ESTIMATION OF CHARACTERISTIC PARAMETERS OF RECEPTION QUALITY IN THE LOCALIZATION OF A CELLULAR RADIO COMMUNICATION NETWORK FROM AN APPROACH TO THE AVERAGE CURRENT OF CORRELATION COEFFICIENTS

Abstract

The invention relates to a method for estimating parameters characteristic of reception quality at a location of a cellular radiocommunication network comprising: - a selection from a set of network cells, of at least two cells associated with the highest average reception powers of a useful signal at the location; - a determination, on a logarithmic scale, of at least two signal-to-interference-plus-noise ratios at the location for the useful signal received from each of said at least two selected cells; - a determination of a correlation coefficient between said at least two signal-to-interference-plus-noise ratios determined on a logarithmic scale, - a determination of a maximum between said at least two signal-to-interference-plus-noise ratios determined on a logarithmic scale;- an estimation of the characteristic parameters of a reception quality at the location based on the maximum and the correlation coefficient.

Description

Technical field

[0001] The field of the invention is that of cellular radio communications, for example in cellular communication networks of the 3G, 4G, 5G or higher type. More specifically, the invention relates to the estimation of parameters characteristic of a quality of reception of a useful signal, at all points of such networks, for the purposes, for example, of planning or optimizing network resources, or of monitoring its performance. Prior art

[0002] Cellular radiocommunication networks are traditionally structured into neighboring cells, each of which is equipped with one or more base stations, each carrying a plurality of transmitting antennas. The cells form a tiling of a geographical area, and one objective of the radiocommunication network operator is to ensure radio coverage for its users across the entire geographical area in question, i.e. to ensure access to the services it offers at any point in this geographical area, while avoiding the appearance of dead zones as much as possible.

[0003] In a 3G radio environment (or third generation radio communications network, also called UMTS for English " Universal Mobile Telecommunications System', in French "universal mobile telecommunications system"), 4G (or fourth generation radio communications network, also called LTE for the English " Long-Term Evolution") and 5G (or fifth generation radio communications network), the transmitting antennas of neighboring cells emit useful signals in the same frequency band. At a given moment, each user terminal is attached to one of the cells of the network, commonly called a server cell, and from which it receives the useful signal it needs.

[0004] However, in addition to the useful signal sent by its serving cell, a user terminal also receives interference signals from other cells in whose coverage area it is located. The ability of the user terminal to correctly decode the signal intended for it depends on the reception power of the useful signal and the interference, and more specifically on the ratio of these two quantities. The "SINR" metric (in English " Signal to Noise plus interference ratio"; in French "signal to interference plus noise ratio"), is the ratio of the power of the useful signal divided by the sum of the powers of the interfering signals and the thermal noise, received at the receiver of the user terminal. If the user terminal is able to correctly decode the useful signal intended for it for a given service, then this service is accessible with sufficient quality at the location of the terminal.

[0005] The coverage area for this service can therefore be defined as the set of locations, within the cell, where the received SINR is greater than a given threshold. The operator sizes and configures its network according to its objectives, including coverage, for example, 99% of the territory must be covered for voice service, 95% for video service, etc. Since coverage cannot be measured at every location in the network, the precise estimation of the SINR and its characteristic parameters is crucial to ensure the operator's coverage objectives.

[0006] However, the signal transmitted by a base station and received by a user terminal is subject to variations linked to the nature of the radio environment. Indeed, the reception powers at two user terminals located at the same distance from the base station are different because the obstacles existing on the paths between each user terminal and the base station are different (reflection phenomena on significant obstacles, such as buildings in urban areas or forests in rural areas for example). In this case, we speak of the random phenomenon of "shadowing" which adds a weakening term in the expression of the power of the radio signal received by the user terminal.

[0007] As previously indicated, in a cellular radiocommunication network, a user terminal is typically attached to the cell offering it the highest reception power, which is commonly called its server cell. To estimate the coverage offered at each location of the radiocommunication network, it is therefore first necessary to determine which is the server cell at this location, then the interfering cells, and then to estimate the associated SINR. However, in the absence of measurements and due to the random variation in the powers of the signals received at a given location (the "shadowing" phenomenon), the identity of the server cell is not always known deterministically, and it can vary statistically, especially at the edge of cells.

[0008] However, for the sake of simplification, previously published work on this subject is based on the assumption that at a given location in the network, the serving cell is "fixed", and corresponds, for example, to the cell from which the useful signal is received with the highest average reception power for the user terminal.

[0009] So, in the article "SINR and rate distributions for downlink cellular networks", IEEE Transactions on UVireless Communications, vol. 19, no. 7, pp. 4604-4616, 2020, published by the inventors of the present patent application, the authors propose to evaluate the quality of service perceived by a user terminal from the statistical distribution of the SINR ratio, which is approximated in the form of a normal random variable in the logarithmic domain, the mean and variance of which can be calculated. This work is based on the simplifying assumption that at a given location in the network, a user terminal receives a useful signal from a fixed server cell k, and M interfering signals from M neighboring cells. The SINR at this location is then defined as the ratio of the power of the useful signal received from this server cell k to the sum of the power of the thermal noise and the powers of the interfering signals received from the M neighboring cells.

[0010] In the article, " Downlink average rate and SINR distribution in cellular networks," IEEE Transactions on Communications, vol. 64, no. 2, pp. 847-862, Feb. 2016, X. Yan et al. are particularly interested in cellular networks based on an OFDMA type multiplexing technique (for the English " Orthogonal Frequency Division Multiple Access ", in French "orthogonal frequency division multiple access"), and propose another approach for the statistical modeling of the SINR ratio. Their work is also based on the simplifying hypothesis that at a given location (r, 0) of the network, a user terminal receives a useful signal from a base station BS 0 of a fixed serving cell, and L interfering signals from the base stations BS i of i interfering neighboring cells.

[0011] In each of these two publications, the characteristic parameters proposed to estimate the SINR distribution are only valid if the server cell of a user terminal actually remains unchanged. However, in a real environment, in which the random phenomenon of "shadowing" is added to the average power of the signal received by a user terminal, it is common for several nearby cells to statistically exchange the role of server cell, at a given location in the network.

[0012] Thus, the approximation on which these two articles of the prior art are based is satisfactory when the difference between the average power of the signal received from a first cell having the highest value and the average power of the signal received from a second cell having the second highest value is quite large, typically for user terminals close to the center of the cell. However, it reaches its limits of validity for user terminals at the edge of cells. However, it should be noted that the area at the edge of cells is the area where it is important, for the network operator, to know the SINR precisely in order to guarantee coverage.

[0013] There is therefore a need for a technique for estimating parameters characteristic of reception quality at a location of a cellular radiocommunication network which improves these works of the prior art. In particular, there is a need for such a technique which improves the estimation of the quality of the signal received at any point of a cellular radiocommunication network, and in particular, but not exclusively, in locations at the edge of cells.

[0014] There is still a need for such a technique that can improve the estimation of SINR characteristics, particularly for planning, radio coverage optimization or even monitoring the performance of a cellular radiocommunication network. Exposition of the invention

[0015] The invention meets this need by proposing a method for estimating parameters characteristic of reception quality at a location of a cellular radiocommunication network comprising: a selection from a set of cells in the network, of at least two cells associated with the highest average reception powers of a useful signal at the location; a determination, on a logarithmic scale, of at least two signal-to-interference-plus-noise ratios at the location for the useful signal received from each of said at least two selected cells; a determination of a correlation coefficient between said at least two signal-to-interference-plus-noise ratios determined on a logarithmic scale, a determination of a maximum between said at least two signal-to-interference-plus-noise ratios determined on a logarithmic scale; an estimation of the characteristic parameters of a reception quality at the location from the maximum and the correlation coefficient.

[0016] Thus, the invention is based on a completely new and inventive approach to estimating reception quality at any point in a network, for the purposes of, for example, network planning, optimizing an existing cellular radiocommunication network, or monitoring network performance. Indeed, the prior art techniques for estimating reception quality are all based on the assumption that at a given location in the network, there is a single, fixed server cell to which a user terminal is attached. This is the assumption on which the proposals of the aforementioned articles are based. " SINR and rate distributions for downlink cellular networks", IEEE Transactions on UVireless Communications, vol. 19, no. 7, pp. 4604-4616, 2020 , published by the inventors of the present patent application and "Downlink average rate and SINR distribution in cellular networks," IEEE Transactions on Communications, vol. 64, no. 2, pp. 847-862, Feb. 2016, de X. Yan et al.

[0017] Unlike these prior works, the estimation technique according to an embodiment of the invention considers the realistic case where the role of waitress can be statistically played by several neighboring cells, which is particularly frequent in the case where the user terminal is located at the edge of the cell, due to the random nature of the “Shadowing” phenomenon. The present solution thus seeks to identify two or more cells which can potentially play the role of waitress cell in a given location, it being understood that at a given instant, a user terminal is only attached to a single waitress cell, from which it receives the useful signal.It further proposes a method for calculating the characteristic parameters of the signal to interference plus noise ratio measured for the user terminal in this realistic case, from the signal to interference plus noise ratios measured for the user terminal for each of the radio signals emitted by the plurality of cells likely to play the role of server cell, namely those whose reception power of the useful signal at this location is the highest.

[0018] This signal to interference plus noise ratio, which can be described as realistic given the working hypothesis formulated, is calculated in the form of a maximum, on a logarithmic scale, of the signal to interference plus noise ratios of the different potential server cells.

[0019] Advantageously, in order to be able to estimate the characteristic parameters of the reception quality at a given location, the method is based on the determination of a correlation coefficient between the signal to interference plus noise ratios previously determined.

[0020] Knowledge of the maximum signal to interference plus noise ratios and a correlation coefficient between these signal to interference plus noise ratios makes it possible to estimate a certain number of parameters characteristic of the reception quality at a given location, and in particular the probability, in a geographical area, of having a signal to interference plus noise ratio greater than a given threshold, to estimate, for example, the quality of the coverage of the cellular radiocommunication network.

[0021] In a particular embodiment, the determination of the correlation coefficient comprises an approximation of the correlation coefficient. This approximation is determined from a calculation of an average of a set of correlation coefficients between said at least two signal-to-interference-plus-noise ratios determined on a logarithmic scale. The estimation of the characteristic parameters of a reception quality at the location is then made from the maximum and the correlation coefficient approximated from the calculated average.

[0022] According to the work of S CE Clark, "The greatest of afinite set of random variables," in Operations Research, Vol. 9, No. 2, 145-162, 1961, the mean and variance can be calculated for the maximum of two correlated normal random variables, assuming that the correlation coefficient between these two variables is known. In the case of planning and optimization of a cellular network, the correlation coefficient between the SINRs of two potentially serving cells on a logarithmic scale is not a known quantity. Thus, according to one embodiment of the invention, the correlation coefficient is iteratively calculated, then its average is calculated.

[0023] This correlation coefficient is then approximated by its mean directly on a logarithmic scale. Thus, the network operator does not need to calculate this correlation coefficient at every point in the network. According to the invention, the correlation coefficient is calculated only once for all potentially serving cells two by two. As a result, implementing the method requires less computing power in network planning and optimization tools.

[0024] Advantageously, it is possible to reserve a limited memory space to record once and for all the average values ​​of the correlation coefficients between the SINRs of the selected cells (potentially serving cells).

[0025] According to a particular aspect, the method according to the invention further comprises a calculation of an average and a variance of said at least two signal-to-interference-plus-noise ratios determined on a logarithmic scale. The determination of the correlation coefficient between said at least two signal-to-interference-plus-noise ratios determined on a logarithmic scale is then carried out from the calculated averages and variances of said at least two signal-to-interference-plus-noise ratios determined on a logarithmic scale.

[0026] As will be seen in more detail later in this document, the characteristic parameters of the distributions (mean and variance) of the signal-to-interference-plus-noise ratios for cells are calculated, for example, using the Schwartz-Yeh technique described in the article by C.-L. Ho, "Calculating the mean and variance of power sums with two log-normal components," IEEE Trans. Veh. Technol., vol. 44, no. 4, pp. 756-762, 1995 .

[0027] According to a particular aspect of the invention, the correlation coefficient is determined according to the formula: τ ij = E SINR i dB SINR j dB − q i q j s i s j Or: SINR i And SINR j respectively designate said at least two signal to interference plus noise ratios determined on the logarithmic scale of said at least two cells, qi And qj respectively designate said averages of said at least two signal-to-interference plus noise ratios determined on a logarithmic scale, yesAnd sj respectively designate said variances of said at least two signal-to-interference plus noise ratios determined on a logarithmic scale.

[0028] According to another particular aspect of the invention, the estimation of the characteristic parameters comprises a calculation of at least some of the elements belonging to the group comprising: an average of said calculated maximum; a variance of said calculated maximum; from the determined correlation coefficient, then approximated by its average and the averages and variances of said at least two signal to interference plus noise ratios determined on a logarithmic scale.

[0029] Thus, the technique of the invention makes it possible to calculate the expressions of the mean and variance on the logarithmic scale of the maximum SINRs. Knowing the mean and variance allows for finer optimization of the operator's network coverage.

[0030] According to one embodiment, the average of the calculated maximum is calculated according to the formula: q z = q i Φ q i − q j θ + q j Φ q j − q i θ + θφ q i − q j θ Or : θ = s i 2 + s j 2 − 2 τ ij s i s j , f (.) is the probability density function of the standard normal distribution, Φ(.) is the distribution function of the standard normal distribution, qi And qj respectively designate said averages of said at least two signal-to-interference plus noise ratios determined on a logarithmic scale, s i 2 And s j 2 respectively denote said variances of said two signal-to-interference plus noise ratios determined on a logarithmic scale, and t ij is said correlation coefficient between said two signal to interference plus noise ratios determined on a logarithmic scale.

[0031] According to one embodiment, the variance of the calculated maximum is calculated according to the formula: s z 2 = s i 2 + q i 2 Φ q i − q j θ + s j 2 + q j 2 Φ q j − q i θ + q i + q j θφ q i − q j θ − q z 2 Or : θ = s i 2 + s j 2 − 2 τ ij s i s j , f(.) is the probability density function of the standard normal distribution, Φ(.) is the distribution function of the standard normal distribution, qi And qj respectively designate said averages of said two signal-to-interference plus noise ratios determined on a logarithmic scale, s i 2 And s j 2 respectively designate said variances of said two signal-to-interference plus noise ratios determined on a logarithmic scale, t ij is said correlation coefficient between said two signal-to-interference plus noise ratios determined on a logarithmic scale, and qz is said average of said calculated maximum.

[0032] According to a particular aspect, the determination of the maximum comprises a calculation between said at least two signal to interference plus noise ratios determined on a logarithmic scale, this calculation comprising, where appropriate, a determination, among said at least two selected cells, of at least two cells having between them an average power difference greater than or equal to a predetermined threshold (λ).

[0033] Advantageously, among the selected cells that can potentially be servers, it is possible not to take into account cells offering a very low average reception power compared to cells offering high average reception powers. Indeed, when the average reception power for a signal emitted by a cell k is much lower than the average power offered by a cell ioffering the highest average reception power among the selected potentially serving cells, the probability that the SINR of cell k is the maximum is negligible. Thus, in order to simplify the calculation of the maximum SINRs, it is wise to disregard this cell k. For this, a threshold λ (greater than 0; in dB) is set for which if a cell offers an average reception power that is more than λ away from the highest average reception power, then its SINR is not taken into consideration for the calculation of the maximum SINRs.

[0034] The invention also relates to a computer program product comprising program code instructions for implementing a method for estimating parameters characteristic of a reception quality at a location of a cellular radiocommunication network as described previously, when executed by a processor.

[0035] The invention also relates to a recording medium readable by a computer on which is recorded a computer program comprising program code instructions for executing the steps of the method for estimating parameters characteristic of a reception quality at a location of a cellular radiocommunication network according to the invention as described above.

[0036] Such a recording medium may be any entity or device capable of storing the program. For example, the medium may include a storage medium, such as a ROM, for example a CD ROM or a microelectronic circuit ROM, or a magnetic recording medium, for example a USB flash drive or a hard disk.

[0037] On the other hand, such a recording medium may be a transmissible medium such as an electrical or optical signal, which may be conveyed via an electrical or optical cable, by radio or by other means, so that the computer program contained therein is remotely executable. The program according to the invention may in particular be downloaded over a network, for example the Internet.

[0038] Alternatively, the recording medium may be an integrated circuit in which the program is incorporated, the circuit being adapted to execute or to be used in the execution of the aforementioned method of estimating parameters characteristic of a reception quality at a location of a cellular radiocommunication network.

[0039] The invention also relates to a method for planning the deployment of a cellular radiocommunication network, which implements an estimation of characteristic parameters of a reception quality at a location of said network, according to the method described previously, and a determination of network planning parameters as a function of the estimated characteristic parameters.

[0040] Such a process can, for example, be implemented in planning tools such as Merit / Acp ®< or Atoll ®< for example.

[0041] It also relates to a method for optimizing operating parameters of a cellular radiocommunication network, which implements an estimation of characteristic parameters of a reception quality at a location of said network, according to the method described previously, and a determination of optimized operating parameters of the network as a function of the estimated characteristic parameters.

[0042] Such a process can be integrated into CSON ® type optimization tools.

[0043] The invention also relates to a method for monitoring the performance of a cellular radiocommunication network, which implements an estimation of characteristic parameters of a reception quality at a location of said network, according to the method described previously, and an estimation of at least one performance criterion of the network as a function of the estimated characteristic parameters.

[0044] The invention also relates to a system for planning the deployment of a cellular radiocommunication network, which comprises a processor configured to execute the steps of the method for estimating characteristic parameters of a reception quality at a location of said network, as described previously, and to determine network planning parameters as a function of the estimated characteristic parameters.

[0045] The invention also relates to a system for optimizing operating parameters of a cellular radiocommunication network, which comprises a processor configured to execute the steps of the method for estimating parameters characteristic of a reception quality at a location of said network, as described previously, and to determine optimized operating parameters of the network as a function of the estimated characteristic parameters.

[0046] The invention finally relates to a system for monitoring the performance of a cellular radiocommunication network, which comprises a processor configured to execute the steps of the method for estimating characteristic parameters of a reception quality at a location of said cellular radiocommunication network as described previously and to analyze a performance of the network as a function of the estimated characteristic parameters. Presentation of figures

[0047] Other aims, characteristics and advantages of the invention will appear more clearly on reading the following description, given as a simple illustrative, and non-limiting, example, in relation to the figures, among which: [ Figure 1 ] presents in schematic form a cellular radiocommunication network to which the estimation method can be applied according to different embodiments of the invention; [ Figure 2 ] schematically illustrates the existence of a common propagation zone for correlated cells in the network of the Figure 1 ; [ Figure 3 ] describes in flowchart form the main steps of the estimation method according to one embodiment of the invention; [ Figure 4 ]presents in the form of a histogram the absolute value of the error in calculating the average SINR perceived by the user at a location of interest, on a logarithmic scale; [ Figure 5 ] presents in schematic form the hardware structure of a system for monitoring the performance of a cellular radiocommunication network of the Figure 1 in one embodiment of the invention. Detailed description of the methods of implementation of the invention

[0048] The general principle of the invention is based on an estimation of parameters characteristic of the quality of reception of a useful signal at any point in a cellular radiocommunication network, based on a realistic hypothesis consisting of considering that several cells are likely to play the role of server cell, in a given location, due to the random phenomenon of "shadowing".

[0049] The proposed solution allows to calculate the expressions of the mean and variance on the logarithmic scale of the "real" SINR, i.e. perceived by a user terminal, at any point of a cellular radiocommunication network. Knowing the mean and variance of the SINR therefore allows for more precise optimization of the coverage of an operator's network.

[0050] For the record, and as illustrated by the Figure 1 , a cellular radiocommunication network 1, or mobile network, is composed of a network of relay antennas (or base stations) 2 1 to 2 N (N=4 in the example shown), each covering a portion of territory delimited 3 1 to 3 P (P=4 in the example shown), commonly called a cell (represented schematically in hexagonal form on the Figure 1 ), and carrying communications in the form of radio waves to and from user terminals located in the corresponding cell.

[0051] To access the services offered by the network operator (voice or data), a user terminal must therefore be located within the coverage area of ​​a 2 i relay antenna. This has a limited range, and only covers a restricted territory around it, called a cell. To cover as much territory as possible and ensure that user terminals always have access to the services offered, operators deploy thousands of 3 i cells, each of them equipped with 2 i antennas, ensuring that their coverage areas overlap, so as to provide as complete a coverage of the territory as possible.

[0052] Indeed, if a user terminal is able to correctly decode the signal intended for it for a given service, then this service is accessible with sufficient quality at the location of the user terminal. The coverage area for this service is the set of locations where the SINR determined for the user terminal is greater than a given threshold. The operator sizes and configures its network according to its objectives including coverage, for example 99% of the territory must be covered for the voice service, 95% for the video service, etc. Since coverage cannot be measured at every location in the network, the precise estimation of the SINR and its characteristic parameters is crucial to ensure the operator's coverage objectives.

[0053] It should be noted that the size of the cells depends on multiple criteria such as the type of relay antennas used, the relief (plain, mountain, valley, etc.), the location (rural area, urban area, etc.), population density, etc. The size of the 3 i cell is also limited by the range of the user terminals, which must be capable of establishing an uplink link with the relay antenna.

[0054] Furthermore, a 2-cell relay antenna has a limited transmission capacity and can only handle a certain number of simultaneous service access requests. This is why, in cities, where population density is high and the number of communications is significant, cells tend to be numerous and small - spaced a few hundred or even only a few dozen meters apart. In the countryside, where population density is much lower, cell sizes are much larger, sometimes up to several kilometers but very rarely exceeding more than ten kilometers.

[0055] Planning and optimizing the operation of a cellular radiocommunication network 1 are therefore complex and delicate issues for the network operator. They require reliable and precise information on the reception quality that a given configuration of relay antennas and cells can offer at any point in the network. This information can be obtained by knowing the signal-to-interference-plus-noise ratio, or SINR, at any point in the network. However, since the latter cannot be effectively measured at every point in the network, it is important for the operator to have a statistical estimate of this parameter and its variance and mean characteristics. The estimation of the SINR characteristics is then used by the operator in planning tools to optimize radio coverage.

[0056] The technique of the invention aims to propose a method for estimating the SINR at any location of the network, based on the hypothesis that several cells can potentially play the role of server cell at a given point, due to the random phenomenon of "shadowing".

[0057] We focus more particularly in the following, in relation to the Figures 2 and 3 , to describe the estimation of the SINR actually perceived by a user terminal 4 at a location of interest, in the case where it is considered that several cells of the network can play the role of server cell at this location of interest.

[0058] According to a classical approach in the context of the simulation of the radio coverage of a network, it is assumed here that the values ​​of the loads ( r ) of the cells are equal. For the record, the charge ( r) of a cell corresponds to the fraction of resources granted by it to user terminals located in its coverage area.

[0059] First of all, during a step E1, a set is selected comprising at least two cells that can potentially act as a server cell for a user terminal at a given location. In particular, the aim is to select the cells of the communication network offering the highest average reception power of a useful signal at the location of the user terminal 4. In other words, the cells for which the user terminal 4 receives a useful signal are selected. In an example related to the figure 2 , we select from the cells of the communication network, the three cells: 3 i , 3 j , 3 k .

[0060] In another example related to the figure 3, we consider a number M of cells (M being an integer greater than or equal to 1): cell 1, cell 2...up to cell M (respectively noted: CELL 1, CELL 2...CELL M ).

[0061] For the record, the expression of SINR i perceived by a user terminal at a location of interest in the case where its server cell is the cell i East : SINR i = 10 μ i + ε i 10 N + ∑ j = 1 , j ≠ i M ρ j 10 μ j + ε j 10 Or M is the number of cells from which the user terminal located at the location of interest picks up a useful signal, N is the power of the thermal noise, for 1 ≤ i ≤ M, µ i is the average power of the signal received from the cell i . Without loss of generality, we assume that, µ 1 , ≥ µ 2 ≥ ···. ≥ µ M . In other words, we subsequently consider that cell 1 (CELL 1) offers an average reception power greater than cell 2 (CELL 2) which has an average reception power greater than cell 3 (CELL 3) etc...the cell M (CELL M ) therefore having the lowest average reception power among all the cells selected in step E1, e i is a centered normal random variable with variance σ i 2 which refers to “shadowing” and p j denotes the cell charge j. As described previously, it is assumed here that the values ​​of the cell charges are equal, i.e.: r 1 = ··· = p M = p.

[0062] Due to the phenomenon of "shadowing", the server cell is not "frozen" and several cells can act as a server. The server cell of the user terminal at a location of interest is therefore the one that offers the highest received power, and not necessarily the one that offers the highest average received power. In other words, if the cell i is the server cell, then: µ i + e i > µ j + e j (for all j ≠ i ).

[0063] The random variables of "shadowing" of the different cells considered are correlated since they correspond to the impact on the power received by the user terminal, of the obstacles that the signal crosses during its propagation from a relay antenna to the user terminal. These obstacles present in the environment close to the user terminal are therefore the same for the different cells considered. The "shadowing" impacting the path i, j, k (from cell 3) i , 3 j , 3 k and to the user terminal 4) is therefore the sum of two independent Gaussian random variables, one of which x is common to all paths i, j, k to user terminal 4 as shown in Figure 2. In this regard, reference may be made to the work of SS Szyszkowicz, H. Yanikomeroglu, and JS Thompson, "On the feasibility of wireless shadowing correlation models," IEEE Trans. Veh. Technol., vol. 59, no. 9, pp. 4222 . So, we can write that ε i = ε ′ i + ξ , ε j = ε ′ j + ξ ε k = ε ′ k + ξ Or e ' i , e ' j , e ' k And x (1 ≤ i ≠ j ≠ k ≤ M ) are independent normal random variables with zero means and variances σ i ′ 2 = σ i 2 − β 2 , σ j ′ 2 = σ j 2 − β 2 , σ k ′ 2 = σ k ′ 2 − β 2 And β 2< , respectively, where β 2< is the variance of x .

[0064] During a step E2, to calculate the characteristic parameters of the so-called “real” SINR, i.e. measured for the user terminal, in the realistic case where this “shadowing” phenomenon is taken into account, the expressions of the SINRs (SINR 1 , SINR 2 ... SINR) are determined using the previous equation EQ1. M ) measured for each cell in the cell set (CELL 1, CELL 2 ...CELL M ) selected during step E1. In this case, we consider that each cell can potentially play the role of waitress.

[0065] Consequently, if we consider that the cells (CELL 1, CELL 2 ...CELL M ) selected can statistically play the role of server cell, the SINR measured for the user terminal at the location of interest therefore amounts to determining a maximum between all the SINRs (SINR 1, SINR 2 ... SINR M ) measured, that is to say: SINR dB = max SINR 1 dB , SINR 2 dB , … , SINR M dB Or SINR i dB = 10 log 10 SINR i , 1 ≤ i ≤ M ; And SINR dB = 10 log 10 SINR .

[0066] It should be noted that if a cell k offers an average reception power ( µ k ) very low compared to that of the first cell (CELL 1) (in this case, we assume that the first cell has the highest average reception power among the set of cells 1 to M), in other words µ k " µ 1 then the probability that the SINR (SINR k ) measured for cell k corresponds to the maximum SINRs is negligible. Thus, taking into account cell k brings more complexity than precision in the calculation of the maximum SINRs.

[0067] In order to simplify the calculation of the maximum SINRs, it is therefore advisable not to take this cell k into account. Thus, we consider that if µ k < µ 1 - l (dB) where l > 0, the cell is not considered as potentially serving. lis a configurable variable allowing to set a limit on the number of cells to be taken into consideration for the calculation of the maximum SINRs. For example, if we set l = 20dB, then all cells having an average power µ deviated by more than 20dB from the highest average receiving power are not taken into account for the calculation of the maximum SINR (i.e. SINR dB< ), because the possibility of these cells acting as server cells is negligible.

[0068] We can therefore reduce the number of cells to be taken into consideration in the calculation of the maximum SINR. We therefore consider M 0 ≤ M, the number of cells whose average powers are greater than or equal to µ 1 - l in dB.

[0069] Thus, in a step E3, we determine the maximum of the SINRs as follows: SINR dB = max SINR 1 dB , SINR 2 dB , … , SINR M 0 dB . Typically, M0 = 2, 3 or 4 cells. However, we always consider all M cells in the SINR calculation i in equation EQ1. In other words, for the calculation of SINR 1 to SINR M 0 using equation EQ1, we take into account all M cells.

[0070] The study by CE Clark, "The greatest of a finite set of random variables" in Operations Research, Vol. 9, No. 2, 145-162, 1961, allows us to calculate characteristic parameters of mean and variance for the maximum of two correlated normal random variables. Furthermore, it should be noted that the maximum between the two normal variables is approximated by a normal distribution. Thus, for the case of calculating the maximum of the SINRs where M 0 cells are potentially serving, calculating the maximum SINRs on a logarithmic scale amounts to maximizing the quantities two by two S INR i dB , 1 ≤ i ≤ M 0 .

[0071] For example, for M 0 = 4, SINR dB = max max max SINR 1 dB SINR 2 dB , SINR 3 dB , SINR 4 dB

[0072] However, in the study C.E. Clark, "The greatest of a finite set of random variables," in Operations Research, Vol. 9, No. 2, 145-162, 1961 , The correlation coefficients between normal random variables are assumed to be known. On the contrary, in the case of cellular network planning and optimization, the correlation coefficient between the SINRs of two neighboring cells in logarithmic scale is not a known quantity.

[0073] Thus, to be able to calculate the characteristic parameters of mean and variance of the maximum SINR (SINR dB< ), we determine, during a step E4, the characteristic distribution parameters, i.e. the mean and the variance, of SINR i dB by the Schwartz-Yeh technique, described in the article by C.-L. Ho, “Calculating the mean and variance of power sums with two log-normal components,” IEEE Trans. Veh. Technol., vol. 44, no. 4, pp. 756-762, 1995 .

[0074] Indeed, as stated in the article " SINR and rate distributions for downlink cellular networks", IEEE Transactions on UVireless Communications, vol. 19, no. 7, pp. 4604-4616, 2020 , THE SINR i , (where 1 ≤ i ≤ M), are normal random variables in the logarithmic domain, whose mean and variance can be calculated. We then denote by qi , si 2< , respectively the mean and variance of SINR i dB< .

[0075] In order to apply the study CE Clark, "The greatest of a finite set of random variables," in Operations Research, Vol. 9, No. 2, 145-162, 1961 , we determine, in a step E5, from qi , s i 2 , the correlation coefficient between the SINRs of the set of MB cells two by two.

[0076] We recall that the correlation coefficient between SINR i dB And SINR j dB for 1 ≤ i ≠ j ≤ M 0 is: τ ij = E SINR i dB SINR j dB − q i q j s i s j

[0077] However, in order to simplify the determination of this correlation coefficient at the level of network planning and optimization tools, an approximation of this correlation coefficient is given, unlike the study CE Clark, "The greatest of a finite set of random variables," in Operations Research, Vol. 9, No. 2, 145-162, 1961.

[0078] More specifically, the inventors propose to approximate T ij by the average of the possible values ​​that can have t ij knowing the possible values ​​of the metrics: 1) average power in reception of signals emitted by the M 0 cells taken into consideration, 2) value of “shadowing”, 3) correlation between cells and 4) cell charges.

[0079] To do this, during step E5, we generate several Monte Carlo type realizations by varying the different metrics, described above, and at each iteration a new value of t ij , 1 ≤ i ≠ j ≤ M 0 is calculated and stored in a RAM M1 of a network performance monitoring system (respectively of a network deployment planning system or of a network operating parameters optimization system) presented below in connection with the Figure 5 .

[0080] At the end of the iterations, an average of a set of correlation coefficients calculated and recorded is calculated. At the end of this operation, the network performance monitoring system (respectively the network deployment planning system or the network operating parameter optimization system) only records the average correlation coefficients for a pair of cells. The M1 RAM of the network performance monitoring system (respectively the network deployment planning system or the network operating parameter optimization system) therefore only needs a small number of memory slots: only 1 average coefficient for M 0 =2 , 3 average coefficients for M 0 =3, 6 for M 0 =4, 10 for M 0 =5, etc. The method proposed above thus makes it possible to reserve a limited memory space to record once and for all the average values ​​of the correlation coefficients between the SINRs measured for neighboring cells.

[0081] In an example implementation, we simulate a network with M =6 cells. As explained previously, we neglect cells with a low probability of being a waitress, for this we set l = 20 dB. We therefore obtain M 0 =2 , 3 or 4 potentially serving cells. We perform 2000 Monte Carlo-type realizations corresponding to several possible values ​​of the metrics: average power in reception of a signal emitted by the first cell (CELL 1) such that − 100 ≤ μ 1 ≤ − 50 dBm , standard deviation of “shadowing”: 7 ≤ in ≤ 12 dB for 1 ≤ i ≤ M, of 4 ≤ β ≤ 10 dB ? Or β 2< is the variance of x, of the cell charge 0.2 ≤ r ≤ 0.9, average powers in reception of a signal emitted by the other cells knowing that: For M 0 = 2, µ 1 - µ 2 ≤ 20 dB and µ 1 - µ i > 20 dB for 3 ≤ i ≤ 6. For M 0 = 3, µ 1 - µ i ≤ 20 dB for 2 ≤ i ≤ 3 and µ 1 - µ i > 20 dB for 4 ≤ i ≤ 6. For M 0 = 4, µ 1 - µ i ≤ 20 dB for 2 ≤ i ≤ 4 and µ 1 - µ i > 20 dB for 5 ≤ i ≤ 6. We generate 2000 Monte Carlo-type realizations for each value of M 0 ∈ {2,3,4}. The averages of the correlation coefficients for a pair of cells are given in Table 1 below. [Table 1] t 12 t 13 t 14 t 23 t 24 t 34 M 0 =2 -0,728 - - - - - M 0 =3 -0,681 -0,349 - 0,033 - - M 0 =4 -0,595 -0,342 -0,204 -0,038 0,058 0,191 THE Table 1 above expresses the averages of the correlation coefficients noted τ ij for 1 ≤ i ≠ I ≤ M 0 , for different values ​​of M 0 . The same procedure can be carried out for higher values ​​of M 0 .

[0082] These different steps referenced E1 to E5 make it possible to determine, during a step E6, the mean and the variance of the maximum of the SINRs on the logarithmic scale.

[0083] It should be noted that step referenced E3 can be carried out before, after or concurrently with steps referenced E4 and E5.

[0084] In step E6, the mean and variance of the maximum pairwise SINRs are iteratively determined based on the study CE Clark, "The greatest of a finite set of random variables," in Operations Research, Vol. 9, No. 2, 145-162, 1961 , et du coefficient de corrélation determined during step E5.

[0085] For this, we consider a variable Z 1 = max SINR 1 dB SINR 2 dB .

[0086] So, according to CE Clark, "The greatest of a finite set of random variables," in Operations Research, Vol. 9, No. 2, 145-162, 1961 : the average of Z 1 is written: q Z 1 = f q 1 q 2 s 1 s 2 τ 12 the second-order moment of Z 1 is written: E Z 1 2 = g q 1 q 2 s 1 s 2 τ 12 the variance of Z 1 is written: s Z 1 2 = E Z 1 2 − q Z 1 2 and the correlation coefficient between Z 1 and SINR 3 dB are respectively τ Z 1,3 = h q 1 q 2 s 1 s 2 τ 12 τ 13 τ 23 Or f q 1 q 2 s 1 s 2 τ 12 = q 1 Φ q 1 − q 2 θ + q 2 Φ q 2 − q 1 θ + θφ q 1 − q 2 θ g q 1 q 2 s 1 s 2 τ 12 = s 1 2 + q 1 2 Φ q 1 − q 2 θ + s 2 2 + q 2 2 Φ q 2 − q 1 θ + q 1 + q 2 θφ q 1 − q 2 θ h q 1 , q 2 , s 1 , s 2 , τ 12 τ 13 , τ 23 = s 1 τ 13 Φ q 1 − q 2 θ + s 2 τ 23 Φ q 2 − q 1 θ g q 1 q 2 s 1 s 2 τ 12 − f q 1 q 2 s 1 s 2 τ 12 2 θ = s 1 2 + s 2 2 − 2 τ 12 s 1 s 2 , f(.) is the probability density function of the standard normal distribution (reduced centered) and Φ(.) is the distribution function of the standard normal distribution.

[0087] If now we want to calculate the same parameters for Z 2 = max Z 1 SINR 3 dB , just calculate q Z 2 = f ( q Z 1 , q 3 , s Z 1 , s 3 , t Z 1.3) and E Z 2 2 = g q Z 1 q 3 s Z 1 s 3 τ Z 1,3 then deduce s Z 2 2 .

[0088] If we want to calculate the parameters for Z 3 = max Z 2 SINR 4 dB , just calculate q Z 3 = f ( q Z 2 , q 4, s Z 2 , s 4, t Z 2.4) and E Z 3 2 = g q Z 2 q 4 s Z 2 s 4 τ Z 2,4 then deduce s Z 3 2 Or t Z 2.4 = h ( q Z 1 , q 3 , s Z 1 , s 3 , tZ 1.3, t Z 1.4, t 34) and t Z 1.4 = h ( q 1 , q 2 , s 1 , s 2 , t 12, t 14, t 24). And so on until SINR M 0 dB .

[0089] As mentioned earlier, typical values ​​of M 0 are 2, 3 or 4 cells. If: M 0 = 2, then the maximum SINR dB< = Z 1. The mean and variance of SINR dB< are those of Z 1 . M 0 = 3, then the maximum SINR dB< = Z 2. The mean and variance of SINR dB< are those of Z 2 . M 0 = 4, then the maximum SINR dB< = Z 3. The mean and variance of SINR dB< are those of Z 3 .

[0090] Advantageously, the error in the estimation of the correlation coefficient has little impact on the accuracy of the estimation of the characteristic parameters of the SINR on the logarithmic scale (mean and variance). It is therefore possible to use an average correlation coefficient as an approximation of the correlation coefficient, because the error between the actual mean (respectively the variance) and that calculated based on the approximate values ​​of the correlation coefficients is small.

[0091] Indeed, let us consider two correlated normal random variables with respective variances γ 1 2 And γ 2 2 . Either t the correlation coefficient between these two variables. According to equations EQ 4.1 and EQ 4.3, the mean and variance of the maximum of the two variables depend on the coefficient t through quantity α = γ 1 2 + γ 2 2 − 2 τ γ 1 γ 2 .

[0092] Now, be it the an approximate value of t, SO α = γ 1 2 + γ 2 2 − 2 τ ˜ γ 1 γ 2 1 − 2 eγ 1 γ 2 γ 1 2 + γ 2 2 − 2 τ ˜ γ 1 γ 2 , Or e = t - the . When Δ ≃ 1 (where Δ= 1 − 2 eγ 1 γ 2 γ 1 2 + γ 2 2 − 2 τ ˜ γ 1 γ 2 ), we can approximate a by a ≃ γ 1 2 + γ 2 2 − 2 τ ˜ γ 1 γ 2 . In this case, the approximate value of the correlation coefficient can be considered in the calculations.

[0093] In this case, the approximate value of t ij is the average t ij for 1 ≤ i ≠ j ≤ M 0 .

[0094] To assess the accuracy of our averaging when calculating the correlation coefficients, we consider the metric of the relative error on the quantity Δ, i.e. the quantity |Δ - 1|.

[0095] To validate this theoretical approach presented in relation to the Figure 3 ,the inventors of the present patent application simulated a network 1 with six cells, and considered several realizations corresponding to several values ​​of the standard deviation of the “shadowing” and several values ​​of the difference between the average reception powers of the two cells having the highest average reception powers.

[0096] They also varied the difference between the average reception powers with the other interfering cells. In particular, they simulate a network with M = 6 cells and M 0 = 4 potentially serving cells. We consider 3000 Monte Carlo-type realizations corresponding to several values: of the average power in reception of a signal emitted by cell 1 such that -100 ≤ µ 1 ≤ -50 dBm, average powers in reception of signals emitted by cells knowing that µ 1 - µ i < l for 2 ≤ i≤ 4 because M 0 = 4 and µ 1 - µ i > l for 5 ≤ i ≤ 6 with l = 20 dB, of the standard deviation of the “shadowing”: 7 ≤ in ≤ 12 dB for 1 ≤ i ≤ M, of 4 ≤ β ≤ 10 dB, of 0.4 ≤ r ≤ 0.9.

[0097] Since M 0 = 4, we have 6 correlation coefficients to calculate for each realization. In the table below, we give the percentage where the relative error on the calculation of Δ is less than or equal to 15%. [Table 2] Approximation de t 12 Approximation de t 13 Approximation de t 14 Approximation de t 23 Approximation de t 24 Approximation de t 34 |D - 1| < 0.15 98% 96% 91% 80% 82% 80%

[0098] THE Table 2 represents the relative error on the value of Δ. The results in Table 2 justify the approximation by the average of the correlation coefficient since the relative error is low (<0.15) in the majority of cases.

[0099] We now calculate the SINR i measured for cell i. SINR i is the maximum between all SINRs coming from the M 0 potential waitresses for the same experiment scenario.

[0100] We compare the average SINR on the logarithmic scale (denoted by SINR dB) to our theoretical approximation.

[0101] In the Figure 4 , we give the histogram of the absolute value of the error between SINR dB< and the approximate value. We note that the theoretical method according to the invention manages to estimate the maximum of the SINR well since the error on the average does not exceed 1.5dB in the majority of cases.

[0102] We also note a low error in the estimation of the variance of the SINR with the method according to the invention.

[0103] We now present, in relation to the Figure 5 ,the hardware structure of a system for monitoring the performance of a cellular radiocommunication network according to an embodiment of the invention, or of a system for planning the deployment of a cellular radiocommunication network, or of a system for optimizing operating parameters of a cellular radiocommunication network.

[0104] Such a system referenced 5 comprises a unit for estimating parameters characteristic of a reception quality at a location of the cellular radiocommunication network, and a unit for analyzing the performance of the network (respectively a unit for determining network planning parameters or a unit for determining optimized network operating parameters), as a function of the estimated characteristic parameters.

[0105] The term unit can correspond to a software component as well as to a hardware component or a set of hardware and software components, a software component itself corresponding to one or more computer programs or sub-programs or more generally to any element of a program capable of implementing a function or a set of functions.

[0106] More generally, such a network performance monitoring system 5 (respectively network deployment planning system or network operating parameter optimization system) comprises a random access memory M1 (for example a RAM memory), a processing unit 6 equipped for example with a processor, and driven by a computer program, representative of the unit for estimating parameters characteristic of a reception quality at a location of the cellular radiocommunication network, stored in a read-only memory M2 (for example a ROM memory or a hard disk). Upon initialization, the code instructions of the computer program are for example loaded into the random access memory M1 before being executed by the processor of the processing unit 6. The random access memory M1 contains in particular the different variables used in the calculations described above in relation to the Figure 3 .The processor of the processing unit 6 controls the calculation of the means and variances of the signal to interference plus noise ratios of the plurality of potential server cells, the calculation of the correlation coefficient, as well as the calculation of the mean and variance of the SINR on a logarithmic scale, corresponding to the maximum of the SINR ratios dB< .

[0107] The RAM M1 may also contain the results of the calculations carried out by the processor of the processing unit 6. It may provide these results to a network performance analysis unit 7 (respectively a unit for determining network planning parameters or a unit for determining optimized network operating parameters), equipped with a processor and controlled by a computer program. This processor may be the same as that of the processing unit 6, or be separate from it.

[0108] The system 5 also comprises an I / O input / output module 8 making it possible to return to the network operator the results of the network performance analysis carried out by the analysis unit 7 (respectively the results of the determination of the planning parameters carried out by the network planning parameter determination unit 7 or the results of the determination of the optimized operating parameters carried out by the network optimized operating parameter determination unit 7).

[0109] All components M1, M2, 6, 7 and 8 of the system 5 are for example connected by a communication bus 9.

[0110] There Figure 5illustrates only one particular way, among several possible ones, of implementing the network performance monitoring system (respectively the network deployment planning system or the network operating parameters optimization system), so that it carries out the steps of the method detailed above, in relation to the Figures 1 to 3 (in any of the different embodiments, or in a combination of these embodiments). Indeed, these steps can be carried out indifferently on a reprogrammable computing machine (a PC computer, a DSP processor or a microcontroller) executing a program comprising a sequence of instructions, or on a dedicated computing machine (for example a set of logic gates such as an FPGA or an ASIC, or any other hardware module).

[0111] In the case where the network performance monitoring system 5 (respectively the network deployment planning system or the network operating parameter optimization system) is implemented with a reprogrammable computing machine, the corresponding program (i.e. the sequence of instructions) may be stored in a removable storage medium (such as for example a floppy disk, a CD-ROM or a DVD-ROM) or not, this storage medium being partially or totally readable by a computer or a processor.

Claims

1. Method for estimating parameters characteristic of reception quality at a location of a cellular radiocommunication network (1), characterized in that it includes: - a selection (E1) from a set of cells (3 i ) of said network, of at least two cells associated with the highest average reception powers of a useful signal at said location (CELL1, CELL2...CELL M ); - a determination (E2), on a logarithmic scale, of at least two signal-to-interference plus noise ratios at said location for said useful signal received from each of said at least two selected cells (SINR dB 1, SINR dB 2...SINR dB M); - a determination (E5) of a correlation coefficient between said at least two signal to interference plus noise ratios determined on a logarithmic scale, - a determination (E3) of a maximum between said at least two signal to interference plus noise ratios determined on a logarithmic scale; - an estimation (E6) of said parameters characteristic of a reception quality at said location from said maximum and said correlation coefficient.

2. Method for estimating parameters characteristic of a reception quality according to claim 1, characterized in that said determination of said correlation coefficient (E5) comprises an approximation of said correlation coefficient, said approximation being determined from a calculation of an average of a set of correlation coefficients between said at least two signal to interference plus noise ratios determined on a logarithmic scale, and in thatsaid estimation (E6) of said characteristic parameters of a reception quality at said location is made from said maximum and said correlation coefficient approximated from said calculated average.

3. Method for estimating parameters characteristic of a reception quality according to claim 1, characterized in that it further includes: - a calculation (E4) of an average (q(SINR1, SINR2...SINR M )) and a variance (s 2 (SINR1, SINR2...SINR M )) associated with each of said at least two signal-to-interference-plus-noise ratios determined on a logarithmic scale, said determination (E5) of said correlation coefficient between said at least two signal-to-interference-plus-noise ratios determined on a logarithmic scale, being carried out from said means and variances associated with each of said at least two signal-to-interference-plus-noise ratios determined on a logarithmic scale calculated (E4).

4. Method for estimating parameters characteristic of a reception quality according to claim 3, characterized in that said correlation coefficient is determined according to the formula: τ ij = E SINR i dB SINR j dB − q i q j s i s j Or : SINR i And SINR j respectively designate said at least two signal to interference plus noise ratios determined on the logarithmic scale of said at least two cells, q i And q j respectively designate said averages associated with each of said at least two signal-to-interference plus noise ratios determined on a logarithmic scale, s i And s j respectively designate said variances associated with each of said at least two signal to interference plus noise ratios determined on a logarithmic scale.

5. Method for estimating parameters characteristic of a reception quality according to claim 3 or 4, characterized in thatsaid estimation of said characteristic parameters comprises a calculation (E6) of at least some of the elements belonging to the group comprising: - an average of said calculated maximum; - a variance of said calculated maximum; from said determined correlation coefficient, then approximated by the average of said determined correlation coefficient and of said averages and variances associated with each of said at least two signal to interference plus noise ratios determined on a logarithmic scale.

6. Method for estimating parameters characteristic of a reception quality according to claim 5, characterized in that said average of said calculated maximum is calculated according to the formula: q z = q i Φ q i − q j θ + q j Φ q j − q i θ + θφ q i − q j θ Or : θ = s i 2 + s j 2 − 2 τ ij s i s j , φ (.) is the probability density function of the standard normal distribution, Φ(.) is the distribution function of the standard normal distribution, q i And q j respectively designate said averages associated with each of said at least two signal-to-interference plus noise ratios determined on a logarithmic scale, s i 2 And s j 2 respectively designate said variances associated with each of said two signal-to-interference plus noise ratios determined on a logarithmic scale, and T ij is said correlation coefficient between said two signal to interference plus noise ratios determined on a logarithmic scale.

7. Method for estimating parameters characteristic of a reception quality according to claim 6, characterized in that said variance of said calculated maximum is calculated according to the formula: s z 2 = s i 2 + q i 2 Φ q i − q j θ + s j 2 + q j 2 Φ q j − q i θ + q i + q j θφ q i − q j θ − q z 2 Or : θ = s i 2 + s j 2 − 2 τ ij s i s j , φ (.) is the probability density function of the standard normal distribution, Φ(.) is the distribution function of the standard normal distribution, q i And q j respectively designate said averages associated with each of said two signal-to-interference plus noise ratios determined on a logarithmic scale, s i 2 And s j 2 respectively designate said variances associated with each of said two signal-to-interference plus noise ratios determined on a logarithmic scale, t ij is said correlation coefficient between said two signal-to-interference plus noise ratios determined on a logarithmic scale, and q z is said average of said calculated maximum.

8. Method for estimating parameters characteristic of a reception quality according to any one of claims 1 to 7, characterized in thatthe determination (E3) of said maximum comprises a calculation between said at least two signal to interference plus noise ratios determined on a logarithmic scale, said calculation comprising, where appropriate, a determination, among said at least two selected cells, of at least two cells having between them an average power difference greater than or equal to a predetermined threshold (λ).

9. Computer program product comprising program code instructions for implementing a method according to any one of claims 1 to 8, when executed by a processor.

10. Method for planning the deployment of a cellular radiocommunication network, characterized in that itimplements an estimation of characteristic parameters of a reception quality at a location of said network according to any one of claims 1 to 8 and a determination of planning parameters of said network as a function of said estimated characteristic parameters.

11. Method for optimizing operating parameters of a cellular radiocommunication network, characterized in that it implements an estimation of characteristic parameters of a reception quality at a location of said network according to any one of claims 1 to 8, and a determination of optimized operating parameters of said network as a function of said estimated characteristic parameters.

12. Method for monitoring the performance of a cellular radiocommunication network, characterized in that itimplements an estimation of characteristic parameters of a reception quality at a location of said network according to any one of claims 1 to 8, and an estimation of at least one performance criterion of said network as a function of said estimated characteristic parameters.

13. System for planning the deployment of a cellular radiocommunication network, characterized in that it comprises a processor configured to execute the steps of the method for estimating characteristic parameters of a reception quality at a location of said network according to any one of claims 1 to 8 and to determine planning parameters of said network as a function of said estimated characteristic parameters.

14. System for optimizing operating parameters of a cellular radiocommunication network, characterized in that itcomprises a processor configured to execute the steps of the method for estimating characteristic parameters of a reception quality at a location of said network according to any one of claims 1 to 8, and to determine optimized operating parameters of said network as a function of said estimated characteristic parameters.

15. System (5) for monitoring the performance of a cellular radiocommunication network, characterized in that it comprises a processor configured to execute the steps of the method for estimating characteristic parameters of a reception quality at a location of said cellular radiocommunication network according to any one of claims 1 to 8 and to analyze a performance of said network as a function of said estimated characteristic parameters.