Estimating characteristic parameters of a reception quality at a location of a cellular radio communication network

Abstract

The invention relates to a method for estimating characteristic parameters of a reception quality at a location of a cellular radio communication network. According to the invention, such a method comprises steps of: - determining (E1), among a set of cells of said network, at least two cells associated with highest average reception strengths of a wanted signal at said location; - determining (E2) at least two signal-to-interference-plus-noise ratios at said location for said wanted signal received from each of said at least two cells; - calculating (E5) a maximum between said at least two determined signal-to-interference-plus-noise ratios on the logarithmic scale; - estimating (E6) said characteristic parameters of a reception quality at said location from said calculated maximum.

Description

Estimation of characteristic parameters of reception quality at a location of a cellular radiocommunication network

[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 transmitting antennas, also called base stations. The cells form a tiling of a geographical area, and one objective of the radiocommunication network operator is to ensure 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, avoiding as much as possible the appearance of white zones.

[0003] In a 3G (or third-generation mobile network, also known as UMTS for "Universal Mobile Telecommunications System"), 4G (or fourth-generation mobile network, also known as LTE for "Long Term Evolution") and 5G (or fifth-generation mobile network) radio environment, the transmitting antennas of neighboring cells emit useful signals in the same frequency band. At any given time, each user is attached to one of the cells in the network, commonly called a server cell, and from which they receive the useful signal they need.

[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 terminal's ability 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"), 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 terminal's receiver. If the 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 terminal's location.

[0005] The coverage area for this service can therefore be defined as the set of locations 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 on 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 terminal is subject to variations linked to the nature of the radio environment. Indeed, the reception powers at two mobile terminals located at the same distance from the base station are different because the obstacles existing on the paths between each mobile terminal and the 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 received power.

[0007] As previously indicated, in a cellular radiocommunication network, a user terminal is typically attached to the cell that it receives best, 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 power 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 network location, the serving cell is "fixed", and corresponds, for example, to the cell from which the useful signal with the highest average reception power for the user terminal is received.

[0009] Thus, in the article “SINR and rate distributions for downlink cellular networks”, IEEE Transactions on Wireless 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 serving 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. focus on cellular networks based on OFDMA (Orthogonal Frequency Division Multiple Access) multiplexing techniques, and propose an alternative approach for statistical modeling of SINR. Their work also relies on the simplifying assumption that at a given location of the network, a user terminal receives a useful signal from a base station BS0 of a frozen 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 over time, at a given location in the network.

[0012] Thus, the approximation on which these two articles of prior art are based is satisfactory when the difference between the average power of the signal received from the strongest cell and the second strongest cell is quite large, typically for mobile terminals close to the center of the cell. However, it reaches its limits of validity for mobile terminals at the edge of cells. However, it should be noted that the area at the edge of cells is the risk area where it is very 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.

[0015] The invention meets this need by proposing a method for estimating parameters characteristic of a reception quality at a location of a cellular radiocommunication network, which comprises steps of:- determining, among a set of cells of the network, at least two cells associated with the highest average reception powers of a useful signal at the location;- determining at least two signal-to-interference-plus-noise ratios at the location for the useful signal received from each of said at least two cells;- calculating a maximum between said at least two signal-to-interference-plus-noise ratios determined on a logarithmic scale;- estimating the parameters characteristic of a reception quality at the location from the calculated maximum.

[0016] Thus, the invention is based on a completely new and inventive approach to estimating reception quality at any point in a network, for purposes such as network planning, optimizing an existing cellular radiocommunication network, or monitoring network performance. Indeed, 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 serving cell to which a user terminal is attached. This is the assumption on which the proposals of the aforementioned articles “SINR and rate distributions for downlink cellular networks,” IEEE Transactions on Wireless 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, by 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 of a location at the edge of a cell, due to the random nature of the "shadowing" phenomenon. It 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 in this realistic case, from the signal to interference plus noise ratios of the plurality of cells likely to play the role of server cell, namely those whose useful signal reception power 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] Knowledge of this 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 coverage of the cellular radiocommunication network.

[0020] In a particular embodiment, two cells associated with the highest average reception powers of a useful signal at said location are determined, and the estimation of the characteristic parameters also comprises: - a calculation of a mean and a variance of the two signal-to-interference-plus-noise ratios determined on a logarithmic scale; - a calculation of a mean and a variance on a logarithmic scale of a random variable defined as the inverse of a product of the two signal-to-interference-plus-noise ratios determined on a linear scale; - a calculation of a mean of a product of the two signal-to-interference-plus-noise ratios determined on a linear scale, from the calculated mean and variance of the random variable.

[0021] This places us in the simpler case where we only consider the influence of two potential server cells, which is a quite realistic simplifying hypothesis for a location at the edge of a cell. As will be seen in more detail later in this document, the characteristic parameters of the distributions of the signal-to-interference-plus-noise ratios for these two cells (mean and variance) 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.This same technique can be used to calculate the characteristic parameters of a random variable Z defined as the inverse of the product of the two signal-to-interference plus noise ratios on a linear scale for the two potential server cells, and advantageously deduce the average of the product of these two signal-to-interference plus noise ratios.

[0022] Thus, in one embodiment, the average of the product of the two signal-to-interference plus noise ratios determined on a linear scale is calculated according to the formula: Or : And respectively denote the two signal-to-interference plus noise ratios determined on the linear scale of the two cells, denotes the logarithmic scale mean of the product , denotes the variance on the logarithmic scale of the product , And .

[0023] According to another advantageous aspect, the estimation of the characteristic parameters also comprises a determination of a correlation coefficient between the two signal-to-interference-plus-noise ratios determined on the logarithmic scale, as a function of:- the average of the product of the two signal-to-interference-plus-noise ratios determined on the calculated linear scale;- the averages and variances of the two signal-to-interference-plus-noise ratios determined on the calculated logarithmic scale.

[0024] Indeed, according to the work of S. Nadarajah and S. Kotz, “Exact Distribution of the Max / Min of Two Gaussian Random Variables,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 16, no. 2, pp. 210–212, Feb. 2008, the mean, variance and distribution 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 optimizing a cellular network, the correlation coefficient between the SINRs of two neighboring cells on the logarithmic scale is not a known quantity. The technique according to one embodiment of the invention advantageously proposes a method for calculating this correlation coefficient of two normal variables on the logarithmic scale, from the knowledge of the mean of their product on the linear scale.

[0025] Thus, this correlation coefficient is determined according to the formula: Or : And respectively denote the two signal-to-interference plus noise ratios determined on the linear scale of the two cells, And respectively denote the averages of the two signal-to-interference plus noise ratios determined on a logarithmic scale, And respectively denote the variances of the two signal-to-interference plus noise ratios determined on a logarithmic scale, and .

[0026] According to one aspect of the invention, the estimation of the characteristic parameters of the reception quality comprises a calculation of at least some of the elements belonging to the group comprising:- a distribution of the calculated maximum;- a mean of the calculated maximum;- a variance of the calculated maximum; from the determined correlation coefficient and the means and variances of the two signal to interference plus noise ratios determined on a logarithmic scale.

[0027] Thus, the technique of the invention makes it possible to calculate the expressions of the distribution of the SINR, the mean of the SINR and its variance on a logarithmic scale. Knowing the distribution and the variance, in addition to the mean, makes it possible to know the set of possible values ​​that the SINR can have with the associated probability. This allows for more precise optimization of the network.

[0028] In particular, according to one embodiment, the distribution of the calculated maximum is calculated according to the formula: ,Or : is the probability density function of the standard centered reduced normal distribution, is the distribution function of the standard normal law, And respectively denote the averages of the two signal-to-interference plus noise ratios determined on a logarithmic scale, And respectively denote the variances of the two signal-to-interference plus noise ratios determined on a logarithmic scale, and is the correlation coefficient between the two signal-to-interference plus noise ratios determined on a logarithmic scale.

[0029] In addition, the average of the calculated maximum is calculated according to the formula: Or : , is the probability density function of the standard centered reduced normal distribution, is the distribution function of the standard normal law, And respectively denote the averages of the two signal-to-interference plus noise ratios determined on a logarithmic scale, And respectively denote the variances of the two signal-to-interference plus noise ratios determined on a logarithmic scale, and is the correlation coefficient between the two signal-to-interference plus noise ratios determined on a logarithmic scale.

[0030] Finally, the variance of the calculated maximum is calculated according to the formula: Or : , is the probability density function of the standard centered reduced normal distribution, is the distribution function of the standard normal law, And respectively denote the averages of the two signal-to-interference plus noise ratios determined on a logarithmic scale, And respectively denote the variances of the two signal-to-interference plus noise ratios determined on a logarithmic scale, is the correlation coefficient between the two signal-to-interference plus noise ratios determined on a logarithmic scale, and is the average of the calculated maximum.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] Such a process can, for example, be implemented in planning tools such as Merit / Acp® or Atoll®.

[0038] 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.

[0039] Such a process can be integrated into CSON® type optimization tools.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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

[0044] 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:

[0045] This diagram shows a cellular radiocommunication network to which the estimation method can be applied according to different embodiments of the invention;

[0046] It illustrates in schematic form the existence of a common propagation zone for the correlated cells of the network of the;

[0047] Describes in the form of a flowchart the main steps of the estimation method according to one embodiment of the invention;

[0048] The present invention schematically shows the hardware structure of a system for monitoring the performance of a cellular radiocommunication network in one embodiment of the invention.

[0049] Detailed description of embodiments of the invention

[0050] 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".

[0051] The proposed solution allows to calculate the expressions of the SINR distribution, the SINR mean and its variance on a logarithmic scale, at any point in a cellular radiocommunication network. Knowing the distribution and the variance, in addition to the mean, allows to know the set of possible values ​​that the SINR can have, with the associated probability. This therefore allows a more precise optimization of the network.

[0052] For the record, and as illustrated by the, a cellular radiocommunication network 1, or mobile network, is composed of a network of relay antennas (or base stations) 21 to 2 N (N=4 in the example shown), each covering a portion of territory delimited 31 to 3 P (P=4 in the example shown), commonly called a cell (and shown schematically in hexagonal form on the), and carrying communications in the form of radio waves to and from user terminals.

[0053] To access the services offered by the network operator (voice or data), a mobile user must therefore be within range of a relay antenna 2 i . 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 users always have access to the services offered, operators deploy thousands of cells 3 i, each of them being equipped with 2 antennas i by making their cells overlap, so as to provide as complete a mesh as possible of the territory.

[0054] Indeed, if a terminal is able to correctly decode the signal intended for a given service, then this service is accessible with sufficient quality at the terminal's location. The coverage area for this service is the set of locations where the received SINR is higher 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. As coverage cannot be measured at every location in the network, the precise estimation of the SINR and its characteristic parameters is decisive to ensure the operator's coverage objectives.

[0055] 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.), the population density, etc. The size of the cell 3 i is also limited by the range of user terminals, which must be able to establish the return link.

[0056] In addition, a relay antenna 2 ihas 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.

[0057] 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 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 distribution, variance, and mean characteristics. The estimation of the SINR characteristics is then used by the operator in planning tools to optimize radio coverage.

[0058] The technique of the invention aims to propose a method for estimating the SINR at any location of the network, starting from the hypothesis that several cells can potentially play the role of server cell at a given point, due to the random phenomenon of "shadowing". In the following, in relation to item 3, we focus more particularly on describing the particular case of estimating the SINR according to this method, in the case where we consider that two cells of the network can play the role of server cell at a given location, at which there is for example a user mobile terminal 4.

[0059] Indeed, the case of two potential server cells corresponds to a very common case. According to a classic approach in the context of the simulation of the coverage of a network, it is assumed here that the values ​​of the loads of the cells are equal. As a reminder, the load of a cell corresponds to the fraction of resources granted by it to the users located in its coverage area.

[0060] To calculate the characteristic parameters of the SINR in this realistic case, the expressions of the SINR for the two cells CELL1, CELL2 having the two highest average reception powers of the useful signal, determined during a step E1, are first given, during a step referenced E2, as follows:

[0061]

[0062]

[0063] Or : is the number of cells that are received by a user located at the location of interest, is the thermal noise power, for is the average power received from the cell , is a centered normal random variable with variance which refers to “Shadowing” and denotes the cell charge .

[0064] SINR1 and SINR2 are normal random variables on a logarithmic scale, as discussed in the article by I. Hadj-Kacem, H. Braham, and S. Ben Jemaa, “SINR and rate distributions for downlink cellular networks”, IEEE Transactions on Wireless Communications, vol. 19, no. 7, pp. 4604–4616, 2020.

[0065] The objective of the present embodiment is to calculate, during a step referenced E5, the maximum between SINR1 and SINR2 on the logarithmic scale: Or And

[0066] The random variables of "Shadowing" are correlated in general since they correspond to the impact of the obstacles that the signal crosses during its propagation, and these obstacles are the same in the environment close to the user of interest when it comes to the downlink. The "Shadowing" suffered on the path , , (from the cell , , and to the mobile terminal 4) is therefore the sum of two independent Gaussian random variables, one of which is common to all paths , , to the mobile terminal 4 as shown in the. 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.

[0067] So, we can write that

[0068]

[0069]

[0070]

[0071] Or And ( are independent normal random variables with zero means and variances , , And , respectively, where is the variance of .

[0072] The step referenced E3 consists of calculating the characteristic parameters of the distributions And 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.

[0073] We note by And (respectively And ) the mean and variance of (respectively ). Then we define the random variable:

[0074] We show that: Or is the sum of lognormal random variables, therefore is also lognormal. Let SO . Either a centered normal random variable with variance . When we can write And . Thus, we approximate by a sum of four lognormal random variables.

[0075] The characteristic parameters of on a logarithmic scale (noted can also be 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.

[0076] Product characteristic parameters are therefore on a logarithmic scale. We therefore deduce the average of the product as follows: Or .

[0077] The paper by S. Nadarajah and S. Kotz, “Exact Distribution of the Max / Min of Two Gaussian Random Variables,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 16, no. 2, pp. 210–212, Feb. 2008, shows that in general the mean, variance, and distribution can be calculated for the maximum of two correlated normal random variables, assuming the correlation coefficient between these two variables is known. 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.

[0078] According to one embodiment of the invention, this coefficient is calculated during a step referenced E4, as stated in the Theorem presented below.

[0079] Let And two lognormal variables where ( is a normal variable with mean and variance . And are assumed to be correlated and we denote by their correlation coefficient. The objective here is to find the value of when the average of the product of And (i.e. ) is known.

[0080] We first recall that

[0081] The random variable is a normal variable. Thus, the product is a lognormal random variable. Let And the mean and variance of respectively. So, And

[0082] Thus, the lognormal random variable has the following characteristic parameters: And . Hence the average of the product East :

[0083] Finally, we can deduce the correlation coefficient between And as follows:

[0084] Thus, by applying this theorem, we show that the correlation coefficient between And is given by:

[0085] These different steps referenced E1 to E4 allow us to determine, during a step referenced E5, the maximum between SINR1 and SINR2 on the logarithmic scale: .

[0086] We can then determine, during a step referenced E6, the distribution of this maximum ( as well as its mean and variance based on the technique presented by S. Nadarajah and S. Kotz, in “Exact Distribution of the Max / Min of Two Gaussian Random Variables,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 16, no. 2, pp. 210–212, Feb. 2008.

[0087] The distribution of East : Or Or is the probability density function of the standard (centered) normal distribution and is the distribution function of the standard normal distribution.

[0088] The mean SINR on the logarithmic scale is Or .

[0089] The variance of the is, according to the works of S. Nadarajah and S. Kotz cited above:

[0090] To validate this theoretical approach presented in relation to the, 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 powers of the two cells having the highest average powers.

[0091] They also varied the difference between the average powers with the other interfering cells.

[0092] They thus generated several samples of the signals received by the user such as = −80dBm, ∈ {−80dBm, −82dBm, −83dBm}, ∈ {−88dBm, −90dBm, −92dBm, −94dBm} ( , the correlation coefficient between cells CELL1 and CELL2 belongs to the set {0.3, 0.5, 0.7}, ∈ {0.5, 0.6, 0.7} ( , ( And . The characteristic parameters of andZare calculated 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.For , it is enough to note that is the sum of two correlated lognormal random variables. An equivalent remark applies for

[0093] They then calculated the SINR according to the approach presented above in relation to Fig 3, i.e. the maximum between the two SINRs coming from the two potential server cells. They compared the average SINR on the logarithmic scale ( ) compared to the analytical results and to the case where the server cell is “frozen” (prior art technique).

[0094] <1 dB<1.5 dB<2 dB<3 dB<5 dB<8 dBTheoretical expression81%97%100%---"Frozen" waitress0007%42%96%

[0095] Table 1 above expresses as a percentage the number of cases where the absolute value of the error between and the estimated value exceeds a threshold. It is noted that the theoretical method according to the different embodiments of the invention offers a good estimate of the maximum SINR: in fact, the error on the average does not exceed 1 dB in 81% of cases and 1.5 dB in 97% of cases. On the contrary, if the server cell were assumed to be "frozen" (prior art technique), the error would exceed 3 dB in 93% of cases, and 5 dB in 58% of cases.

[0096] We now present, in relation to the, 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.

[0097] 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.

[0098] 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.

[0099] 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 controlled 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 Fig. 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 two potential server cells, the calculation of the correlation coefficient between these two SINRs on a logarithmic scale, as well as the calculation of the distribution, the mean and the variance of the SINR on a logarithmic scale, corresponding to the maximum of the ratios. And .

[0100] 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.

[0101] 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).

[0102] All components M1, M2, 6, 7 and 8 of the system 5 are for example connected by a communication bus 9.

[0103] The illustrates only one particular way, among several possible ways, of implementing the network performance monitoring system (respectively the network deployment planning system or the network operating parameter optimization system), so that it performs the steps of the method detailed above, in relation to the to 3 (in any one of the different embodiments, or in a combination of these embodiments). Indeed, these steps can be performed 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).

[0104] 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

Method for estimating parameters characteristic of a reception quality at a location of a cellular radiocommunication network (1), characterized in that it comprises steps of: - determination (E1), among a set of cells (3 i ) of said network, of two cells associated with the highest average reception powers of a useful signal at said location;- determination (E2) of two signal-to-interference-plus-noise ratios at said location for said useful signal received from each of said two cells;- calculation (E5) of a maximum between said two signal-to-interference-plus-noise ratios determined on a logarithmic scale;- estimation (E6) of said parameters characteristic of a reception quality at said location from said calculated maximum, said estimation comprising a calculation (E3) of an average of a product of the two signal-to-interference-plus-noise ratios determined on a linear scale. Method for estimating parameters characteristic of a reception quality according to claim 1, characterized in that the calculation of an average of a product of the two signal to interference plus noise ratios determined on a linear scale is implemented from a calculation of an average and a variance on a logarithmic scale of a random variable defined as the inverse of the product of said two signal to interference plus noise ratios determined on a linear scale. Method for estimating parameters characteristic of a reception quality according to claim 2, characterized in that said average of the product of said two signal to interference plus noise ratios determined on a linear scale is calculated according to the formula: Or : And respectively designate said two signal-to-interference plus noise ratios determined on the linear scale of said two cells, denotes the logarithmic scale mean of said product , denotes the variance on the logarithmic scale of said product , And . Method for estimating characteristic parameters of a reception quality according to any one of claims 1 to 3, characterized in that said estimation of said characteristic parameters also comprises a determination (E4) of a correlation coefficient between said two signal-to-interference-plus-noise ratios determined on a logarithmic scale, as a function of:- said average of said product of said two signal-to-interference-plus-noise ratios determined on a calculated linear scale;- averages and variances of said two signal-to-interference-plus-noise ratios determined on a logarithmic scale. Method for estimating parameters characteristic of a reception quality according to claim 4, characterized in that said correlation coefficient is determined according to the formula: Or : And respectively designate said two signal-to-interference plus noise ratios determined on the linear scale of said two cells, And respectively designate said averages of said two signal-to-interference plus noise ratios determined on a logarithmic scale, And respectively denote said variances of said two signal-to-interference plus noise ratios determined on a logarithmic scale, and . Method for estimating characteristic parameters of a reception quality according to claim 4 or 5, characterized in that said estimation of said characteristic parameters comprises a calculation (E6) of at least some of the elements belonging to the group comprising: - a distribution of said calculated maximum; - an average of said calculated maximum; - a variance of said calculated maximum; from said determined correlation coefficient and said averages and variances of said two signal to interference plus noise ratios determined on a logarithmic scale. Method for estimating parameters characteristic of a reception quality according to claim 6, characterized in that said distribution of said calculated maximum is calculated according to the formula: ,Or : is the probability density function of the standard centered reduced normal distribution, is the distribution function of the standard normal law, And respectively designate said averages of said two signal-to-interference plus noise ratios determined on a logarithmic scale, And respectively denote said variances of said two signal-to-interference plus noise ratios determined on a logarithmic scale, and is said correlation coefficient between said two signal to interference plus noise ratios determined on a logarithmic scale. Method for estimating parameters characteristic of a reception quality according to claim 6 or 7, characterized in that said average of said calculated maximum is calculated according to the formula: Or : , is the probability density function of the standard centered reduced normal distribution, is the distribution function of the standard normal law, And respectively designate said averages of said two signal-to-interference plus noise ratios determined on a logarithmic scale, And respectively denote said variances of said two signal-to-interference plus noise ratios determined on a logarithmic scale, and is said correlation coefficient between said two signal to interference plus noise ratios determined on a logarithmic scale. Method for estimating parameters characteristic of a reception quality according to any one of claims 6 to 8, characterized in that said variance of said calculated maximum is calculated according to the formula: Or : , is the probability density function of the standard centered reduced normal distribution, is the distribution function of the standard normal law, And respectively designate said averages of said two signal-to-interference plus noise ratios determined on a logarithmic scale, And respectively designate said variances of said two signal-to-interference plus noise ratios determined on a logarithmic scale, is said correlation coefficient between said two signal-to-interference plus noise ratios determined on a logarithmic scale, and is said average of said calculated maximum. A computer program product comprising program code instructions for implementing a method according to any one of claims 1 to 9, when executed by a processor. Method for planning the deployment 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 9 and a determination of planning parameters of said network as a function of said estimated characteristic parameters. 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 9, and a determination of optimized operating parameters of said network as a function of said estimated characteristic parameters. Method for monitoring the performance of a cellular radiocommunication network, characterized in that it implements an estimation of parameters characteristic of a reception quality at a location of said network according to any one of claims 1 to 9, and an estimation of at least one performance criterion of said network as a function of said estimated characteristic parameters. 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 9 and to determine planning parameters of said network as a function of said estimated characteristic parameters. System for optimizing operating parameters of a cellular radiocommunication network, characterized in that it 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 according to any one of claims 1 to 9, and to determine optimized operating parameters of said network as a function of said estimated characteristic parameters. 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 9 and to analyze a performance of said network as a function of said estimated characteristic parameters.

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