ESTIMATING THE CHARACTERISTIC PARAMETERS OF A RECEPTION QUALITY IN A LOCATION OF A CELLULAR RADIOCOMMUNICATION NETWORK FROM AN APPROXIMATION OF CORRELATION COEFFICIENTS

Abstract

A method for estimating the characteristic parameters of a reception quality in a location of a cellular radiocommunication network. This method includes: selecting from a set of cells of the network, at least two cells offering the highest mean reception powers of a useful signal at the location; determining, on the logarithmic scale, at least two signal to interference plus noise ratios at the location for the received useful signal coming from each of the at least two cells selected; determining a correlation coefficient between the at least two signal to interference plus noise ratios determined on the logarithmic scale, determining a maximum between the at least two signal to interference plus noise ratios determined on the logarithmic scale; estimating the characteristic parameters of a reception quality at the location from the maximum and from the correlation coefficient.

Claims

1. A method of estimating the characteristic parameters of a reception quality in a location of a cellular radiocommunication network, wherein the method comprises: selecting from a set of cells of said network, at least two cells offering the highest mean reception powers of a useful signal at said location (CELL.sub.1, CELL.sub.2 . . . CELL.sub.M); determining, on the logarithmic scale, at least two signal to interference plus noise ratios at said location for said received useful signal coming from each of said at least two cells selected (SINR.sup.dB.sub.1, SINR.sup.dB.sub.2 . . . SINR.sup.dB.sub.M); determining a correlation coefficient between said at least two signal to interference plus noise ratios determined on the logarithmic scale; determining a maximum between said at least two signal to interference plus noise ratios determined on the logarithmic scale; and estimating said characteristic parameters of a reception quality at said location from said maximum and from said correlation coefficient.

2. The method of estimating the characteristic parameters of a reception quality according to claim 1, wherein said correlation coefficient is determined from an approximation of a mean of a product of said at least two signal to interference plus noise ratios.

3. The method of estimating the characteristic parameters of a reception quality according to claim 1, wherein said correlation coefficient is determined from: a calculation of at least two signal to interference plus noise ratios only taking into account a single cell generating interferences offering the highest mean reception power of a useful signal at said location; and at least two variances representative of an impact on the mean reception powers of said useful signal, of obstacles that said useful signal encounters during its propagation.

4. The method of estimating the characteristic parameters of a reception quality according to claim 1, wherein the method further comprises: calculating a mean (q (SINR.sub.1, SINR.sub.2 . . . SINR.sub.M)) and a variance (s.sup.2(SINR.sub.1, SINR.sub.2 . . . SINR.sub.M)) of each of said at least two signal to interference plus noise ratios determined on the logarithmic scale, and in that said correlation coefficient is determined according to the formula: ${}_{\mathrm{ij}}=\frac{E\left[{\mathrm{SINR}}_i^{\mathrm{dB}}{\mathrm{SINR}}_j^{\mathrm{dB}}\right]-q_i{q}_j}{s_i{s}_j}$ where: SINR.sub.i and SINR.sub.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.sub.i and q.sub.j respectively designate said means of each of said at least two signal to interference plus noise ratios determined on the logarithmic scale, s.sub.i and s.sub.j respectively designate said variances of said at least two signal to interference plus noise ratios determined on the logarithmic scale.

5. The method of estimating the characteristic parameters of a reception quality according to claim 3, wherein said estimation of said characteristic parameters comprises calculating at least some of the elements belonging to the a group comprising: a mean of said calculated maximum; and a variance of said calculated maximum; from said determined correlation coefficient and from the means and variances of each of said at least two signal to interference plus noise ratios determined on the logarithmic scale.

6. The method of estimating the characteristic parameters of a reception quality according to claim 5, wherein said mean of said calculated maximum is calculated according to the formula: $q_z=q_i\left(\frac{q_i-q_j}{}\right)+q_j\left(\frac{q_j-q_i}{}\right)+\left(\frac{q_i-q_j}{}\right)$ where: $=\sqrt{s_i^2+s_j^2-2{}_{\mathrm{ij}}{s}_i{s}_j},$ (.) is the probability density function of the standard centered normal law, (.) is the distribution function of the standard normal law, q.sub.i and q.sub.j respectively designate said means of each of said at least two signal to interference plus noise ratios determined on the logarithmic scale, s.sub.i.sup.2 and s.sub.j.sup.2 respectively designate said variances of each of said two signal to interference plus noise ratios determined on the logarithmic scale, and .sub.ij is said correlation coefficient between said two signal to interference plus noise ratios determined on the logarithmic scale.

7. The method of estimating the characteristic parameters of a reception quality according to claim 6, wherein said variance of said calculated maximum is calculated according to the formula: $s_z^2=\left(s_i^2+q_i^2\right)\left(\frac{q_i-q_j}{}\right)+\left(s_j^2+q_j^2\right)\left(\frac{q_j-q_i}{}\right)+\left(q_i+q_j\right)\left(\frac{q_i-q_j}{}\right)-{\left(q_z\right)}^2$ where: $=\sqrt{s_i^2+s_j^2-2{}_{\mathrm{ij}}{s}_i{s}_j},$ (.) is the probability density function of the standard centered normal law, (.) is the distribution function of the standard normal law, q.sub.i and q.sub.j respectively designate said means of each of said two signal to interference plus noise ratios determined on the logarithmic scale, s.sub.i.sup.2 and s.sub.j.sup.2 respectively designate said variances of each of said two signal to interference plus noise ratios determined on the logarithmic scale, and .sub.ij is said correlation coefficient between said two signal to interference plus noise ratios determined on the logarithmic scale, and q.sub.z is said mean of said calculated maximum.

8. The method of estimating the characteristic parameters of a reception quality according to any one of claim 1, wherein said determination of said maximum between said at least two signal to interference plus noise ratios determined on the logarithmic scale, comprises, where applicable, determining, from said at least two cells selected, at least two cells mutually having an mean power difference greater than or equal to a predetermined threshold.

9. A processing circuit comprising a processor and a memory, the memory storing program code instructions of a computer program a to execute the method according to of claim 1, when the computer program is executed by the processor.

10. A method of planning the deployment of a cellular radiocommunication network, wherein the method implements an estimation of characteristic parameters of a reception quality in a location of said network according to claim 1 and a determination of parameters for planning said network depending on said estimated characteristic parameters.

11. A method of optimizing the operating parameters of a cellular radiocommunication network, wherein the method implements estimating characteristic parameters of a reception quality in a location of said network according to claim 1 and a determination of optimized operating parameters of said network depending on said estimated characteristic parameters.

12. A method of monitoring the performance of a cellular radiocommunication network, wherein the method implements estimating characteristic parameters of a reception quality in a location of said network according to claim 1 and an estimation of at least one performance criterion of said network depending on said estimated characteristic parameters.

13. A system for planning the deployment of a cellular radiocommunication network, wherein the system comprises a processor configured to execute the steps of the method for estimating the characteristic parameters of a reception quality in a location of said network according to claim 1 and for determining the parameters for planning said network depending on said estimated characteristic parameters.

14. A system for optimizing the operating parameters of a cellular radiocommunication network, wherein the system comprises a processor configured to execute the steps of the method for estimating the characteristic parameters of a reception quality in a location of said network according to claim 1 and for determining the optimized operating parameters of said network depending on said estimated characteristic parameters.

15. A system for monitoring the performance of a cellular radiocommunication network, wherein the system comprises a processor configured to execute the steps of the method for estimating the characteristic parameters of a reception quality in a location of said cellular radiocommunication network according to claim 1 and for analyzing a performance of said network depending on said estimated characteristic parameters.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Other aims, features and advantages of the development will become apparent upon reading the following description, given simply by way of illustrative and non-limiting example, with reference to the figures, in which:

[0074] FIG. 1 schematically shows a cellular radiocommunication network to which the estimation method may be applied according to various embodiments of the development;

[0075] FIG. 2 schematically illustrates the existence of a common propagation area for the correlated cells of the network of FIG. 1;

[0076] FIG. 3 describes in the form of a flow chart the main steps of the estimation method according to a first embodiment of the development;

[0077] FIG. 4 describes in the form of a flow chart the main steps of the estimation method according to a second embodiment of the development;

[0078] FIG. 5 shows in the form of a bar chart the absolute value of the error on the calculation of the mean of the SINR perceived by the user at the location of interest, on the logarithmic scale, according to the embodiment of the development in connection with FIG. 3;

[0079] FIG. 6 shows in the form of a bar chart the absolute value of the error on the calculation of the mean of the SINR perceived by the user at the location of interest, on the logarithmic scale, according to the embodiment of the development in connection with FIG. 4;

[0080] FIG. 7 schematically shows the hardware structure of a system for monitoring the performance of a cellular radiocommunication network of FIG. 1 in one embodiment of the development.

DETAILED DESCRIPTION OF CERTAIN ILLUSTRATIVE EMBODIMENTS

[0081] The general principle of the development is based on estimating the characteristic parameters of the reception quality of a useful signal at any point of a cellular radiocommunication network, based on a realistic hypothesis consisting in considering that a plurality of cells are likely to act as serving cells, in a given location, due to the random shadowing phenomenon.

[0082] The proposed solution makes it possible to calculate the expressions of the mean and of the variance on the logarithmic scale of the actual SINR, that is to say measured for a user terminal, at any point of a cellular radiocommunication network. Knowing the mean and the variance of the SINR makes it possible to more accurately optimize the coverage of the network of an operator.

[0083] As a reminder, and as illustrated in FIG. 1, a cellular radiocommunication network 1, or mobile network, is made up of a network of relay antennas (or base stations) 2.sub.lto 2.sub.N (N=4 in the example shown), each covering a delimited portion of territory 3.sub.l to 3.sub.P (P=4 in the example shown), commonly called a cell (schematically shown in hexagonal form in FIG. 1), and routing the communications in the form of radio waves to and from user terminals located in the corresponding cell.

[0084] In order to access the services proposed by the operator of the network (voice or data), a user terminal must therefore be located in the coverage area of a relay antenna 2.sub.i. This has a limited range, and only covers a restricted territory around it, called a cell. In order to cover a maximum of territory and make sure that the user terminals always have access to the proposed services, the operators deploy thousands of cells 3.sub.i, each of them being equipped with antennas 2.sub.i making sure that their coverage areas overlap, so as to offer a meshing of the territory that is as comprehensive as possible.

[0085] Indeed, if a user terminal is able to correctly decode the signal that is intended for it for a given service, then this service is accessible with a sufficient quality at the place of the user terminal. The coverage area for this service is all the places where the SINR determined for the user terminal is greater than a given threshold. The operator dimensions and configures its network according to its objectives including the coverage, for example 99% of the territory must be covered for the voice service, 95% for the video service, etc. As the coverage cannot be measured at any place of the network, the accurate estimation of the SINR and of its characteristic parameters is decisive for ensuring the coverage objectives of the operator.

[0086] It will 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 installation site (rural area, urban area, etc.), the population density, etc. The size of the cell 3; is also limited by the range of the user terminals, which must be able to establish an uplink with the relay antenna.

[0087] Moreover, a relay antenna 2.sub.i has a limited transmission capacity, and can only process a certain number of simultaneous requests to access the service. This is why, in cities, where the population density is high and the number of communications is significant, the cells tend to be high in number and small in dimensionspaced a few hundreds or even only a few tens of meters apart. In the countryside, where the population density is much lower, the size of the cells is much larger, sometimes reaching up to several kilometers but only very rarely exceeding more than ten kilometers.

[0088] The planning and the optimization of the operation of a cellular radiocommunication network 1 are therefore complex and delicate issues for the operator of the network. They require having reliable and accurate information relating to the reception quality that a given configuration of relay antennas and cells may offer at any point of the network. This information can be obtained by knowing the signal to interference plus noise ratio, or SINR, at any point of the network. However, as the latter cannot be measured effectively at any point of the network, it is important for the operator to be able to have a statistical estimation of this parameter and of its variance and mean characteristics. The estimation of the characteristics of the SINR is then used by the operator in planning tools in order to optimize the radio coverage.

[0089] The aim of the technique of the development is to propose a method for estimating the SINR at any location of the network, starting from the hypothesis that a plurality of cells may potentially act as serving cells in any given point, due to the random shadowing phenomenon.

[0090] More particularly hereinafter, with reference to FIGS. 2 to 4, it is endeavored to describe the estimation of the SINR actually measured for a user terminal 4 at a location of interest, in the case where it is considered that a plurality of cells of the network may act as serving cells in this location of interest.

[0091] According to a conventional approach in the context of simulating the radio coverage of a network, here it is assumed that the values of the loads (p) of the cells are equal. As a reminder, the load () of a cell corresponds to the fraction of resources granted by it to user terminals located in its coverage area.

[0092] First of all, in connection with FIG. 3, a first embodiment according to the development is described. During a step E31, a set is selected comprising at least two cells that can potentially act as serving cells for a user terminal in a given location. In particular, it is sought to select the cells of the communication network that offer the highest mean reception power of a useful signal at the location of the user terminal 4. In other terms, the cells for which the user terminal 4 captures a useful signal are selected. In one example in connection with FIG. 2, the following three cells are selected from the cells of the communication network: 3.sub.i, 3.sub.j, 3.sub.k.

[0093] In another example in connection with FIG. 3, or FIG. 4, a number M of cells (M being an integer greater than or equal to 1) is considered: cell 1, cell 2 . . . up to the cell M (respectively denoted: CELL.sub.1, CELL.sub.2 . . . CELL.sub.M).

[0094] As a reminder, the expression of the SINR/measured for a user terminal at a location of interest in the case where its serving cell is the cell i is:

[00006]$\begin{array}{cc}{\mathrm{SINR}}_i=\frac{{10}^{\frac{{}_i+{}_i}{10}}}{N+{.Math.}_{j=1,ji}^M{}_j{10}^{\frac{{}_j+{}_j}{10}}}& \left(\mathrm{EQ1}\right)\end{array}$

[0095] where

[0096] Mis the number of cells from which the user terminal located in the location of interest captures a useful signal,

[0097] N is the power of the thermal noise,

[0098] for 1iM, .sub.i is the mean power of the signal received from the cell i. Without loss of generality, it is assumed that, 12 . . . .sub.M. In other words, it is subsequently considered that the cell 1 (CELL.sub.1) offers an mean reception power greater than the cell 2 (CELL.sub.2) that has an mean reception power greater than the cell 3 (CELL.sub.3) etc. . . . the cell M (CELL.sub.M) therefore offering the lowest mean reception power from the set of cells selected in step E31, [0099] .sub.i is a centered normal random variable of variance .sub.i.sup.2 that designates the shadowing and [0100] .sub.j designates the load of the cell j. As described above, here it is assumed that the values of the loads of the cells are equal, that is to say: .sub.1= . . . =.sub.M=.

[0101] Due to the shadowing phenomenon, the serving cell is not frozen and a plurality of cells may statistically act as serving cells. The serving cell of the user terminal at the location of interest is therefore that which offers the highest received power, and not necessarily that which offers the highest mean reception power. In other words, if the cell i is the serving cell, then: .sub.i+.sub.i>.sub.j+.sub.j(for any ji).

[0102] The random shadowing variables of the various cells considered are correlated since they correspond to the impact on the power received by the user terminal, obstacles that the signal overcomes during its propagation from a relay antenna to the user terminal. These obstacles present in the immediate environment of the user terminal are consequently the same for the various cells considered. The shadowing impacting the path i, j, k (coming from the cell 3.sub.i, 3.sub.j, 3.sub.k and going to the user terminal 4) is therefore the sum of two random Gaussian variables independent of one another of which one is common to all the paths i, j, k going to the user terminal 4 as shown in FIG. 2. In this respect, reference can be made to the works of S. S. Szyszkowicz, H. Yanikomeroglu, and J. S. Thompson, On the feasibility of wireless shadowing correlation models, IEEE Trans. Veh. Technol., vol. 59, no. 9, pp. 4222.

[0103] Thus, it can be written that

[00007]${}_i={}_i^{}+,$${}_j={}_j^{}+$${}_k={}_k^{}+$

[0104] Where .sub.i, .sub.j, .sub.k and (1ijkM) are independent normal random variables of zero means and of variances .sub.i.sup.2=.sub.i.sup.2.sup.2, .sub.j.sup.2=.sub.j.sup.2, .sub.k.sup.2.sub.k.sup.2.sup.2 and .sup.2, respectively, where .sup.2 is the variance of .

[0105] During a step E32, for calculating the characteristic parameters of the so-called actual SINR, that is to say measured for the user terminal, in the realistic case where this shadowing phenomenon is taken into account, using the preceding equation EQ1 the expressions of the SINRs (SINR.sub.1, SINR.sub.2 . . . SINR.sub.M) measured for each cell of the set of cells (CELL.sub.1, CELL.sub.2 . . . CELL.sub.M) selected during the step E31 are determined. In this case, it is considered that each cell may potentially act as serving cells.

[0106] Consequently, if it is considered that the cells (CELL.sub.1, CELL.sub.2 . . . CELL.sub.M) selected may statistically act as serving cells, the SINR measured for the user terminal at the location of interest therefore amounts to determining a maximum between all the SINRs (SINR.sub.1, SINR.sub.2 . . . SINR.sub.M) measured, that is to say:

[00008]${\mathrm{SINR}}^{dB}=\max \left({\mathrm{SINR}}_1^{dB},{\mathrm{SINR}}_2^{dB},.Math.,{\mathrm{SINR}}_M^{dB}\right)$$\mathrm{where}$${\mathrm{SINR}}_i^{dB}=10{\log }_{10}{\mathrm{SINR}}_i,$$1iM;$$\mathrm{and}$${\mathrm{SINR}}^{dB}=10{\log }_{10}\mathrm{SINR}.$

[0107] It should be noted that if a cell k offers a very low mean reception power (.sub.k) relative to that of the first cell (CELL.sub.1) (in the present case, it is assumed that the first cell has the highest mean reception power from the set of cells 1 to M), in other words .sub.k<.sub.1, then the probability that the SINR (SINRA) measured for the cell k corresponding to the maximum of the SINRs is negligible. Thus, taking the cell k into account adds more complexity than accuracy in the calculation of the maximum of the SINRs.

[0108] In order to simplify the calculation of the maximum of the SINRs, it is therefore judicious to not take this cell k into account. Thus, it is considered that if .sub.k<.sub.1(dB) where >0, the cell is not considered as potentially a serving cell. is a configurable variable making it possible to place a limit on the number of cells to be taken into consideration for calculating the maximum of the SINRs. For example, if =20 dB is set, then all the cells having an mean u deviating by more than 20 dB from the highest mean reception power are not taken into consideration for calculating the maximum SINR (that is to say SINRdB), because the possibility that these cells act as serving cells is negligible.

[0109] Therefore, the number of cells to be taken into consideration in the calculation of the maximum SINR can be reduced. Therefore, it is considered that M.sub.0M is the number of cells of which the mean powers are greater than or equal to .sub.1 in dB.

[0110] Thus, in a step E33, the maximum of the SINRs is determined as follows:

[00009]${\mathrm{SINR}}^{dB}=\max \left({\mathrm{SINR}}_1^{dB},{\mathrm{SINR}}_2^{dB},.Math.,{\mathrm{SINR}}_{M_0}^{dB}\right).$

[0111] Typically, M.sub.0=2, 3 or 4 cells.

[0112] However, the set of cells M is always considered in the calculation of the SINR.sub.i in the equation EQ1. In other terms, for calculating the SINR.sub.1 to SINR.sub.M using the equation EQ1, all the M cells are taken into account.

[0113] The C. E. Clark study, The greatest of a finite set of random variables in Operations Research, Vol. 9, No. 2, 145-162, 1961, enables us to calculate the mean and variance characteristic parameters for the maximum of two correlated normal random variables. Moreover, it should be noted that the maximum between the two normal variables is approximated by a normal distribution. Thus, for the case of the calculation of the maximum of the SINRs where M.sub.0 cells are potentially serving cells, calculating the maximum of the SINRs on the logarithmic scale amounts to maximizing the quantities SINR.sub.i.sup.dBtwo by two, 1iM.sub.0.

[0114] For example, for M.sub.0=4,

[00010]${\mathrm{SINR}}^{dB}=\max \left(\max \left[\max \left({\mathrm{SINR}}_1^{dB},{\mathrm{SINR}}_2^{dB}\right),{\mathrm{SINR}}_3^{dB}\right],{\mathrm{SINR}}_4^{dB}\right)$

[0115] However, in the C. E. Clark study, The greatest of a finite set of random variables, in Operations Research, Vol. 9, No. 2, 145-162, 1961, the correlation coefficients between the normal random variables are assumed known. On the contrary, in the case of planning and optimizing the cellular network, the correlation coefficient between the SINRs of two neighboring cells on the logarithmic scale is not a known quantity.

[0116] It is reminded that the correlation coefficient between SINR.sub.i.sup.DB and SINR.sub.j.sup.dB for 1ijM.sub.0 is:

[00011]$\begin{array}{cc}{}_{ij}=\frac{E\left[{\mathrm{SINR}}_i^{dB}{\mathrm{SINR}}_j^{dB}\right]-q_i{q}_j}{s_i{s}_j}& \left(\mathrm{EQ2}\right)\end{array}$

[0117] In order to be able to apply the C. E. Clark study, The greatest of a finite set of random variables, in Operations Research, Vol. 9, No. 2, 145-162, 1961, in order to calculate the mean and variance characteristic parameters of the maximum SINR (SINR.sup.dB), first of all the correlation coefficient is determined for one or more pairs of cells.

[0118] For this purpose, during a step E34, the characteristic parameters of distribution, that is to say the mean and the variance, of SINR.sub.i.sup.dB are determined 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. Indeed, as indicated in the article SINR and rate distributions for downlink cellular networks, IEEE Transactions on Wireless Communications, vol. 19, no. 7, pp. 4604-4616, 2020, the SINR.sub.i, (where 1iM), are normal random variables in the logarithmic domain, of which the mean and the variance can be calculated. It is then noted by q.sub.i, s.sub.i.sup.2, respectively the mean and the variance of SINR.sub.i.sup.dB.

[0119] Subsequently, the mean of the product E[SINR.sub.i.sup.dB SINR.sub.j.sup.dB], 1ijM.sub.0 is determined during a step E35. The following is then defined:

[00012]${\mathrm{SINR}}_i=\frac{10\frac{{}_i+{}_i^{}}{10}}{\mathrm{10}\frac{{}_j+{}_j^{}}{10}+Y_{\mathrm{ij}}}$${\mathrm{SINR}}_j=\frac{10\frac{{}_j+{}_j^{}}{10}}{\mathrm{10}\frac{{}_i+{}_i^{}}{10}+Y_{ij}}$$\mathrm{Where}$$Y_{ij}=N10^{\frac{}{10}}+{.Math.}_{k=1}^{M}10\frac{{}_k+{}_k^{}}{10}-10\frac{{}_i+{}_i^{}}{10}-10\frac{{}_j+{}_j^{}}{10}.$

[0120] Y.sub.ij is the sum of (M-1) log-normal random variables, therefore Y.sub.ij is also log-normal.

[0121] On the logarithmic scale, it is possible to define a variable y=10 log(Y.sub.ij). y is a normal random variable. Thus, the characteristic parameters of y are calculated based on 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. .sub.Y denotes (its mean) and .sub.Y.sup.2 (its variance). I.e. x.sub.i=.sub.i+.sub.i and .sub.j=.sub.j+.sub.j.

[0122] x.sub.i, x.sub.j and y are independent, then: )

[00013]$\begin{array}{cc}E\left[{\mathrm{SINR}}_i^{dB}{\mathrm{SINR}}_j^{dB}\right]={}_i{}_j-E\left[x_i{A}_i\right]-E\left[x_j{A}_j\right]+E\left[A_i{A}_j\right]& \left(\mathrm{EQ3}\right)\end{array}$$\mathrm{where}$$A_i=10\log \left(10\frac{x_i+{}_{dB}}{10}+10^{\frac{y}{10}}\right),$$A_j=10\log \left(10\frac{x_j+{}_{dB}}{10}+10^{\frac{y}{10}}\right)$$\mathrm{and}$${}_{dB}=10\log \left(\right).$

[0123] It can then be approximated:

[00014]$\{\begin{array}{cc}{A}_i\left(x_i+{}_{dB}\right)& \mathrm{if}{}_i+{}_{dB}-{}_{Y}d\\ A_{i}y& \mathrm{if}{}_Y-{}_i-{}_{dB}d\\ A_{i}0.5\left(x_i+y\right)+c& \mathrm{if}.Math.{}_i+{}_{dB}-{}_Y.Math.<d\end{array}$$\mathrm{Where}$$c=0.5{}_{dB}+10\frac{\ln \left(2\right)}{\ln \left(10\right)}$

and d>0 is a configurable real number.

[0124] If the difference between the mean of x.sub.j+.sub.dB and that of y is greater than or equal to d, it is estimated that

[00015]$10^{\frac{x_j+{}_{dB}}{10}}{10}^{\frac{y}{10}}.$

[0125] Indeed, when

[00016]$10^{\frac{x_j+{}_{dB}}{10}}{10}^{\frac{y}{10}}$

(i.e. then .sub.i+.sub.dB.sub.Y), A.sub.ix.sub.i+.sub.dB. Inversely, if

[00017]$10\frac{x_j+{}_{dB}}{10}{10}^{\frac{y}{10}}$$\left(i.e.\mathrm{then}{}_i+{}_{dB}{}_Y\right),$$A_i{x}_i.$$\mathrm{Otherwise},$$A_i=x_i+{}_{dB}+10\log \left(1+10^{\frac{y-x_i-{}_{dB}}{10}}\right).$

[0126] It should be noted that for any x>0, this gives log

[00018]$\left(1+x\right)\frac{\ln \left(2\right)}{\ln \left(10\right)}+0.5\log \left(x\right).$

[0127] Indeed, if a function F(x)=ln(1+x)ln(2)0.5 ln (x) is considered, then the derivative of F is

[00019]$F^{}\left(x\right)=\frac{x-1}{2x\left(1+x\right)}.$

Its vanauon table is:

[0128] Thus, for any x>0, F(x)0. By dividing by ln(10),

[00020]$\frac{F\left(x\right)}{\ln \left(10\right)}0.$

Hence,

[0129] [00021]$\log \left(1+x\right)\frac{\ln \left(2\right)}{\ln \left(10\right)}+0.5\log \left(x\right).$

[0130] By applying this result, this gives,

[00022]$\log \left(1+10^{\frac{y-x_i-{}_{\mathrm{dB}}}{10}}\right)\frac{\ln \left(2\right)}{\ln \left(10\right)}+0.5\frac{y-x_i-{}_{\mathrm{dB}}}{10}.$

[0131] In the case where the deviation between the quantities .sub.i+.sub.dB and .sub.Y is not very high, it is approximated

[00023]$\log \left(1+10^{\frac{y-x_i-{}_{\mathrm{dB}}}{10}}\right)\frac{\ln \left(2\right)}{\ln \left(10\right)}+0.5\frac{y-x_i-{}_{\mathrm{dB}}}{10}.$

[0132] Advantageously, as shown below in connection with FIG. 5, the error on the estimation of the correlation coefficient has little impact on the accuracy for estimating the characteristic parameters of the SINR on the logarithmic scale (mean and variance). In summary:

[00024]$\{\begin{array}{cc}{A}_i\left(x_i+{}_{\mathrm{dB}}\right)& \mathrm{if}{}_i+{}_{\mathrm{dB}}{}_Y\\ A_{i}y& \mathrm{if}{}_i+{}_{\mathrm{dB}}{}_Y\\ A_{i}0.5\left(x_i+y\right)+c& \mathrm{otherwise}\end{array}$$\mathrm{Where}c=0.5{}_{\mathrm{dB}}+10\frac{\ln \left(2\right)}{\ln \left(10\right)}.$

[0133] Hereinafter, it is considered that .sub.i+.sub.dB.sub.Y if the deviation between .sub.i+.sub.dB and .sub.Y y is greater than a fixed value d. This means .sub.i+.sub.dB.sub.Yd where d>0. Thus,

[00025]$\{\begin{array}{cc}{A}_i\left(x_i+{}_{\mathrm{dB}}\right)& \mathrm{if}{}_i+{}_{\mathrm{dB}}-{}_{Y}d\\ A_{i}y& \mathrm{if}{}_Y-{}_i-{}_{\mathrm{dB}}d\\ A_{i}0.5\left(x_i+y\right)+c& \mathrm{if}.Math.{}_i+{}_{\mathrm{dB}}-{}_Y.Math.<d\end{array}$

[0134] The same reasoning applies for the quantity A.sub.j between the random variables x.sub.j and y:

[00026]$\{\begin{array}{cc}{A}_j\left(x_j+{}_{\mathrm{dB}}\right)& \mathrm{if}{}_j+{}_{\mathrm{dB}}-{}_{Y}d\\ A_{j}y& \mathrm{if}{}_Y-{}_j-{}_{\mathrm{dB}}d\\ A_{j}0.5\left(x_j+y\right)+c& \mathrm{if}.Math.{}_j+{}_{\mathrm{dB}}-{}_Y.Math.<d\end{array}$

[0135] Based on these approximations of A.sub.i and A.sub.j, the expressions of E[x.sub.iA.sub.i], E[x.sub.jA.sub.j] and E[A.sub.iA.sub.j] become easy to calculate. Thus, it can be deduced therefrom E[SINR.sub.i.sup.dB SINR.sub.j.sup.dB]. Subsequently, during a step E36, the expression of E[SINR.sub.i.sup.dB SINR.sub.j.sup.dB] is injected into the equation EQ2 above to obtain the correlation coefficient between SINR.sub.i.sup.dB and SINR.sub.j.sup.dB for 1ijM.sub.0.

[0136] It should be noted that [0137] if .sub.ij >1, then .sub.ij=1 [0138] if .sub.ij <1, then .sub.ij=1.

[0139] These various steps referenced E31 to E36 make it possible to determine, during a step E37, the mean and the variance of the maximum of the SINRs on the logarithmic scale.

[0140] It should be noted that the step referenced E33 may be performed before, after or concomitantly with the steps referenced E34 to E36.

[0141] In a step E37, the mean and the variance of the maximum of SINRs are determined iteratively two by two, based on the C. E. Clark study, The greatest of a finite set of random variables, in Operations Research, Vol. 9, No. 2, 145-162, 1961, and on the correlation coefficient determined during the step E36.

[0142] For this purpose, a variable Z.sub.1=max(SINR.sub.1.sup.dB, SINR.sub.2.sup.dB) is considered.

[0143] Then, according to C. E. Clark, The greatest of a finite set of random variables, in Operations Research, Vol. 9, No. 2, 145-162, 1961:

[0144] the mean of Z.sub.1 is written:

[00027]$\begin{array}{cc}{q}_{Z1}=f\left(q_1,q_2,s_1,s_2,{}_{12}\right)& \left(\mathrm{EQ4}\text{.1}\right)\end{array}$

[0145] the 2nd order moment of Z.sub.1,

[00028]$\begin{array}{cc}E\left[Z_1^2\right]=g\left(q_1,q_2,s_1,s_2,{}_{12}\right)& \left(\mathrm{EQ4}\text{.2}\right)\end{array}$

[0146] the variance of Z.sub.1

[00029]$\begin{array}{cc}{s}_{Z1}^2=E\left[Z_1^2\right]-q_{Z1}^2& \left(\mathrm{EQ4}\text{.3}\right)\end{array}$

[0147] and the correlation coefficient between Z.sub.1 and SINR.sub.3.sup.dB are respectively

[00030]$$\begin{gathered} \begin{array}{cc}{}_{Z1,3}=h\left(q_1,q_2,s_1,s_2,{}_{12},{}_{13},{}_{23}\right)\mathrm{where} \\ f\left(q_1,q_2,s_1,s_2,{}_{12}\right)=q_1\left(\frac{q_1-q_2}{}\right)+q_2\left(\frac{q_2-q_1}{}\right)+\left(\frac{q_1-q_2}{}\right) \\ g\left(q_1,q_2,s_1,s_2,{}_{12}\right)=\left(s_1^2+q_1^2\right)\left(\frac{q_1-q_2}{}\right)+\left(s_2^2+q_2^2\right)\left(\frac{q_2-q_1}{}\right)+\left(q_1+q_2\right)\left(\frac{q_1-q_2}{}\right) \\ h\left(q_1,q_2,s_1,s_2,{}_{12}{}_{13},{}_{23}\right)=\frac{s_1{}_{13}\left(\frac{q_1-q_2}{}\right)+s_2{}_{23}\left(\frac{q_2-q_1}{}\right)}{\sqrt{g\left(q_1,q_2,s_1,s_2,{}_{12}\right)-{\left(f\left(q_1,q_2,s_1,s_2,{}_{12}\right)\right)}^2}} \\ =\sqrt{s_1^2+s_2^2-2{}_{12}{s}_1{s}_2},& \left(\mathrm{EQ4}\text{.4}\right)\end{array} \end{gathered}$$

[0148] (.) is the probability density function of the standard centered normal law and

[0149] (.) and the distribution function of the standard normal law.

[0150] If it is now desired to calculate the same parameters for Z.sub.2=max (Z.sub.1, SINR.sub.3.sup.dB ), simply calculate q.sub.z2=f(q.sub.z1, q.sub.3, s.sub.Z1, S3, .sub.Z1,3) and E[Z.sub.2.sup.2]=g(q.sub.z1, q.sub.3, s.sub.Z1, s.sub.3, .sub.Z1,3) then deduce s.sub.22.sup.2 therefrom.

[0151] If it is desired to calculate the parameters for Z.sub.3=max (Z.sub.2, SINR.sub.4.sup.dB), simply calculate qz3=f(q.sub.z2, q.sub.4, s.sub.z2, s.sub.4, .sub.Z2,4) and E[Z.sub.3.sup.2]=g(q.sub.z2, q.sub.4, s.sub.z2, s.sub.4, .sub.Z2,4) then deduce s.sub.Z3.sup.2 therefrom where .sub.z2,4=h(q.sub.z1, s.sub.3, s.sub.Z1, s.sub.3, .sub.Z1,3, .sub.Z1,4, .sub.34) and .sub.z1,4=h(q.sub.1, q.sub.2, s.sub.1, s.sub.2, .sub.12, .sub.14, .sub.24).

[0152] and so on up to SINR.sub.M.sub.o.sup.dB.

[0153] As mentioned above, the typical values of M.sub.0 are 2, 3 or 4 cells. If: M.sub.0=2, then the maximum SINR.sup.dB=Z.sub.1. The mean and the variance of SINR.sup.dB are those of Z.sub.1.

[0154] M.sub.0=3, then the maximum SINR.sup.dB=Z.sub.2. The mean and the variance of

[0155] SINR.sup.dB are those of Z.sub.2.

[0156] M.sub.0=4, then the maximum SINR.sup.dB=Z.sub.3. The mean and the variance of SINR.sup.dB are those of Z.sub.3.

[0157] Advantageously, the error on the estimation of the correlation coefficient has little impact on the accuracy for estimating the characteristic parameters of the SINR on the logarithmic scale (mean and variance). Therefore, it is possible to use a correlation coefficient mean as approximation of the correlation coefficient, because the error between the actual mean (respectively the variance) and that calculated based on the approximated correlation coefficient values is low.

[0158] Indeed, i.e. two correlated normal random variables of respective variances .sub.1.sup.2 and .sub.2.sup.2. I.e. the correlation coefficient between these two variables. According to the equations EQ4.1 and EQ4.3, the mean and the variance of the maximum of the two variables depends on the coefficient by means of the quantity ={square root over (.sub.1.sup.2+.sub.2.sup.2.sub.1.sub.2)}.

[0159] Now, i.e. {circumflex over ()} an approximate value of , then

[00031]$=\sqrt{\left({}_1^2+{}_2^2-2\stackrel{}{}{}_1{}_2\right)\left(1-\frac{2e{}_1{}_2}{{}_1^2+{}_2^2-2\stackrel{}{}{}_1{}_2}\right),}$

[0160] where e={circumflex over ()}. When 1 (where

[00032]$=\sqrt{\left(1-\frac{2e{}_1{}_2}{{}_1^2+{}_2^2-2\stackrel{}{}{}_1{}_2}\right)}),$

can be approximated by {square root over (.sub.1.sup.2+.sub.2.sup.22{circumflex over ()}.sub.1.sub.2)}. In this case, the approximated value of the correlation coefficient can be considered in the calculations.

[0161] In this case, the approximated value of .sub.ij is the mean .sub.ij for 1ijM.sub.0.

[0162] In order to evaluate the accuracy of this approximation during the calculation of the correlation coefficients, the metrics of the relative error on the quantity , i.e. therefore the quantity |1| is considered.

[0163] In order to validate this theoretical approach presented with reference to FIG. 3, the inventors of the present patent application simulated a network 1 with six cells, and considered a plurality of embodiments corresponding to a plurality of values of the standard deviation of the shadowing and a plurality of values of the deviation between the mean reception powers of the two cells having the highest mean reception powers.

[0164] They also varied the deviation between the mean reception powers with the other interfering cells. In particular, they simulate a network with M=6 cells and M0=4 potentially serving cells. We consider 3,000 Monte Carlo type embodiments corresponding to a plurality of values: [0165] the mean reception power of a signal transmitted by the cell 1 such as 100.sub.150 dBm, [0166] the mean reception powers of the signals transmitted by cells knowing that .sub.1.sub.i for 2i4 because M.sub.0=4 and .sub.1.sub.i< for 5i6 with =20 dB, [0167] the standard deviation of the shadowing: 7.sub.i12 dB for 1iM, [0168] 410 dB, [0169] 0.40.9.

[0170] The value of dis set at 7. Since M.sub.0=4, we have 6 correlation coefficients to calculate for each embodiment. In the table below, the percentage is given where the relative error on the calculation is lower than or equal to 15%.

TABLE-US-00001 TABLE 1 Approx- Approx- Approx- Approx- Approx- Approx- imation imation imation imation imation imation of .sub.12 of .sub.13 of .sub.14 of .sub.23 of .sub.24 of .sub.34 | 1| < 97.7% 96.6% 96.7% 75% 79% 82.1% 0.15

[0171] Table 1 shows the relative error on the value of . The results of Table 1 justify the approximation by calculating the bounds of the correlation coefficient since the relative error is low (<0.15) in most cases.

[0172] We now calculate the maximum SINR, measured for the cell i. SINR/is the maximum between all the SINR coming from the potential serving cells M.sub.0 for the same experiment scenario.

[0173] We compare the mean of the SINR on the logarithmic scale (that is denoted by SINR.sub.dB) relative to our theoretical approximation.

[0174] In FIG. 5, we give the bar chart of the absolute value of the error between SINR.sub.dBand the approximate value. We note that the theoretical method according to the development is able to estimate the maximum of the SINR given that the error on the mean does not exceed 1.5 dB in most cases.

[0175] We also note a low error on the estimation of the variance of the SINR with the method according to the development.

[0176] Now, in connection with FIG. 4, a second embodiment of the development is presented. In this particular embodiment, the steps referenced E41 to E44 and E46 are identical to the steps referenced E31 to E34 and E37, respectively, described above in connection with FIG. 3.

[0177] During a step E45, the expression of the SINRs (SINR.sub.1, SINR.sub.2 . . . SINR.sub.M) measured for a user terminal capturing a radio signal transmitted by each of the cells of the set (CELL.sub.1, CELL.sub.2 . . . CELL.sub.M) is modified during the step E42. More particularly, the number of cells causing interferences that may disturb the reception of a radio signal by the user terminal to be taken into account for calculating the SINRs is reduced to one. The cell causing interferences selected and kept for calculating the SINRs is the cell offering the highest mean reception power. In one example, it is assumed that the cell 1 (CELL1) is, from the set of cells determined in E41, the cell offering the highest mean reception power. In other words: .sub.1.sub.2 . . . .sub.M.sub.0.

[0178] Thus, this can then be written:

[00033]${\mathrm{SINR}}_1\frac{10^{\frac{{}_1+{}_1^{}}{10}}}{\mathrm{10}\frac{{}_2+{}_2^{}}{10}}\mathrm{And}{\mathrm{SINR}}_i\frac{10^{\frac{{}_i+{}_i^{}}{10}}}{\mathrm{10}\frac{{}_1+{}_1^{}}{10}}$

[0179] for 2iM.sub.0.

[0180] During the step E45, the value of the correlation coefficient is then approximated between the SINRs on the logarithmic scale from the values of the determined

[0181] SINRs by reducing the number of cells causing the interferences. Therefore,

[00034]${}_{12}-1,{}_{1j}-\frac{{}_1^2}{\sqrt{\left({}_1^2+{}_2^2\right)\left({}_1^2+{}_j^2\right)}},\mathrm{for}3j{M}_0\mathrm{and}$${}_{\mathrm{ij}}\frac{{}_1^2}{\sqrt{\left({}_1^2+{}_i^2\right)\left({}_1^2+{}_j^2\right)}},\mathrm{for}2ij{M}_0$

[0182] Indeed, if the calculation is detailed, this gives:

[00035]${\mathrm{SINR}}_1^{\mathrm{dB}}\text{?}{x}_1-{}_{\mathrm{dB}}-x_2\mathrm{and}{\mathrm{SINR}}_i^{\mathrm{dB}}\text{?}{x}_i-{}_{\mathrm{dB}}-x_i\mathrm{for}2i{M}_0;\mathrm{where}:x_i\text{?}{}_i+\text{?}\mathrm{for}1M_0\text{?}{}_{\mathrm{dB}}\text{?}10\log .$$\text{?}\text{indicates text missing or illegible when filed}$

[0183] So:

[00036]$E\left[{\mathrm{SINR}}_1^{\mathrm{dB}}\right]\text{?}{}_1-{}_2-{}_{\mathrm{dB}}\text{?}\mathrm{var}\left[{\mathrm{SINR}}_1^{\mathrm{dB}}\right]\text{?}{}_1^2+{}_2^2$$\text{?}\text{indicates text missing or illegible when filed}$

[0184] (var denotes variance)

[0185] And:

[00037]$E\left[{\mathrm{SINR}}_i^{\mathrm{dB}}\right]\text{?}{}_i-{}_1-{}_{\mathrm{dB}}\text{?}\mathrm{var}\left[{\mathrm{SINR}}_{\text{?}}^{\mathrm{dB}}\right]\text{?}{}_1^2+{}_i^2$$\text{?}\text{indicates text missing or illegible when filed}$

[0186] By using the approximated values of SINR.sub.i.sup.dB, the approximated value of the correlation coefficient is:

[00038]${}_{ij}\frac{E\left[{\mathrm{SINR}}_i^{\mathrm{dB}}{\mathrm{SINR}}_j^{\mathrm{dB}}\right]-E\left[{\mathrm{SINR}}_i^{\mathrm{dB}}\right]E\left[{\mathrm{SINR}}_j^{\mathrm{dB}}\right]}{\sqrt{\mathrm{var}\left[{\mathrm{SINR}}_i^{\mathrm{dB}}\right]\mathrm{var}\left[{\mathrm{SINR}}_j^{\mathrm{dB}}\right]}}$

[0187] Then: [0188] for the coefficients between cells 1 and 2, this gives:

[00039]$E\left[{\mathrm{SINR}}_1^{\mathrm{dB}}{\mathrm{SINR}}_2^{\mathrm{dB}}\right]E\left[{\mathrm{SINR}}_1^{\mathrm{dB}}\right]E\left[{\mathrm{SINR}}_2^{\mathrm{dB}}\right]-\left({}_1^2+{}_2^2\right)$

[0189] Hence: [0190] 121. [0191] for the coefficients between the cells 1 and j (3jM.sub.0), this gives: E[SINR.sub.1.sup.dB SINR.sub.j.sup.dB]E[SINR.sub.1.sup.dB]E[SINR.sub.j.sup.dB].sub.1.sup.2

[0192] Thus:

[00040]${}_{1j}-\frac{{}_1^2}{\sqrt{\left({}_1^2+{}_2^2\right)\left({}_1^2+{}_j^2\right)}}$ [0193] for the coefficients between the cells i and j (2ijM.sub.0), this gives:

[00041]$E\left[{\mathrm{SINR}}_i^{\mathrm{dB}}{\mathrm{SINR}}_j^{\mathrm{dB}}\right]E\left[{\mathrm{SINR}}_i^{\mathrm{dB}}\right]E\left[{\mathrm{SINR}}_j^{\mathrm{dB}}\right]+{}_1^2)$

[0194] Hence:

[00042]${}_{\mathrm{ij}}\frac{{}_1^2}{\sqrt{\left({}_1^2+{}_i^2\right)\left({}_1^2+{}_j^2\right)}}$

[0195] Subsequently, as described above in connection with FIG. 3, in the step E37, during the step E46, the mean and the variance of the SINR.sup.dB, or maximum SINR, is determined iteratively, using the preceding equations EQ4.1, EQ4.2, EQ4.3 and EQ4.4. We specify here that, in the equations EQ4.1, EQ4.2, EQ4.3 and EQ4.4, we use the exact values of the means and variances of SINR.sub.i.sup.dB, 1iM_0 (obtained in the step E44) and the approximate values of the correlation coefficients calculated above.

[0196] In order to validate this other theoretical approach in connection with FIG. 4, the inventors carry out as above the following experiment: simulation of a network with M=6 cells and M.sub.0=4 potentially serving cells. 3,000 Monte Carlo type embodiments are considered corresponding to a plurality of values: [0197] the mean reception power of a signal transmitted by the cell 1 such as 100.sub.150 dBm, [0198] the mean reception powers of the signals transmitted by cells knowing that .sub.1.sub.i for 2i4 because M.sub.0=4 and .sub.1.sub.i> for 5i6 with =20 dB, [0199] the standard deviation of the shadowing: 7.sub.i12 dB for 1iM, [0200] 410 dB, [0201] 0.40.9.

[0202] In FIG. 6, we give the bar chart of the absolute value of the error between SINR.sub.dB and the approximate value. We also note here that this theoretical method makes it possible to estimate the maximum of the SINR given that the error on the mean does not exceed 1.5 dB in most cases.

[0203] In addition, for this method, we note a low error on the estimation of the variance of the SINR.

[0204] Now, with reference to FIG. 7, the hardware structure of a system for monitoring the performance of a cellular radiocommunication network according to one embodiment of the development, or a system for planning the deployment of a cellular radiocommunication network, or a system for optimizing the operating parameters of a cellular radiocommunication network is presented.

[0205] Such a system referenced 5 comprises a unit for estimating the characteristic parameters of a reception quality in a location of the cellular radiocommunication network, and a unit for analyzing the performance of the network (respectively a unit for determining the parameters for planning the network or a unit for determining the optimized operating parameters of the network), depending on the estimated characteristic parameters.

[0206] The term unit may correspond to both a software component and a hardware component or a set of hardware and software components, a software component itself corresponding to one or more computer program(s) or sub-program(s), or more generally to any element of a program capable of implementing a function or a set of functions.

[0207] More generally, such a system 5 for monitoring the performance of the network (respectively system for planning the deployment of the network or system for optimizing the operating parameters of the network) comprises a random access memory M1 (for example a RAM), a processing unit 6 equipped for example with a processor, and controlled by a computer program, representative of the unit for estimating the characteristic parameters of a reception quality in a location of the cellular radiocommunication network, stored in a read-only memory M2 (for example a ROM or a hard drive). Upon initialization, for example, the code instructions of the computer program are loaded into the random-access memory MI before being executed by the processor of the processing unit 6. The random-access memory Ml particularly contains the various variables used in the calculations described above with reference to FIGS. 3 and 4. 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 serving cells, the calculation of the correlation coefficient, as well as the calculation of the mean and of the variance of the SINR on the logarithmic scale, corresponding to the maximum of the ratios SINR.sup.dB.

[0208] The random-access memory MI 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 unit for analyzing the performance of the network 7 (respectively a unit for determining the parameters for planning the network or a unit for determining the optimized operating parameters of the network), 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 different therefrom.

[0209] The system 5 also comprises an input/output I/O module 8 making it possible to return to the operator of the network the results of the analysis of the performance of the network carried out by the analysis unit 7 (respectively the results of the determination of the network planning parameters 7 or the results of the determination of the optimized operating parameters carried out by the unit for determining the optimized operating parameters of the network 7).

[0210] All the components M1, M2, 6, 7 and 8 of the system 5 are for example connected by a communication bus 9.

[0211] FIG. 7 only illustrates a particular way, from several possible, of producing the system for monitoring the performance of the network (respectively the system for planning the deployment of the network or the system for optimizing the operating parameters of the network), so that it carries out the steps of the method detailed above, with reference to FIGS. 1 to 4 (in any one of the various embodiments, or in a combination of these embodiments). Indeed, these steps may be implemented 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 like an FPGA or an ASIC, or any other hardware module).

[0212] In the case where the system 5 for monitoring the performance of the network (respectively the system for planning the deployment of the network or the system for optimizing the operating parameters of the network) is produced with a reprogrammable computing machine, the corresponding program (that is to say the sequence of instructions) could be stored in a removable storage medium (such as for example, a flash drive, a CD-ROM or a DVD-ROM) or not, this storage medium being partially or totally readable by a computer or a processor.