must be within acceptable limits between each cell pair, in this example between cell pair c1 and cN, to prevent adverse effects that could lead to communication failure in the worst case. The overall frequency allocation is based on the probability p of interference. co or p adj Even when increasing, after all unplanned cells have been planned and their respective frequencies have been assigned, the frequency interference probability between some cells, or within a certain region or area of ​​the network, will not exceed a predetermined threshold t for each BCCH and TCH frequency. BCCH and t TCH Since the above is avoided, the interference probability can still be kept within an acceptable limit.

[0081] Figures 5 to 9 show exemplary mathematical formulations of the conditions / constraints described above. In this regard, the following nomenclature is defined. F = {0, 1, ..., M}: Set of all frequencies C={0,1,…,N}: The set of all cells SEC⊂C×C: Tuple of cells having a sector relationship NB⊂C×C: A tuple of cells that have a neighboring relationship. ∀c∈C and ∀f∈F\F(c): x as a fixed bit cf BCCH =x cf TCH = 0.

[0082] The mathematical formulations in Figures 5 to 9 are expressed as the so-called Hamiltonian function, abbreviated as Hamiltonian, and represent the QUBO formulation of qubits as described above.

[0083] The mathematical formulation in Figure 5 formulates the frequency demand condition as described above with respect to Figure 2. The mathematical formulation in Figure 5 takes the value "0" or the value "1" (or both with a certain probability) and is represented as a bit (or qubit as used below) in the quantum-inspired processor, and is a binary frequency variable x for each BCCH and TCH frequency. cf BCCH and x cf TCH It is formulated as a summed term that includes . Thus, the binary frequency variable shown in Figure 5 is constructed as described above with reference to Figure 2. For each cell c in the set of all cells C in network 1, and for each frequency f of the frequency F(c) that is potentially assigned to each cell c, the respective qubit can be set. If each frequency is a BCCH frequency, then the qubit x cf BCCH Each is set. If, instead, each frequency is the TCH frequency, each qubit x cf TCH Each of these is set. cf BCCH , xcf TCH This is set to a value of "1" if each frequency is set accordingly. Otherwise, each qubit x cf BCCH , x cf TCH It is set to the value "0".

[0084] Considering the Hamiltonian formulation in Figure 5, the Hamiltonian in the optimal solution must be equal to zero. This is satisfied only if the two terms E1 and E2 are both zero. This means that for every cell c in the set C, exactly one frequency f from the potential frequencies F(c) is assigned as the BCCH frequency, and the number of required frequencies w c This holds true when another frequency f is assigned as the TCH frequency. The mathematical formulation shown in Figure 5 allows all unplanned cells, favorably all cells in Network 1, to satisfy the frequency demand conditions described above, with reference to Figure 2.

[0085] The mathematical formulation in Figure 6, as described above with reference to Figure 2, formulates the frequency-distance combination condition. This Hamiltonian is given by the qubit x cf BCCH , x cf TCH The terms relating to the frequency relationship are summed up. Here, different combinations of BCCH or TCH frequencies are distinguished for each identical channel (co) or adjacent channel (adj) frequency. Identical channel frequencies are considered by combinations of qubits relating to the same frequency f, and adjacent channel frequencies are considered by combinations of qubits relating to frequencies f and f+1, respectively. Thus, the mathematical formulation in Figure 6 takes into account the condition that for all unplanned cells c in the set C and all frequencies f in the potential frequency F(c), each frequency f in each cell c must have a distance of ≥ 2 from other frequencies f, as described above with reference to Figure 2.

[0086] Considering the formulation of the Hamiltonian in Figure 6, the Hamiltonian in the optimal solution must be zero. This is only satisfied if each frequency relation (combination of product terms) obtains the value "0". This means that at least one of the qubits in each product term must obtain the value "0". If, instead, both qubits in each product term obtain the value "1", the condition in Figure 6 is not satisfied, and the frequency combination distance condition cannot be met.

[0087] The mathematical formulation of the Hamiltonian shown in Figure 7 represents the cell neighborhood conditions described above with reference to Figure 3a. In Figure 7, all cell pairs c1, c2 that have neighborhood relation NB where at least one of cells c1, c2 is unplanned are considered. For example, here neighboring cells c1, c2 as described in Figure 3a N This is also included.

[0088] The Hamiltonian in Figure 7 is formulated as a summation term over all identical channel (co) frequencies f between cell pairs c1 and c2, and all adjacent channel (adj) frequencies f, f+1 or f, f-1 between cell pairs c1 and c2, considering each combination of qubits with respect to BCCH and / or TCH frequency combinations. Thus, the Hamiltonian according to Figure 7 represents the mathematical formulation of the forbidden frequency relation as described for Figure 3a. Considering the formulation of the Hamiltonian in Figure 7, the Hamiltonian in the optimal solution must be equal to zero. Here, the same thing as described for Figure 6 applies. The Hamiltonian in Figure 7 is equal to zero only if all product terms for each combination of qubits achieve the value "0". If the frequencies f are assigned such that at least one product term for each combination of qubits obtains the value "1", the condition in Figure 7 is not met.

[0089] A similar explanation to that for Figure 7 applies to the mathematical formulations in Figures 8 and 9.

[0090] Figure 8 represents the cell-sector state described above with reference to Figure 3b. In Figure 8, all cell pairs c1, c2 that have a sector relation SEC are considered. Cells c1, c2 in Figure 8 represent all cell pairs in the network that have a sector relation where at least one of cells c1, c2 is unplanned. For example, this also includes the cell pairs c1, c2 and c1, c3 of sector 5 described with reference to Figure 3b. For each of all cell pairs c1, c2, the Hamiltonian in Figure 8 is equal to zero only if all product terms of each qubit combination achieve the value "0". The condition in Figure 8 is not met if the frequency f is assigned such that at least one product term of each qubit combination obtains the value "1".

[0091] Figure 9 shows the frequency interference probability conditions described above, with reference to Figure 4. In Figure 9, each condition p c1c2 co ,p c1c2 adj ≧t BCCH ,t TCH All cell pairs c1, c2 having such a probabilistic relationship are considered. Cells c1, c2 in Figure 9 represent all cell pairs having such a probabilistic relationship in a network where at least one of cells c1, c2 is unplanned. For example, here neighboring cells c1, c2 as described for Figure 4 N This is also included. The Hamiltonian in Figure 9 is equal to zero only if all product terms of each qubit combination achieve the value "0". The condition in Figure 9 is not met if the frequency f is assigned such that at least one product term of each qubit combination obtains the value "1".

[0092] The mathematical formulation of the Hamiltonian shown in Figure 10 calculates the frequency interference probability p for the same channel (co) frequency and adjacent channel (adj) frequency that are potentially simultaneously assigned between each cell pair c1 and c2. c1c2 co ,p c1c2 adjrepresents the core optimization problem taking into account. Cells c1 and c2 in FIG. 10 represent all cell pairs c1, c2 having such a probability relationship in a network where at least one of cells c1 and c2 is unplanned. Thus, in the core optimization problem of FIG. 10, for each BCCH and TCH frequency, conditions p c1c2 co , p c1c2 adj <t BCCH , t TCH only the frequency interference probability having is considered. The core optimization problem is to minimize the Hamiltonian according to FIG. 10 in order to find an optimized frequency allocation for all cell pairs c1, c2 such that the frequency interference between cells is minimized.

[0093] The Hamiltonian according to FIG. 10 contains summed terms, and each term multiplies the frequency interference probability p c1c2 co , p c1c2 adj by different combinations of different qubits set as described above. In other words, the Hamiltonian of FIG. 10 is formulated as a stress function that penalizes the frequency relationship between each cell pair leading to the frequency interference between each cell having the calculated frequency interference probability. If each combination of qubits in the respective frequency relationship of the terms according to FIG. 10 has the value "1", this frequency combination is multiplied by the respective frequency interference probabilities p c1c2 co , p c1c2 adj and thus contributes to the frequency interference with such a probability. Thus, the mathematical formulation of FIG. 10 sums all the contributions of the respective frequency interference probabilities p c1c2 co , p c1c2 adj for all combinations of all co-channel (co) frequencies and adjacent channel (adj) frequencies of all BCCH and TCH frequencies that are potentially used simultaneously between each cell pair of unplanned cells.

[0094] The Hamiltonian in Figure 10 is generally optimized for all (partially) unplanned cell pairs in the network by a quantum-inspired processor that proceeds through various setting values ​​for each qubit, thereby calculating each result of the Hamiltonian. The goal of doing so is to find the minimum value of the Hamiltonian for each setting value of the qubit. As soon as each minimum value of the Hamiltonian in Figure 10 is found, the respective qubit values ​​leading to this minimum are stored, ultimately defining the optimal frequency assignment for cell interference. Other frequencies that are not candidates for same-channel or adjacent-channel frequency interference between cell pairs c1 and c2 in Figure 10 (and therefore have a frequency distance ≥ 2 to all other cells) can be assigned to each cell without further consideration in the Hamiltonian in Figure 10. It is assumed that these frequencies have sufficiently high frequency (channel) distances so that they do not play a significant role and contribution to the interference between cells.

[0095] Figure 11 shows the final global QUBO formulation of the overall optimization problem. Here, the suboptimal optimization problems from Figures 5-10 are multiplied by their respective weighting factors A and B and summed up to the global optimization problem. This global optimization problem is finally processed by applying the computer implementation algorithm within the quantum-inspired processor. In this regard, the Hamiltonian from Figure 10 is minimized, thereby taking into account the further optimization constraints formulated in the Hamiltonian from Figures 5-9.

[0096] According to FIG. 11, the core optimization problem of FIG. 10 is multiplied by the weighting factor A, and the sum of other optimization constraint conditions according to FIGS. 5 to 9 is multiplied by the weighting factor B. Using these weighting factors A and B, different weights and focuses can be set for different sub-optimization problems. For example, when factor B is larger than factor A, the focus is placed on satisfying the optimization constraint conditions as described with respect to FIGS. 5 to FIG. 9. As factor A increases and approaches factor B, a greater weight is applied to the optimization of interference by the optimization problem formulated in FIG. 10 described above.

[0097] In certain implementations, it is allowed for certain constraint conditions to be violated. For example, the constraint condition of FIG. 5 (frequency demand condition) may be violated for certain cells. However, even in these scenarios, if there is a cell for which at least one BCCH frequency is not allocated, the result of the algorithm calculation of the method described above may be invalid. In such a case, the constraint condition of FIG. 5 can be split into different constraint conditions that separately follow items E1 and E2 of FIG. 5. In this case, item E1 must be satisfied for all cells to be planned, while item E2 may be violated for certain cells. For example, item E1 can be weighted by factor B, and item E2 can be weighted by factor C, where A < C < B is defined. In this way, item E2 defines a soft constraint condition, and item E1 defines a hard constraint condition. In this manner, certain constraint conditions can be flexibly modeled for the actual scenarios and use cases of network planning.

[0098] Figure 12 shows an exemplary schematic diagram of an algorithm that implements the approach described above. Figure 12 shows the processing of the method steps and procedures described above, taking into account frequency allocation to unplanned cells 2, which are processed in a decomposed form. This has the advantage of processing the method described despite the limited hardware performance of the quantum idea processor 7 for solving the optimization problem, as described above with respect to Figures 5-11, or even is necessary for that purpose. This optimization problem (see Figure 11 in particular) is very complex and must be decomposed into several parts that can be processed sequentially and iteratively to find the optimal solution. In each iterative step, one partial solution is found by the quantum idea processor 7.

[0099] The process begins with a certain number of planned cells 6 and unplanned cells 2. If there are no planned cells 6, the process begins with one randomly selected unplanned cell 2. However, if there are one or more already planned cells 6, the process begins with a so-called planned cell criterion in order to decompose the optimization problem. This means that in the first step, candidate unplanned cells 2 are sorted / prioritized according to certain constraints or cell relationships with respect to the already planned cells 6. For example, the unplanned cells 2 that have the most cell relationships with the already planned cells 6 are considered first. Their frequency allocation procedure is most affected by the constraints of the already planned cells 6 in terms of potential frequency interference. These unplanned cells 2 are then initially included in a subset 8 of unplanned cells 2.

[0100] In the second step, the process continues using the so-called outside-in criterion. For this purpose, further candidates for unplanned cells 2 that are not yet included in subset 8 of unplanned cells 2 are selected according to certain constraints or cell relations with respect to the cells already included in subset 8 of unplanned cells 2. These unplanned cells 2 are then also included in subset 8. This second step is repeated until the upper limit of the frequency variables (qubits) that can be computed at once by the quantum idea processor 7 is reached. The constructed subset 8 is then input into an algorithmic procedure within the quantum idea processor 7. For example, the quantum idea processor 7 shown in Figure 12 is configured to solve an optimization problem by quantum annealing emulation. The quantum idea processor 7 applies the mathematical formulation of the global optimization problem shown in Figure 11. The quantum idea processor 7 then computes the optimized solution of the global optimization problem shown in Figure 11 for subset 8.

[0101] After the algorithmic procedure is complete, the final calculated minimum value of the global optimization problem shown in Figure 11 is then output from the quantum-idea processor 7 for each subset 8. The cells in subset 8 are updated to planned cells 6 with the determined optimized frequency assignments. If any unplanned cells 2 remain, the procedure is sequentially iterated for the remaining set of unplanned cells 2 until all cells are planned and processed. In this case, the global solution for all cells in the communication network is stored. The algorithm then terminates.

[0102] Therefore, by applying the computer-implemented algorithmic procedure shown in Figure 12, based on the above implementation and description relating to Figures 1 to 11, an optimized frequency allocation can be provided for all cells c in the communication network 1. This process is a hybrid method between a preprocessing step (formation of each subset 8) and a solution step using a quantum-inspired processor 7. The procedure in Figure 12 considers highly colliding unplanned cells 2 early in the process to allow for the maximum degree of freedom in optimization. This further improves the quality of the solution compared to existing technology approaches.

[0103] The formulation of the optimization problem as a QUBO representation has elegant properties with respect to quantum-inspired computation applied here within processor 7. Today, quantum-inspired computation still has significant limitations. However, as computer science increasingly moves towards quantum computing, the approach described herein may be further improved and developed in the future. For example, as quantum computing becomes increasingly applicable to the increasing size of the underlying optimization problem, decomposition strategies such as those described with reference to Figure 12 can be increasingly reduced. This means that the optimization problem can increasingly be handled and computed as a whole without the problem decomposition steps and sequential iterations. Furthermore, as quantum computing becomes increasingly applicable, the approach described herein can take into account an increasingly increasing number of qubits, increasingly complex optimization problems, and / or increasingly more nonlinear constraints.

[0104] The approach described here is primarily applicable to allocating a requested number of frequencies to cells within a mobile communications network. However, this solution can also be used for planning circuit-isolated mobile phone networks, planning code-isolated mobile phone networks, or for allocation problems such as optimizing allocation during operating time to respond to workload spikes.

[0105] The embodiments shown and described herein are for illustrative purposes only. [Explanation of symbols]

[0106] 1. Communication Network 2. Unplanned cell 3 participants 4 Neighborhood 5 sectors 6. Planned cell 7 Quantum-Inspired Processors 8. Subset of unplanned cells a1~aN Antenna C: The set of all cells c, c1~cN Single cell co-channel adj: adjacent channel BCCH broadcast channel frequencies TCH Traffic Channel Frequency NB Neighborhood Relationship SEC sector E1, E2 Frequency Demand Conditions f frequency p-frequency interference probability t threshold w is the number of required frequencies x frequency variable

Claims

1. A computer-implemented method for optimizing the allocation of frequencies to cells in a mobile communications network, wherein the cells are distributed for communication within the mobile communications network, and the method is: The steps include: specifying a set of unplanned cells in the aforementioned mobile communication network; For each unplanned cell, the step is to specify the set of frequencies that are potentially allocated to that unplanned cell; A step of calculating the frequency interference probability of selected cell pairs, wherein each cell pair defines the relationship between an unplanned cell and another cell in the mobile communication network; The step of formulating terms of the stress function, wherein each term relates the calculated frequency interference probability of each cell pair to the frequency relationship between the cells of that cell pair; The process includes the step of determining an optimized frequency assignment by selecting a subset of frequencies from the respective sets of frequencies for each unplanned cell, such that the stress function is minimized, using a quantum-inspired processor. The method in question is: A step of specifying frequency variables, each frequency variable being associated with a cell in the mobile communication network and a frequency of that cell; The steps include: formulating the frequency relationship in the stress function term as a combination of selected frequency variables; The quantum-inspired processor is used to calculate the stress function term and set the frequency variable such that the stress function is minimized. Further including, method.

2. The method according to claim 1, wherein in the stress function term, only calculated frequency interference probabilities below a predetermined threshold are considered.

3. The method according to claim 1, wherein the method is performed taking into account a frequency demand condition that each subset of frequencies assigned to an unplanned cell has a specified number of frequencies, one of which is defined as a control channel frequency and the other frequencies are defined as traffic channel frequencies.

4. The method according to claim 1, wherein the method is performed taking into consideration a frequency combination distance condition such that, for each unplanned cell, the frequencies of the subset of frequencies assigned to that cell have a frequency relationship having a channel distance.

5. The method according to claim 1, wherein the method is performed considering a cell neighborhood condition in which a forbidden frequency relationship is defined between cells having a determined neighborhood relationship.

6. The method according to claim 1, wherein the method is performed taking into consideration a frequency interference probability condition in which a forbidden frequency relationship is defined between cells of a cell pair whose frequency interference probability exceeds a predetermined threshold.

7. The aforementioned frequency relationships distinguish between same-channel frequency relationships and adjacent-channel frequency relationships. The same-channel frequency relationship is formulated with respect to the frequencies of the same channel, and the adjacent-channel frequency relationship is formulated with respect to the frequencies of adjacent channels. The method according to claim 1.

8. The method according to claim 1, wherein the frequency relationship distinguishes between the control channel frequency and the traffic channel frequency.

9. A step of specifying frequency variables, each frequency variable being associated with a cell in the mobile communication network and a frequency for that cell; The steps are: formulating the frequency relationships of each condition as terms of the selected frequency variable; The quantum-inspired processor further includes the steps of calculating the frequency relationships of each condition and setting the frequency variables such that each frequency relationship becomes zero. The method according to claim 4.

10. The calculated frequency interference probability for each cell pair distinguishes between same-channel frequency interference probability and adjacent-channel frequency interference probability. The same-channel frequency interference probability is calculated for the frequencies of the same channel between the cells of each cell pair, and the adjacent-channel frequency interference probability is calculated for the frequencies of the adjacent channels between the cells of each cell pair. The method according to claim 1.

11. The steps include: specifying a subset of unplanned cells from the aforementioned set of unplanned cells; The steps include: performing the method with respect to the subset of unplanned cells; The steps include updating the cells in the subset to planned cells with determined optimized frequency assignments; The further step includes sequentially iterating the method for the remaining unplanned cells in the set of unplanned cells until all unplanned cells in the set of unplanned cells have been processed. The method according to claim 1.

12. A quantum-inspired processor, particularly a digital annealing processing unit or a quantum annealing processing unit, configured to perform the steps of the method according to claim 1.

13. A computer program having instructions, wherein, when the program is executed by one or more processors, the instructions cause each of the one or more processors to perform the method according to claim 1.

14. An interface device comprising one or more interfaces to cells of a mobile communications network, wherein the cells are distributed for communication within the mobile communications network, and the interface device is configured to automatically deploy an optimized frequency allocation determined by the method of claim 1 to the cells of the mobile communications network.

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