Power Domain Interference Map Estimation Method in Cellular Network Multi-Cell Scenario

Abstract

The present application discloses a method for estimating a power domain interference graph in a multi-cell scenario of a cellular network. The method includes: constructing a transmission power matrix based on the power allocation of each base station to multiple resource blocks within a preset time period; creating an objective function based on maximizing the energy efficiency and minimizing the condition number of the transmission power matrix, and constructing an optimization model with the allocation of resource blocks, the transmission power range, and the full rank of the transmission power matrix as the constraint conditions of the objective function; decomposing the optimization model to obtain a first sub-optimization model and a second sub-optimization model, solving the first sub-optimization model, and based on the solution of the first sub-optimization model, solving the second sub-optimization model to obtain an optimal transmission power matrix; calculating the equivalent channel vector of the user based on the optimal transmission power matrix and the received power, expanding the original time domain / frequency domain to the power domain, and fundamentally solving the problems of large measurement overhead and high time synchronization accuracy requirements of traditional methods.

Description

Technical Field

[0001] The present invention relates to the technical field of network interference, and particularly relates to a method for estimating a power domain interference graph in a multi-cell scenario of a cellular network. Background Art

[0002] A multi-cell cellular network is a form of organization of a mobile communication network. It divides the entire service area into multiple cells and sets up a base station (or a subsystem of the base station) in each cell to achieve wide-range mobile communication coverage. With the rapid increase in the number of mobile device users, the cellular network tends to be densely deployed to meet the users' demands for network traffic and speed. However, the dense deployment of base stations will inevitably bring inter-cell interference, which has a significant negative impact on network capacity. Therefore, the communication system must effectively manage this interference and optimize resource allocation in combination with the interference situation of the actual system to maintain network performance.

[0003] Traditional interference management and resource scheduling methods usually assume that inter-cell interference is known and ignore the effective and accurate measurement of interference. However, multi-cell cellular networks are characterized by dynamic changes, large scale, and high complexity, making the existing interference measurement methods face problems such as large measurement overhead, insufficient accuracy, and lack of flexibility in practical applications.

[0004] The Channel State Information Interference Measurement (CSI-IM) method introduced in the 4G Long Term Evolution (LTE) standard is an interference measurement technology widely used in multi-cell cellular networks. When a base station (BS) to be measured sends information to a user equipment (UE) on a certain resource element (RE), assuming that all base stations have achieved ideal time synchronization, other base stations should send zero information on the corresponding time-frequency resource elements. In this way, the signal strength received by the UE on this specific RE will be equal to the inter-cell interference of the base station to be measured on the UE. The signal carried by the base station to be measured on the RE is called CSI-RS, and the mechanism of adjacent cell base stations sending zero information to cooperate in measurement is called CSI-IM. In a multi-cell scenario, the base stations in each cell take turns to send CSI-RS through scheduling, while ensuring that the base stations in the remaining cells send zero information on the corresponding REs, so as to measure the inter-cell interference of all base stations on the UE.

[0005] The CSI-IM based measurement method relies on the scheduling of all neighboring base stations to ensure that during the measurement phase, when the base station to be measured sends CSI-RS, all other remaining base stations send zero information on the corresponding REs, resulting in high measurement overhead. Moreover, it requires strict symbol-level time synchronization between base stations. However, in the 5G system, the symbol time is often in nanosecond (ns) accuracy, which makes the accuracy of time synchronization a challenge that is difficult to overcome for this method. Summary of the Invention

[0006] In view of this, the present invention provides a power domain interference graph estimation method in a multi-cell scenario of a cellular network, mainly aiming to solve the problems of high requirements for time synchronization accuracy and large measurement overhead in the existing interference measurement methods for cellular networks of cells.

[0007] According to one aspect of the present application, there is provided a power domain interference graph estimation method in a multi-cell scenario of a cellular network, the method comprising:

[0008] Obtain the power allocation situations of each base station to multiple resource blocks during a preset time period, and construct a transmission power matrix based on the power allocation situations of each base station to multiple resource blocks during the preset time period;

[0009] Create an objective function based on the maximum energy efficiency and minimum condition number of the transmission power matrix, and use the allocation situation of the resource blocks, the transmission power range, the signal-to-noise ratio range, and the full rank of the transmission power matrix as the constraint conditions of the objective function to construct an optimization model;

[0010] Decompose the optimization model to obtain a first sub-optimization model and a second sub-optimization model, solve the first sub-optimization model, and based on the solution of the first sub-optimization model, solve the second sub-optimization model to obtain an optimal transmission power matrix;

[0011] Obtain the received power corresponding to the power allocated by the user to multiple resource blocks, and calculate the equivalent channel vector of the user based on the optimal transmission power matrix, the received power, and the Gaussian white noise power vector.

[0012] Optionally, the constructing a transmission power matrix based on the power allocation situations of each base station to multiple resource blocks during a preset time period includes:

[0013] Obtain the power allocation situations of each base station to multiple resource blocks with different frequencies during a preset time period, and divide a preset number of adjacent resource elements in the same frequency into a resource sub-block according to the time sequence;

[0014] Obtain the transmission power of each resource element in each resource sub-block, and calculate the average transmission power corresponding to each resource sub-block based on the transmission power of the resource elements in each resource sub-block;

[0015] Generate a transmission power matrix based on the frequency of each resource block and the time and average transmission power corresponding to the included resource sub-blocks.

[0016] Optionally, the optimization model is:

[0017]

[0018] where E(P) represents the energy efficiency of the transmission power matrix P, k(P k [d]) represents the condition number of the transmission power matrix formed by the d-th resource block of the k-th base station and its adjacent resource blocks, δ k,z [d][l] represents whether the k-th base station allocates the l-th resource sub-block of the d-th resource block to the z-th user in the cell where this base station is located, SINR represents the signal-to-noise ratio of the user, γ k,z represents the lower bound of the SINR of the z-th user in the cell where the k-th base station is located, represents the transmission power of the z-th user in the cell where the k-th base station is located, represents the upper bound of the total power of the k-th base station, P k [d] represents the transmission power of the d-th resource block of the k-th base station, N RB represents the number of RBs generating interference, u k represents the number of users in the cell where the k base stations are located, RB k represents the set of RBs of the k-th base station, and K represents the total number of base stations.

[0019] Optionally, the first sub-optimization model is:

[0020]

[0021]

[0022] The second sub-optimization model is:

[0023]

[0024] where α represents the decomposition coefficient.

[0025] Optionally, solving the first sub-optimization model includes:

[0026] Based on the fractional form of the energy efficiency, perform a form transformation on the objective function in the first sub-optimization model to obtain a first intermediate objective function;

[0027] Perform relaxation transformation and convex-concave transformation on the first intermediate objective function in sequence to obtain a second intermediate objective function;

[0028] Perform linear decomposition on the signal-to-noise ratio range constraint condition in the first sub-optimization model to obtain a first signal-to-noise ratio linear constraint and a second signal-to-noise ratio linear constraint, and perform convex transformation on the second signal-to-noise ratio linear constraint to obtain a third signal-to-noise ratio linear constraint;

[0029] Use the second intermediate objective function, the allocation situation of resource blocks, the transmission power range, the first signal-to-noise ratio linear constraint, and the third signal-to-noise ratio linear constraint as the transformation model of the first sub-optimization model, and solve the transformation model of the first sub-optimization model.

[0030] Optionally, solving the second sub-optimization model based on the solution of the first sub-optimization model includes:

[0031] Decompose the second sub-optimization model to obtain a first initial optimization model and a second initial optimization model;

[0032] Based on the solution of the first sub-optimization model, convert the first initial optimization model into a first intermediate optimization model;

[0033] Convert the full-rank constraint condition of the transmission power matrix in the first intermediate optimization model into a positive definite constraint condition to obtain a third sub-optimization model, and solve the third sub-optimization model;

[0034] Based on the solution of the third sub-optimization model, add a ratio constraint condition of the maximum eigenvalue and the minimum eigenvalue of the transmission power matrix to the second initial optimization model to obtain a second intermediate optimization model;

[0035] And perform scaling processing on the constraint conditions in the second intermediate optimization model to obtain a fourth sub-optimization model, and solve the fourth sub-optimization model.

[0036] Optionally, the equivalent channel vector of the user is calculated by using the following method:

[0037]

[0038] Wherein, is the received power corresponding to the transmission power of the user and the i-th resource block, v i is the Gaussian white noise power vector, P is the transmission power matrix, s i is the equivalent channel vector on the i-th resource block.

[0039] According to another aspect of the present application, there is provided an interference measurement device based on the power domain, including:

[0040] A transmission power matrix construction module, configured to obtain the power allocation situation of each base station to multiple resource blocks within a preset time period, and construct a transmission power matrix based on the power allocation situation of each base station to multiple resource blocks within the preset time period;

[0041] An optimization model construction module, configured to create an objective function based on maximizing the energy efficiency and minimizing the condition number of the transmission power matrix, and use the allocation situation of the resource blocks, the transmission power range, the signal-to-noise ratio range, and the full rank of the transmission power matrix as the constraint conditions of the objective function to construct an optimization model;

[0042] An optimization model solving module, configured to decompose the optimization model to obtain a first sub-optimization model and a second sub-optimization model, solve the first sub-optimization model, and based on the solution of the first sub-optimization model, solve the second sub-optimization model to obtain an optimal transmission power matrix;

[0043] An equivalent channel vector acquisition module, configured to obtain the received power corresponding to the power allocated by a user to multiple resource blocks, and calculate the equivalent channel vector of the user based on the optimal transmission power matrix, the received power, and the Gaussian white noise power vector.

[0044] Optionally, the transmission power matrix construction module is further configured to:

[0045] Obtain the power allocation situation of each base station to multiple resource blocks of different frequencies within a preset time period, and divide a preset number of adjacent resource elements in the same frequency into a resource sub-block according to the time sequence;

[0046] Obtain the transmission power of each resource element in each resource sub-block, and calculate the average transmission power corresponding to each resource sub-block based on the transmission power of the resource elements in each resource sub-block;

[0047] Generate a transmission power matrix based on the frequency of each resource block and the time and average transmission power corresponding to the included resource sub-blocks.

[0048] Optionally, the optimization model is:

[0049]

[0050] where E(P) represents the energy efficiency of the transmission power matrix P, k(P k [d]) represents the condition number of the transmission power matrix formed by the d-th resource block of the k-th base station and its adjacent resource blocks, δ k,z [d][l] represents whether the k-th base station allocates the l-th resource sub-block of the d-th resource block to the z-th user in the cell where this base station is located, SINR represents the signal-to-noise ratio of the user, γ k,zdenotes the lower bound of the SINR of the z-th user in the cell where the k-th base station is located. denotes the transmission power of the z-th user in the cell where the k-th base station is located. denotes the upper bound of the total power of the k-th base station, P k [d] denotes the transmission power of the d-th resource block of the k-th base station, N RB denotes the number of RBs that generate interference, u k denotes the number of users in the cells where the k base stations are located, RB k denotes the set of RBs of the k-th base station, and K denotes the total number of base stations.

[0051] Optionally, the first sub-optimization model is:

[0052]

[0053] The second sub-optimization model is:

[0054]

[0055] where α represents the decomposition coefficient.

[0056] Optionally, the optimization model solving module is further configured to:

[0057] Based on the fractional form of the energy efficiency, perform a form transformation on the objective function in the first sub-optimization model to obtain a first intermediate objective function;

[0058] Perform a relaxation transformation and a convex-concave transformation on the first intermediate objective function in sequence to obtain a second intermediate objective function;

[0059] Perform a linear decomposition on the signal-to-noise ratio range constraint condition in the first sub-optimization model to obtain a first signal-to-noise ratio linear constraint and a second signal-to-noise ratio linear constraint, and perform a convexity transformation on the second signal-to-noise ratio linear constraint to obtain a third signal-to-noise ratio linear constraint;

[0060] Use the second intermediate objective function, the allocation situation of the resource blocks, the transmission power range, the first signal-to-noise ratio linear constraint, and the third signal-to-noise ratio linear constraint as the transformation model of the first sub-optimization model, and solve the transformation model of the first sub-optimization model.

[0061] Optionally, the optimization model solving module is further configured to:

[0062] Decompose the second sub-optimization model to obtain a first initial optimization model and a second initial optimization model;

[0063] Based on the solution of the first sub-optimization model, convert the first initial optimization model into a first intermediate optimization model;

[0064] Convert the full-rank constraint condition of the transmit power matrix in the first intermediate optimization model into a positive definite constraint condition to obtain a third sub-optimization model, and solve the third sub-optimization model;

[0065] Based on the solution of the third sub-optimization model, add a ratio constraint condition of the maximum eigenvalue and the minimum eigenvalue of the transmit power matrix to the second initial optimization model to obtain a second intermediate optimization model;

[0066] And perform a scaling process on the constraint conditions in the second intermediate optimization model to obtain a fourth sub-optimization model, and solve the fourth sub-optimization model.

[0067] Optionally, the equivalent channel vector of the user is calculated by using the following method:

[0068]

[0069] where is the received power corresponding to the transmit power of the user and the i-th resource block, v i is the Gaussian white noise power vector, P is the transmit power matrix, s i is the equivalent channel vector on the i-th resource block.

[0070] According to another aspect of the present application, a storage medium is provided, in which at least one executable instruction is stored, and the executable instruction enables a processor to perform operations corresponding to the above-mentioned power domain interference map estimation method in a cellular network multi-cell scenario.

[0071] According to another aspect of the present application, a computer device is provided, including: a processor, a memory, a communication interface, and a communication bus, and the processor, the memory, and the communication interface complete communication with each other through the communication bus;

[0072] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the above-mentioned power domain interference map estimation method in a cellular network multi-cell scenario.

[0073] By means of the above technical solutions, the technical solutions provided by the embodiments of the present invention have at least the following advantages:

[0074] A method for estimating a power-domain interference graph in a multi-cell scenario of a cellular network provided by this application measures the transmission power of each base station and the received power of users at different frequencies over a period of time, that is, constructs a transmission power matrix. Designed in combination with the physical characteristics and requirements of multi-cell cellular network communication, taking the maximum energy efficiency of the transmission power matrix and the minimum condition number of the matrix as the objective function, and taking the allocation of resource blocks, the transmission power range, the signal-to-noise ratio range, and the full rank of the transmission power matrix as constraints, constructs an optimization model, solves the optimization model to obtain the optimal transmission power matrix, and infers interference based on the relationship between the transmission power matrix and the received power vector, thereby expanding the original time-domain / frequency-domain method to a new dimension of the power domain, fundamentally solving the problems of large measurement overhead and high requirement for time synchronization accuracy of traditional methods.

[0075] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly, it can be implemented according to the content of the description. And in order to make the above and other purposes, features and advantages of the present invention more obvious and understandable, the following specifically illustrates the specific embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. And throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0077] Figure 1 The flowchart of a method for estimating a power-domain interference graph in a multi-cell scenario of a cellular network provided by an embodiment of this application is shown;

[0078] Figure 2 The schematic diagram of constructing a transmission power matrix of a method for estimating a power-domain interference graph in a multi-cell scenario of a cellular network provided by an embodiment of this application is shown;

[0079] Figure 3 The decomposition diagram of an optimization model of a method for estimating a power-domain interference graph in a multi-cell scenario of a cellular network provided by an embodiment of this application is shown;

[0080] Figure 4 The structural block diagram of an interference measurement device based on the power domain provided by an embodiment of this application is shown;

[0081] Figure 5 The structural schematic diagram of a computer device provided by an embodiment of the present invention is shown.

[0082] Wherein,

[0083] Figure 4In: 402 - Transmission power matrix construction module; 404 - Optimization model construction module; 406 - Optimization model solution module;

[0084] Figure 5 In: 502 - Processor; 504 - Communication interface; 506 - Memory; 508 - Communication bus; 510 - Program. Specific implementation manner

[0085] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments may be combined with each other.

[0086] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following describes in detail the specific implementation manner, structure, features, and their effects of the application according to the present invention with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments may be combined in any suitable form.

[0087] Aiming at the problems of high time synchronization accuracy requirements and large measurement overhead in the existing interference measurement methods for the existing cellular network of cells, the embodiments of the present application provide a method for estimating the power domain interference map in a multi - cell scenario of a cellular network, as Figure 1 shown, the method includes:

[0088] 102: Obtain the power allocation situation of each base station to multiple resource blocks within a preset time period, and construct a transmission power matrix based on the power allocation situation of each base station to multiple resource blocks within the preset time period;

[0089] 104: Create an objective function based on the maximum energy efficiency and minimum condition number of the transmission power matrix, and use the allocation situation of resource blocks, the transmission power range, the signal - to - noise ratio range, and the full rank of the transmission power matrix as the constraint conditions of the objective function to construct an optimization model;

[0090] 106: Decompose the optimization model to obtain a first sub - optimization model and a second sub - optimization model, solve the first sub - optimization model, and based on the solution of the first sub - optimization model, solve the second sub - optimization model to obtain the optimal transmission power matrix;

[0091] 108: Obtain the received power corresponding to the power allocated by the user to multiple resource blocks, and calculate the equivalent channel vector of the user based on the optimal transmission power matrix, the received power, and the Gaussian white noise power vector.

[0092] Specifically, obtain the power allocation of resource blocks (RBs) on different frequencies of each base station in a multi-cell cellular network over a period of time, and then construct a transmit power matrix based on these power allocation situations. Since interference measurement and resource scheduling are coupled, there are two objectives that need to be optimized simultaneously. The first is to maximize the energy efficiency (EE) of the transmit power matrix, and the second is to minimize the condition number of the transmit power matrix. Energy efficiency in a communication system is defined as the ratio between the communication rate and the transmit power. The larger this ratio, the higher the energy utilization efficiency of the communication system. The condition number is used to indicate whether a matrix has good computational properties. Specifically, when the condition number of the constructed transmit power matrix is smaller, the calculated channel size is more accurate. To achieve these two objectives, the present invention uses the basic requirements in an actual multi-cell system, such as the transmit power range, transmit rate magnitude, resource block allocation mode, and full rank of the transmit power, as constraint conditions, and constructs an optimization model with the maximization of the energy efficiency and the minimization of the condition number of the transmit power matrix as the objective function. However, this optimization model has a typical mixed integer non-linear problem (MINLP) and has the property of NP-hard, and the solution process is very complex and cumbersome. Therefore, the optimization model is decomposed into a first sub-optimization model and a second sub-optimization model. First, solve the first sub-optimization model, and based on the solution of the first sub-optimization model, solve the second sub-optimization model to obtain the optimal transmit power matrix, and infer the interference based on the relationship between the transmit power matrix and the received power vector, that is, calculate the equivalent channel vector of the user based on the optimal transmit power matrix, received power, and Gaussian white noise power vector.

[0093] The present application provides a method for estimating a power domain interference graph in a multi-cell scenario of a cellular network. Compared with the prior art, by measuring the transmit power of each base station and the received power of users on different frequencies over a period of time, that is, constructing a transmit power matrix, and designing in combination with the physical characteristics and requirements of multi-cell cellular network communication, taking the maximization of the energy efficiency of the transmit power matrix and the minimum condition number of the matrix as the objective function, and taking the allocation situation of resource blocks, transmit power range, signal-to-noise ratio range, and full rank of the transmit power matrix as constraint conditions, constructing an optimization model, solving the optimization model to obtain the optimal transmit power matrix, and inferring the interference based on the relationship between the transmit power matrix and the received power vector, thereby expanding the original time domain / frequency domain method to a new dimension of the power domain, fundamentally solving the problems of large measurement overhead and high requirement for time synchronization accuracy of traditional methods.

[0094] In one embodiment, constructing a transmit power matrix based on the power allocation of each base station to multiple resource blocks during a preset period includes:

[0095] Obtain the power allocation situation of each base station to multiple resource blocks of different frequencies within a preset time period. According to the chronological order, divide a preset number of adjacent resource elements in the same frequency into a resource sub-block;

[0096] Obtain the transmission power of each resource element in each resource sub-block, and calculate the average transmission power corresponding to each resource sub-block based on the transmission power of the resource elements in each resource sub-block;

[0097] Generate a transmission power matrix based on the frequency of each resource block and the time and average transmission power corresponding to the included resource sub-blocks.

[0098] Specifically, the execution subject of this application is the central control unit. The central control unit constructs a transmission power matrix according to the resource allocation situation of each base station. Each base station in the multi-cell cellular network sends the power allocation situation of resource blocks (RBs) on different frequencies within a period of time to the central control unit, and then the central control unit constructs the transmission matrix.

[0099] Figure 2 Shows a scenario with three RBs, where each resource block can be considered as a set of adjacent several REs (resource elements) in the same time unit (symbol). These RBs can come from the same base station or different base stations, so as to flexibly characterize the interference between different cell base stations or between different RBs within the same base station in the same cell. From Figure 2 it can be seen that in order to measure the interference between these RBs and a certain user, several consecutive time RBs on the same frequency are combined into a "Block", and then the average power of this Block is considered. It should be noted that at the base station side, based on the power size and modulation method assigned to the RB by the scheduling strategy, the ideal power value of each block can be obtained by calculating the mathematical expectation. However, in the actual system, due to the limited number of signal sampling points used to form the block, there is a deviation between the estimated power value and the ideal value. Based on strict mathematical derivation, it is proved that the expected power value can be made close to the true power by reasonably selecting the block length, so as to ignore this estimation error. The resource allocation strategy will cause the power of different blocks to change, so the average power of each adjacent block is calculated to obtain Figure 1 the transmission power matrix on the right side in. As shown in Figure 1, each row in this 3*3 matrix represents a different RB, and each column represents a block at different times.

[0100] Constructing the transmission power matrix by calculating the average power does not require distinguishing whether the signal is a data signal or a reference signal, thus fundamentally avoiding the occupation of time-frequency resources by the reference signal.

[0101] The construction of the transmit power matrix can also average out the inter-carrier interference on a larger scale. Specifically, the inter-carrier interference originally exists between REs and shows a decreasing trend as the frequency interval between REs increases. The present invention follows the principle in the cellular network standard that the RB is the smallest resource allocation unit, combines multiple REs into RBs for consideration. This way averages out the differences in the magnitudes of inter-carrier interference caused by different interval frequencies between REs. As a result, the differences in the orders of magnitude of the inter-carrier interference borne by each RB are relatively small, and it has good robustness to CFO, improving the measurement accuracy as a whole.

[0102] In addition, the construction of the transmit power matrix can also cope with a certain degree of time asynchrony. The traditional CSI-IM method requires strict time synchronization so that users can only receive signals from a single base station on a specific RE, thereby calculating the inter-cell interference.

[0103] In an embodiment of the present invention, the optimization model is:

[0104]

[0105] where E(P) represents the energy efficiency of the transmit power matrix P, k(P k [d]) represents the condition number of the transmit power matrix formed by the d-th resource block of the k-th base station and its adjacent resource blocks, δ k,z [d][l] represents whether the k-th base station allocates the l-th resource sub-block of the d-th resource block to the z-th user in the cell where this base station is located, SINR represents the signal-to-noise ratio of the user, γ k,z is the lower bound of the SINR of the z-th user in the cell where the k-th base station is located, is the transmit power of the z-th user in the cell where the k-th base station is located, is the upper bound of the total power of the k-th base station, P k [d] represents the transmit power of the d-th resource block of the k-th base station, N RR is the number of RBs generating interference, u k represents the number of users in the cell where the k base stations are located, RB k represents the set of RBs of the k-th base station, and K represents the total number of base stations.

[0106] Specifically, resource allocation is performed according to the requirements and physical characteristics of a multi-cell cellular network. In this application, interference measurement and resource scheduling are coupled together. Therefore, there are two objectives that need to be optimized simultaneously. The first is to maximize the energy efficiency (EE), and the second is to minimize the condition number of the transmit power matrix. Energy efficiency in a communication system is defined as the ratio between the communication rate and the transmit power. The larger this ratio is, the higher the energy utilization efficiency of the communication system. The condition number is used to indicate whether a matrix has good computational properties. Specifically, when the condition number of the constructed transmit power matrix is smaller, the calculated channel size is more accurate. To achieve these two objectives, this application uses the basic requirements in an actual multi-cell system, such as the transmit power range, resource block allocation mode, and full rank of the transmit power matrix, as constraint conditions, and constructs an optimization model with maximizing energy efficiency and minimizing the condition number as the optimization objectives. The optimization model is as follows:

[0107]

[0108] where E(P) represents the EE of the power matrix P, and k(P k [d]) represents the condition number of the d-th RB and its adjacent RBs of the k-th base station, forming a power matrix, and δ k,z [d][l] represents whether the k-th base station allocates the d-th RB on its l-th block to the z-th user in the cell where this base station is located. SINR represents the signal-to-noise ratio of the user, and γ k,z represents the lower bound of the SINR of the z-th user in the cell where the k-th base station is located, represents the upper bound of the total power of the k-th base station, and N RB represents the number of RBs generating interference.

[0109] In one embodiment, the first sub-optimization model is:

[0110]

[0111]

[0112] The second sub-optimization model is:

[0113]

[0114] where α represents the decomposition coefficient.

[0115] Specifically, the optimization model is a model with a typical Mixed-Integer Nonlinear Problem (MINLP) and has the property of NP-hard. The solving process is complex and cumbersome. Therefore, in this application, the optimization model is decomposed, transformed, and then solved, and the solving process is simple and easy. The solving process is mainly divided into three stages. In the first stage, the original optimization problem is decomposed into two sub-problems, namely maximizing EE (energy efficiency) and minimizing the condition number, that is, the optimization model is decomposed into the first sub-optimization model and the second sub-optimization model. In the second stage, by relaxing the non-linear SINR constraint, the original problem in the first sub-optimization model is transformed into a second-order cone problem for solution. In the third stage, the resource block allocation method obtained in the second stage is used as the input, and the condition number and EE (energy efficiency) are used as the objective functions for optimization. By iteratively relaxing the full-rank constraint, the final resource allocation result is obtained.

[0116] The first sub-optimization model is:

[0117]

[0118] The second sub-optimization model is:

[0119]

[0120] Among them, the first sub-optimization model removes the full-rank constraint and only considers how to maximize EE (energy efficiency); after the second sub-optimization model obtains the optimization result of the first sub-optimization model for δ k,z , EE and the condition number are used as the two variables of the objective function. At this time, the second sub-optimization model is a multi-objective optimization problem.

[0121] In one embodiment, solving the first sub-optimization model includes:

[0122] Based on the fractional form of energy efficiency, perform a form transformation on the objective function in the first sub-optimization model to obtain the first intermediate objective function;

[0123] Perform a relaxation transformation and a convex-concave transformation on the first intermediate objective function in sequence to obtain the second intermediate objective function;

[0124] Perform a linear decomposition on the signal-to-noise ratio range constraint condition in the first sub-optimization model to obtain the first signal-to-noise ratio linear constraint and the second signal-to-noise ratio linear constraint, and perform a convexity transformation on the second signal-to-noise ratio linear constraint to obtain the third signal-to-noise ratio linear constraint;

[0125] Use the second intermediate objective function, the resource block allocation situation, the transmission power range, the first signal-to-noise ratio linear constraint, and the third signal-to-noise ratio linear constraint as the transformation model of the first sub-optimization model, and solve the transformation model of the first sub-optimization model.

[0126] Specifically, at this stage: Solving the first sub-optimization model is divided into three sub-steps:

[0127] Step 1: Since EE (energy efficiency) is a fractional objective function, convert it into a subtraction-form objective function as the first intermediate objective function:

[0128]

[0129] where f and g are respectively equal to the numerator and denominator in the original EE, λ is given at initialization and is continuously updated during subsequent optimization. Solve according to the Dinkelbach algorithm. Since this algorithm requires the convexity of f, g, and the feasible set, relax the first intermediate objective function.

[0130] Step 2: Continuity relaxation

[0131] Relax the integer variable δ in quadratic form k,z to any integer value between 0 and 1. To eliminate the impact brought by this relaxation, add a penalty term for this variable to the first intermediate objective function to make the value of this variable approach 0 or 1. The specific form is as follows:

[0132]

[0133] Step 3: Convex approximation: Since f and g contain non-linear log functions, rewrite f in the following form:

[0134]

[0135] And define the following f 1 , f 2 functions:

[0136]

[0137] After that, the first intermediate objective function can be expressed by the following formula after conversion:

[0138]

[0139] To make this objective function concave, perform a first-order Taylor expansion on f 2 to obtain the second intermediate objective function. The first-order Taylor expansion formula of f 2 is:

[0140]

[0141] At this time, for the non-linear constraint C still existing in the first sub-optimization model 4 . C 4 can be decomposed into C 4.1and C 4.2

[0142]

[0143] Using successive convex approximation (SCA), C can be 4.2 transformed into C 4.3

[0144]

[0145] wherein Combining all the above operations, the first sub-optimization model can be written as the transformation model of the first sub-optimization model:

[0146]

[0147] The transformation model of the first sub-optimization model is a second-order cone programming problem and can be iteratively solved until convergence.

[0148] In one embodiment, based on the solution of the first sub-optimization model, solving the second sub-optimization model includes:

[0149] Decomposing the second sub-optimization model to obtain a first initial optimization model and a second initial optimization model;

[0150] Based on the solution of the first sub-optimization model, transforming the first initial optimization model into a first intermediate optimization model;

[0151] Converting the full-rank constraint condition of the transmit power matrix in the first intermediate optimization model into a positive definite constraint condition to obtain a third sub-optimization model, and solving the third sub-optimization model;

[0152] Based on the solution of the third sub-optimization model, adding a ratio constraint condition of the maximum eigenvalue and the minimum eigenvalue of the transmit power matrix in the second initial optimization model to obtain a second intermediate optimization model;

[0153] And performing a scaling process on the constraint conditions in the second intermediate optimization model to obtain a fourth sub-optimization model, and solving the fourth sub-optimization model.

[0154] Specifically, as Figure 3 shown, in this stage, the second sub-optimization model is decomposed to obtain a first initial optimization model and a second initial optimization model to optimize EE (energy efficiency) and the condition number respectively. In the previous stage, by solving the transformation model of the first sub-optimization model, the value of δ k,z is determined. At this time, the first initial optimization model degenerates into a full-rank constraint multi-objective optimization problem and can be rewritten as the first intermediate optimization model:

[0155]

[0156] The full-rank constraint can be approximated as a series of semidefinite programming (SDP) constraints. Assume that Z k [d] is an n b *n b symmetric matrix, and Inb is the identity matrix, where nb is equal to the number of resource blocks, and U k [d] is the constraint matrix of the eigenvalues of the transmit power matrix. By adding the constraint conditions of the eigenvalues of the transmit power matrix to the first intermediate optimization model, the third sub-optimization model is obtained. It is only necessary to iteratively solve the following third sub-optimization model:

[0157]

[0158] where P (t) is the transmit power matrix at the t-th iteration, is the convergence number, includes the eigenvectors corresponding to the minimum eigenvalues of nb . w (t) represents the weight that increases with the iteration number t.

[0159] After obtaining the solution of the third optimization model, use its EE (energy efficiency) solution as the lower bound to solve the second initial optimization model. Since the condition number is the ratio of the largest eigenvalue to the smallest eigenvalue of the power matrix P, minimizing the condition number can be transformed into constraining the magnitude of this ratio. Introduce the constraint term C 8 to describe it:

[0160]

[0161] and use C 9 to ensure that the EE (energy efficiency) is not less than the EE (energy efficiency) value returned by the constraint term C 3.1 :

[0162] (C 9 )f 1 (P)-f 2 (P)≥λ * q(P).

[0163] The second intermediate optimization model is obtained.

[0164] Since is not linear, C 8 still needs to be further transformed. When the optimization process converges, converges to Z k [d]. Therefore, Z can be usedk [d] is substituted into the original C by dividing by μ 8 With the constraint, we obtain

[0165]

[0166] The same scaling is also performed on the remaining constraints to obtain the fourth optimization sub-model. Finally, only by iteratively solving the fourth optimization sub-model can the solution to the entire problem be obtained:

[0167]

[0168] In one embodiment, the following method is used to calculate the equivalent channel vector of the user:

[0169]

[0170] where is the received power corresponding to the transmission power of the user and the i-th resource block, v i is the Gaussian white noise power vector, P is the transmission power matrix, s i is the equivalent channel vector on the i-th resource block.

[0171] Specifically, based on the transmission power matrix, the received power of a certain user on different frequency resource blocks (resource blocks in the transmission power matrix) is obtained. During several block times corresponding to the transmission power matrix, the received power of a user on the i-th resource block is calculated, and this received power can be represented by the vector After that, based on the characteristic in wireless communication that the received power is equal to the transmission power * channel response, the following equation is obtained:

[0172]

[0173] where v i represents the Gaussian white noise power vector, s i represents the equivalent channel vector on the i-th resource block. Since the transmission power P is full rank, it is easy to obtain the magnitude of s i . Since the optimal transmission power matrix is obtained under the full rank constraint, substituting the optimal transmission power matrix into the above formula can obtain the equivalent channel vector on the resource block.

[0174] Furthermore, as an implementation of the method shown above Figure 1 the embodiment of the present invention provides a power domain-based interference measurement device, as shown in Figure 4 shown, the device includes:

[0175] A transmission power matrix construction module 402, configured to obtain the power allocation conditions of each base station to multiple resource blocks within a preset time period, and construct a transmission power matrix based on the power allocation conditions of each base station to multiple resource blocks within the preset time period;

[0176] An optimization model construction module 404, configured to create an objective function based on maximizing the energy efficiency and minimizing the condition number of the transmission power matrix, and use the allocation conditions of resource blocks, the transmission power range, the signal-to-noise ratio range, and the full rank of the transmission power matrix as the constraint conditions of the objective function to construct an optimization model;

[0177] An optimization model solving module 406, configured to decompose the optimization model to obtain a first sub-optimization model and a second sub-optimization model, solve the first sub-optimization model, and based on the solution of the first sub-optimization model, solve the second sub-optimization model to obtain an optimal transmission power matrix;

[0178] An equivalent channel vector acquisition module 408, configured to obtain the received power corresponding to the power allocated by a user to multiple resource blocks, and calculate the equivalent channel vector of the user based on the optimal transmission power matrix, the received power, and the Gaussian white noise power vector.

[0179] This application provides an interference measurement device based on the power domain. Compared with the prior art, by measuring the transmission power of each base station and the received power of the user at different frequencies within a period of time, that is, constructing a transmission power matrix, and designing in combination with the physical characteristics and requirements of multi-cell cellular network communication, taking the maximization of the energy efficiency of the transmission power matrix and the minimum condition number of the matrix as the objective function, and taking the allocation conditions of resource blocks, the transmission power range, the signal-to-noise ratio range, and the full rank of the transmission power matrix as the constraint conditions to construct an optimization model, solving the optimization model to obtain an optimal transmission power matrix, and inferring interference based on the relationship between the transmission power matrix and the received power vector, so as to expand the original time domain / frequency domain method to a new dimension of the power domain, fundamentally solving the problems of large measurement overhead and high requirement for time synchronization accuracy of the traditional method.

[0180] In one embodiment, the transmission power matrix construction module is further configured to:

[0181] Obtain the power allocation conditions of each base station to multiple resource blocks with different frequencies within a preset time period, and divide a preset number of adjacent resource elements in the same frequency into a resource sub-block according to the time sequence;

[0182] Obtain the transmission power of each resource element in each resource sub-block, and calculate the average transmission power corresponding to each resource sub-block based on the transmission power of the resource elements in each resource sub-block.

[0183] Generate a transmission power matrix based on the frequency of each resource block, the time corresponding to the included resource sub - blocks, and the average transmission power.

[0184] In one embodiment, the optimization model is:

[0185]

[0186] where \(E(P)\) represents the energy efficiency of the transmission power matrix \(P\), \(k(P k [d])\) represents the condition number of the transmission power matrix formed by the \(d\) - th resource block of the \(k\) - th base station and its adjacent resource blocks, \(\delta k,z [d][l]\) represents whether the \(k\) - th base station allocates the \(l\) - th resource sub - block of the \(d\) - th resource block to the \(z\) - th user in the cell where this base station is located, \(SINR\) represents the signal - to - noise ratio of the user, \(\gamma k,z represents the lower bound of the \(SINR\) of the \(z\) - th user in the cell where the \(k\) - th base station is located, represents the transmission power of the \(z\) - th user in the cell where the \(k\) - th base station is located, represents the upper bound of the total power of the \(k\) - th base station, \(P k [d]\) represents the transmission power of the \(d\) - th resource block of the \(k\) - th base station, \(N RB is the number of RBs that generate interference, \(u k represents the number of users in the cell where the \(k\) base stations are located, \(RB k represents the set of RBs of the \(k\) - th base station, and \(K\) represents the total number of base stations.

[0187] In one embodiment, the first sub - optimization model is:

[0188]

[0189] The second sub - optimization model is:

[0190]

[0191] where \(\alpha\) represents the decomposition coefficient.

[0192] In one embodiment, the optimization model solving module is further configured to:

[0193] Based on the fractional form of the energy efficiency, perform a form transformation on the objective function in the first sub - optimization model to obtain a first intermediate objective function;

[0194] Perform a relaxation transformation and a convex - concave transformation on the first intermediate objective function in sequence to obtain a second intermediate objective function;

[0195] Perform a linear decomposition on the signal - to - noise ratio range constraint condition in the first sub - optimization model to obtain a first signal - to - noise ratio linear constraint and a second signal - to - noise ratio linear constraint, and perform a convexity transformation on the second signal - to - noise ratio linear constraint to obtain a third signal - to - noise ratio linear constraint;

[0196] Take the second intermediate objective function, the allocation of resource blocks, the transmit power range, the first signal-to-noise ratio linear constraint, and the third signal-to-noise ratio linear constraint as the transformation model of the first sub-optimization model, and solve the transformation model of the first sub-optimization model.

[0197] In one embodiment, the optimization model solving module is further configured to:

[0198] Decompose the second sub-optimization model to obtain a first initial optimization model and a second initial optimization model;

[0199] Based on the solution of the first sub-optimization model, convert the first initial optimization model into a first intermediate optimization model;

[0200] Convert the full rank constraint condition of the transmit power matrix in the first intermediate optimization model into a positive definite constraint condition to obtain a third sub-optimization model, and solve the third sub-optimization model;

[0201] Based on the solution of the third sub-optimization model, add a ratio constraint condition of the maximum eigenvalue and the minimum eigenvalue of the transmit power matrix to the second initial optimization model to obtain a second intermediate optimization model;

[0202] And perform a scaling process on the constraint conditions in the second intermediate optimization model to obtain a fourth sub-optimization model, and solve the fourth sub-optimization model.

[0203] In one embodiment, the following method is used to calculate the equivalent channel vector of the user:

[0204]

[0205] Wherein, is the received power corresponding to the transmit power of the user and the i-th resource block, v i is the Gaussian white noise power vector, P is the transmit power matrix, s i is the equivalent channel vector on the i-th resource block.

[0206] According to an embodiment of the present invention, there is provided a storage medium storing at least one executable instruction, and the computer executable instruction can execute the power domain interference graph estimation method in the cellular network multi-cell scenario in any of the above method embodiments.

[0207] Figure 5 FIG. shows a schematic structural diagram of a computer device provided according to an embodiment of the present invention, and the specific implementation of the computer device is not limited in the specific embodiments of the present invention.

[0208] Such as Figure 5As shown in the figure, the computer device may include: a processor 502, a communications interface 504, a memory 506, and a communication bus 508.

[0209] Among them: the processor 502, the communications interface 504, and the memory 506 communicate with each other through the communication bus 508.

[0210] The communications interface 504 is used to communicate with network elements of other devices such as clients or other servers.

[0211] The processor 502 is used to execute the program 510, and specifically can execute the relevant steps in the embodiment of the method for estimating the power domain interference graph in the multi-cell scenario of the cellular network described above.

[0212] Specifically, the program 510 may include program code, and the program code includes computer operation instructions.

[0213] The processor 502 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention. One or more processors included in the computer device may be of the same type of processor, such as one or more CPUs; or may be of different types of processors, such as one or more CPUs and one or more ASICs.

[0214] The memory 506 is used to store the program 510. The memory 506 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk memory.

[0215] The program 510 is specifically used to cause the processor 502 to perform the following operations:

[0216] Obtain the power allocation situation of each base station to multiple resource blocks within a preset time period, and construct a transmission power matrix based on the power allocation situation of each base station to multiple resource blocks within the preset time period;

[0217] Create an objective function based on the maximum energy efficiency and minimum condition number of the transmission power matrix, and use the allocation situation of resource blocks, the transmission power range, the signal-to-noise ratio range, and the full rank of the transmission power matrix as the constraint conditions of the objective function to construct an optimization model;

[0218] Decompose the optimized model to obtain a first sub-optimized model and a second sub-optimized model, solve the first sub-optimized model, and based on the solution of the first sub-optimized model, solve the second sub-optimized model to obtain the optimal transmit power matrix;

[0219] Obtain the received power corresponding to the power allocated to multiple resource blocks by the user, and calculate the equivalent channel vector of the user based on the optimal transmit power matrix, the received power, and the Gaussian white noise power vector.

[0220] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present invention can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. In one embodiment, they can be implemented by program codes executable by the computing device. Thus, they can be stored in a storage device and executed by the computing device. And in some cases, the steps shown or described can be executed in a different order from here, or they can be separately made into individual integrated circuit modules, or multiple modules or steps among them can be made into a single integrated circuit module to implement. In this way, the present invention is not limited to any specific combination of hardware and software.

[0221] The above embodiments are only exemplary embodiments of the present application and are not used to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art can make various modifications or equivalent replacements within the essence and protection scope of the present application, and such modifications or equivalent replacements should also be regarded as falling within the protection scope of the present application.

Claims

1. A method for estimating a power domain interference graph in a multi-cell scenario of a cellular network, characterized in that: include: Acquire the power conditions allocated by each base station to the multiple resource blocks within a preset period, and construct a transmit power matrix based on the power conditions allocated by each base station to the multiple resource blocks within the preset period; Based on the maximization of energy efficiency and the minimization of condition number of the transmit power matrix, an objective function is created, and the allocation of the resource blocks, the transmit power range, the signal-to-noise ratio range, and the full rank of the transmit power matrix are used as constraints of the objective function to construct an optimization model; Decomposing the optimization model to obtain a first sub-optimization model and a second sub-optimization model, solving the first sub-optimization model, and solving the second sub-optimization model based on the solution of the first sub-optimization model to obtain an optimal transmission power matrix; The receiving power corresponding to the power allocated to the user and the multiple resource blocks is obtained, and based on the optimal transmission power matrix, the receiving power and the Gaussian white noise power vector, an equivalent channel vector of the user is calculated.

2. The power domain interference graph estimation method in a cellular network multi-cell scenario as claimed in claim 1, characterized in that: The constructing a transmission power matrix based on the power allocated by each base station to a plurality of resource blocks within a preset time period includes: Obtaining the power allocated by each base station to multiple resource blocks of different frequencies within a preset period of time, and dividing a preset number of adjacent resource elements in the same frequency into a resource sub-block according to the time sequence; Obtaining the transmit power of each resource element in each resource sub-block, and calculating the average transmit power corresponding to each resource sub-block based on the transmit power of the resource elements in each resource sub-block; A transmit power matrix is ​​generated based on the frequency of each resource block, the time corresponding to the resource sub-blocks included therein, and the average transmit power.

3. The power domain interference graph estimation method in a cellular network multi-cell scenario as claimed in claim 1, characterized in that: The optimization model is: Where E(P) represents the energy efficiency of the transmit power matrix P, k(P k [d]) represents the condition number of the transmit power matrix formed by the d-th resource block of the k-th base station and its adjacent resource blocks, δ k，z [d][l] indicates whether the kth base station allocates the lth resource subblock of the dth resource block to the zth user in the cell where the base station is located. SINR indicates the signal-to-noise ratio of the user, γ k，z represents the lower bound of the SINR of the zth user in the cell where the kth base station is located, represents the transmission power of the zth user in the cell where the kth base station is located, represents the upper bound of the total power of the kth base station, SINR k，z [d][l] is the signal-to-noise ratio of the zth user in the cell where the kth base station is located for the lth resource subblock of the dth resource block, is the transmission power of the zth user in the cell where the kth base station is located for the lth resource subblock of the dth resource block, is the power upper bound of the kth base station for the lth resource subblock of the dth resource block, P k [d] represents the transmission power of the dth resource block of the kth base station, N RB Indicates the number of RBs causing interference, u k Indicates the number of users in the cell where k base stations are located, RB k represents the RB set of the kth base station, and K represents the total number of base stations.

4. The method for estimating a power domain interference graph in a cellular network multi-cell scenario according to claim 3, characterized in that: The first sub-optimization model is: The second sub-optimization model is: Where α represents the decomposition coefficient.

5. The power domain interference graph estimation method in a cellular network multi-cell scenario as claimed in claim 4, characterized in that: Solving the first sub-optimization model includes: Based on the fractional form of energy efficiency, converting the objective function in the first sub-optimization model into a first intermediate objective function; Performing relaxation conversion and convexity conversion on the first intermediate objective function in sequence to obtain a second intermediate objective function; Performing linear decomposition on the signal-to-noise ratio range constraint in the first sub-optimization model to obtain a first signal-to-noise ratio linear constraint and a second signal-to-noise ratio linear constraint, and performing convexity transformation on the second signal-to-noise ratio linear constraint to obtain a third signal-to-noise ratio linear constraint; The second intermediate objective function, the allocation of the resource blocks, the transmission power range, the first signal-to-noise ratio linear constraint and the third signal-to-noise ratio linear constraint are used as a conversion model of the first sub-optimization model, and the conversion model of the first sub-optimization model is solved.

6. The power domain interference graph estimation method in a cellular network multi-cell scenario as claimed in claim 4, characterized in that: The step of solving the second sub-optimization model based on the solution of the first sub-optimization model comprises: Decomposing the second sub-optimization model to obtain a first initial optimization model and a second initial optimization model; Based on the solution of the first sub-optimization model, converting the first initial optimization model into a first intermediate optimization model; Converting the full rank constraint of the transmit power matrix in the first intermediate optimization model into a positive definite constraint to obtain a third sub-optimization model, and solving the third sub-optimization model; Based on the solution of the third sub-optimization model, adding a ratio constraint condition of the maximum eigenvalue and the minimum eigenvalue of the transmit power matrix to the second initial optimization model to obtain a second intermediate optimization model; The constraint conditions in the second intermediate optimization model are scaled to obtain a fourth sub-optimization model, and the fourth sub-optimization model is solved.

7. The method for estimating a power domain interference graph in a cellular network multi-cell scenario according to any one of claims 1 to 5, characterized in that: The equivalent channel vector of the user is calculated using the following method: in, is the received power of the user corresponding to the transmit power of the i-th resource block, v i is the Gaussian white noise power vector, P is the transmit power matrix, s i is the equivalent channel vector on the i-th resource block.

8. An interference measurement device based on power domain, characterized in that: include: A transmission power matrix construction module, used to obtain the power conditions allocated by each base station to multiple resource blocks within a preset period of time, and to construct a transmission power matrix based on the power conditions allocated by each base station to multiple resource blocks within the preset period of time; An optimization model building module is used to create an objective function based on maximizing energy efficiency and minimizing condition number of the transmit power matrix, and to construct an optimization model by taking the allocation of the resource blocks, the transmit power range, the signal-to-noise ratio range, and the full rank of the transmit power matrix as constraints of the objective function; an optimization model solving module, configured to decompose the optimization model to obtain a first sub-optimization model and a second sub-optimization model, solve the first sub-optimization model, and solve the second sub-optimization model based on the solution of the first sub-optimization model to obtain an optimal transmission power matrix; The equivalent channel vector acquisition module is used to obtain the receiving power corresponding to the power allocated to the user and multiple resource blocks, and calculate the equivalent channel vector of the user based on the optimal transmission power matrix, the receiving power and the Gaussian white noise power vector.

9. A storage medium storing at least one executable instruction, wherein the executable instruction enables a processor to perform operations corresponding to the power domain interference graph estimation method in a cellular network multi-cell scenario as described in any one of claims 1-7.

10. A computer device comprising: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the power domain interference graph estimation method in a cellular network multi-cell scenario as described in any one of claims 1-7.

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