WO2024104794A1

WO2024104794A1 - Estimating characteristic parameters of a reception quality at a location of a cellular radio communication network

Info

Publication number: WO2024104794A1
Application number: PCT/EP2023/080746
Authority: WO
Inventors: Imed HADJ KACEM; Sana Ben Jemaa
Original assignee: Orange
Priority date: 2022-11-17
Filing date: 2023-11-06
Publication date: 2024-05-23
Also published as: FR3142311A1

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

The field of the invention is that of cellular radiocommunications, for example in cellular communication networks of type 3G, 4G, 5G or more. More precisely, the invention relates to the estimation of parameters characteristic of a reception quality of a useful signal, at all points of such networks, for purposes for example of planning or optimizing network resources, or even monitoring its performance.

Prior art

Cellular radiocommunication networks are conventionally 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 an objective of the radiocommunication network operator is to ensure coverage, for its users, over the entire geographical area considered, i.e. to ensure access to the services that it offers at any point in this geographical area, avoiding as much as possible the appearance of white areas.

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

However, in addition to the useful signal sent by its serving cell, a user terminal also receives interference signals from other cells in whose coverage area it is located. The ability of the terminal to properly decode the signal intended for it depends on the reception power of the useful signal and the interference, and more particularly 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 noise thermal, received at the terminal 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 location of the terminal.

We can therefore define the coverage area for this service 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 the voice service, 95% for the 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 crucial to ensuring the operator's coverage objectives.

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 the level of two mobile terminals located at the same distance from the base station are different due to the fact that the obstacles existing on the paths between each mobile terminal and the station are different (reflection phenomena on obstacles important, such as buildings in urban areas or forests in rural areas for example). In this case we are talking about the random phenomenon of “Shadowing” which adds a weakening term to the expression of the power received.

As indicated previously, 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 serving cell at this location, then the interfering cells, and then to estimate the associated SINR. However, in the absence of measurements and due to the random variation in the powers of the signals received at a given location (“Shadowing” phenomenon), the identity of the serving cell is not always known in a deterministic manner, and it can vary statistically, especially at the edge of cells.

However, for the purposes of simplification, the work previously published on this subject is based on the hypothesis that at a given location in the network, the serving cell is “fixed”, and corresponds for example to the cell from which the useful signal is received. having the highest average reception power for the user terminal.

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 hypothesis that at a given location in the network, a user terminal receives a useful signal from a fixed server cell k, and M interfering signals coming 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 serving 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.

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

In each of these two publications, the characteristic parameters proposed to estimate the SINR distribution are only valid if the serving cell of a user terminal effectively 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 serving cell. over time, at a given location in the network.

Thus, the approximation on which these two articles of the prior art are based is satisfactory when the difference between the average power of the signal received from 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.

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 this work of the prior art. In particular, there is a need for such a technique which improves the estimation of the signal quality received at any point of a cellular radiocommunication network, and in particular, but not exclusively, in locations at the edge of cells.

There is still a need for such a technique which makes it possible to improve the estimation of characteristics of the SINR report, for purposes in particular of planning, optimization of radio coverage or monitoring of the performance of a cellular radio network. .

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

Thus, the invention is based on a completely new and inventive approach to estimating the reception quality at any point of a network, for the purposes of, for example, network planning, optimization of a network of existing cellular radiocommunication, or even monitoring the performance of a network. Indeed, the techniques of the prior art in terms of estimating reception quality are all based on the hypothesis that in a given location of the network, there exists a single, fixed serving cell, to which a terminal user is attached. This is the hypothesis on which the proposals of the aforementioned articles “SINR and rate distributions for downlink cellular networks” are based, 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.

Unlike this previous work, the estimation technique according to one embodiment of the invention considers the realistic case where the role of waitress can be statistically played by several neighboring cells, which turns out to be 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 serving cell in a given location, it being understood that at a given time, a user terminal is only attached to a single serving 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 capable of playing the role of serving cell, namely those whose the reception power of the useful signal at this location is the highest.

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 serving cells.

Its knowledge makes it possible to estimate a certain number of parameters characteristic of the reception quality in a given location, and in particular the probability, over 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 radio network.

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

We thus place ourselves in the simpler case where we only consider the influence of two potential serving cells, which is a completely 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 according to 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. , flight. 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 serving cells, and advantageously deduce the average. of the product of these two signal to interference plus noise ratios.

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

According to another advantageous aspect, the estimation of the characteristic parameters also includes a determination of a correlation coefficient between the two signal to interference plus noise ratios determined on the logarithmic scale, depending on:
- the average of the product of the two signal to interference plus noise ratios determined on the calculated linear scale;
- calculated means and variances of the two signal to interference plus noise ratios determined on a logarithmic scale.

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 up to two correlated normal random variables, assuming the correlation coefficient between these two variables is known. In the case of planning and optimization of a cellular network, the correlation coefficient between the SINRs of two neighboring cells on the logarithmic scale is not a known quantity. The technique according to one embodiment of the invention advantageously offers a method of calculating this correlation coefficient of two normal variables on the logarithmic scale, from knowledge of the average of their product on the linear scale.

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

According to one aspect of the invention, the estimation of the parameters characteristic 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;
- an average of the calculated maximum;
- a variance of the calculated maximum;
from the correlation coefficient determined and the means and variances of the two signal to interference plus noise ratios determined on the logarithmic scale.

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 the logarithmic scale. Knowing the distribution and the variance, in addition to the mean, allows us to know all the possible values that the SINR can have with the associated probability. This allows for finer optimization of the network.

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

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

Finally, the variance of the calculated maximum is calculated according to the formula:
Or :
,
is the function of the probability density of the reduced standard centered normal distribution,
is the distribution function of the standard normal distribution,
And denote respectively the averages of the two signal to interference plus noise ratios determined on the logarithmic scale,
And respectively designate the variances of the two signal to interference plus noise ratios determined on the 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.

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

The invention also relates to a computer-readable recording medium 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 in a location of a cellular radiocommunication network according to the invention as described above.

Such a recording medium can be any entity or device capable of storing the program. For example, the support may comprise a storage means, such as a ROM, for example a CD ROM or a microelectronic circuit ROM, or even a magnetic recording means, for example a USB key or a hard disk.

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 program computer it contains can be executed remotely. The program according to the invention can in particular be downloaded onto a network, for example the Internet network.

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 in one localization of a cellular radiocommunication network.

The invention also relates to a method for planning the deployment of a cellular radiocommunication network, which implements an estimation of parameters characteristic of a reception quality at a location of said network, according to the method described above, and a determination of network planning parameters based on the estimated characteristic parameters.

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

It also relates to a method for optimizing operating parameters of a cellular radiocommunication network, which implements an estimation of parameters characteristic of a reception quality at a location of said network, according to the method described above, and a determination of optimized network operating parameters based on the estimated characteristic parameters.

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

The invention also relates to a method for monitoring the performance of a cellular radiocommunication network, which implements an estimation of parameters characteristic of a reception quality at a location of said network, according to the method described above, and an estimation of at least one network performance criterion based on the estimated characteristic parameters.

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 parameters characteristic of a reception quality at a location of said network, such as described previously, and to determine network planning parameters based on the estimated characteristic parameters.

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 network operating parameters based on the estimated characteristic parameters.

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 parameters characteristic of a reception quality at a location of said cellular radiocommunication network. as described previously and to analyze network performance as a function of the estimated characteristic parameters.

Presentation of figures

Other aims, characteristics and advantages of the invention will appear more clearly on reading the following description, given by way of a simple illustrative and non-limiting example, in relation to the figures, among which:

There presents in schematic form a cellular radiocommunication network to which the estimation method can be applied according to different embodiments of the invention;

There illustrates in schematic form the existence of a common propagation zone for the correlated cells of the network of the ;

There describes in flowchart form the main steps of the estimation method according to one embodiment of the invention;

There presents in schematic form the hardware structure of a system for monitoring the performance of a cellular radiocommunication network of the in one embodiment of the invention.

Description détaillée de modes de réalisation de l'inventionDetailed description of embodiments of the invention

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 of a cellular radiocommunication network, based on a realistic hypothesis consisting of considering that several cells are likely to play the role of serving cell, in a given location, due to the random phenomenon of “shadowing”.

The proposed solution makes it possible to calculate the expressions of the distribution of the SINR, the mean of the SINR and its variance on the logarithmic scale, at any point of a cellular radiocommunication network. Knowing the distribution and the variance, in addition to the mean, allows us to know all the possible values that the SINR can have, with the associated probability. This therefore allows for finer optimization of the network.

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) 2 ₁ to 2 _N (N=4 in the example shown), each covering a portion of territory delimited 3 ₁ to 3 _P (P=4 in the example shown), commonly called cell (and represented schematically in hexagonal form on the ), and carrying communications in the form of radio waves to and from user terminals.

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 3 _i cells, each of them equipped with 2 _i antennas by ensuring that their cells overlap , so as to provide as complete a coverage of the territory as possible.

Indeed, if a terminal is capable of correctly decoding the signal intended for it for a given service, then this service is accessible with sufficient quality at the location of the terminal. The coverage area for this service is all 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 the voice service, 95% for the video service, etc. As coverage cannot be measured at every location in the network, the accurate estimation of SINR and its characteristic parameters is critical to ensuring the operator's coverage objectives.

Note 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 cell 3 _i is also limited by the range of the user terminals, which must be capable of establishing the return link.

Furthermore, a relay antenna 2 _i has a limited transmission capacity, and can only process a certain number of simultaneous service access requests. This is the reason why, in cities, where the population density is high and the number of communications important, the cells tend to be numerous and small – spaced a few hundred 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 up to several kilometers but very rarely exceeding more than ten kilometers.

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 regarding 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, the latter cannot be effectively measured at every point of the network, it is important for the operator to be able to have a statistical estimate of this parameter and its distribution, variance and average characteristics. The estimation of the SINR characteristics is then used by the operator in planning tools to optimize radio coverage.

The technique of the invention aims to propose a method for estimating the SINR at any location in the network, based on the hypothesis that several cells can potentially play the role of serving cell at a given point, due to the random phenomenon of “shadowing”. We focus more particularly in the following, in relation to the and 3 , to describe 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 serving cell in a given location, at which there is for example a mobile user terminal 4.

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

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 data, during a step referenced E2, as follows:

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

SINR ₁ and SINR ₂ are normal random variables on the logarithmic scale, as explained 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 .

The objective of this embodiment is to calculate, during a step referenced E5, the maximum between SINR ₁ and SINR ₂ on the logarithmic scale:

Or And

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 that these obstacles are the same in the environment close to the user of interest when it is the downlink. The “Shadowing” suffered on the way , , (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 . We can refer, in this regard, 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 .

Thus, we can write that

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

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.

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

We show that:
Or is the sum of log-normal random variables, so is also log-normal. Either SO . Either a normal centered and variance random variable . When we can write And . Thus, we approximate by a sum of four log-normal random variables.

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.

Characteristic parameters of the product thereby are on a logarithmic scale. We therefore deduce the average of the product as follows:
Or .

The article 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 up to 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 on the logarithmic scale is not a known quantity.

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

Let And two log-normal variables where ( is a normal variable with mean and variance . And are assumed to be correlated and we note 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.

We first recall that

The random variable is a normal variable. Thus, the product is a log-normal random variable. Let And the mean and variance of respectively. SO,
And

Thus, the log-normal random variable has the following characteristic parameters: And . Hence the average of the product East :

Finally, we can deduce the correlation coefficient between And as following :

Thus, in application of this theorem, we show that the correlation coefficient between And is given by:

These different steps referenced E1 to E4 make it possible to determine, during a step referenced E5, the maximum between SINR ₁ and SINR ₂ on the logarithmic scale:
.

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 , flight. 16, no. 2, pp. 210–212, Feb. 2008.

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

The mean of the SINR on the logarithmic scale is
Or .

The variance of is, according to the work of S. Nadarajah and S. Kotz cited above:

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 “Shadowing” and several values of the difference between the average powers of the two cells having the highest average powers.

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

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 and Z are calculated according to 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 suffices to note that is the sum of two correlated log-normal random variables. An equivalent remark applies for

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

	<1 dB<1dB	<1.5 dB<1.5dB	<2 dB<2dB	<3 dB<3dB	<5 dB<5dB	<8 dB<8dB
Expression théoriqueTheoretical expression	81%81%	97%97%	100%100%	--	--	--
Serveuse « figée »“Frozen” waitress	00	00	00	7%7%	42%42%	96%96%

Table 1 above expresses as a percentage the number of cases where the absolute value of the error falls within and the estimated value exceeds a threshold. We note 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 1.5dB in 97 % of cases. On the contrary, if the serving 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.

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

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

The term unit can correspond as well to a software component 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 subprograms or more generally to any element of a program capable of implementing a function or a set of functions.

More generally, such a network performance monitoring system 5 (respectively network deployment planning system or system for optimizing network operating parameters) comprises a RAM memory M1 (for example a RAM memory), a storage unit processing 6 equipped for example with a processor, and controlled by a computer program, representative of the unit for estimating parameters characteristic of 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). At initialization, the code instructions of the computer program are for example loaded into the RAM M1 before being executed by the processor of the processing unit 6. The RAM 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 serving cells, the calculation of the correlation coefficient between these two SINR on the logarithmic scale, as well as the calculation of the distribution, of the mean and variance of the SINR on the logarithmic scale, corresponding to the maximum of the ratios And .

The RAM M1 can also contain the results of the calculations carried out by the processor of the processing unit 6. It can provide these results to a network performance analysis unit 7 (respectively a unit for determining planning parameters of the network 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.

The system 5 also includes 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 unit for determining optimized network operating parameters 7).

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

There illustrates only one particular way, among several possible, of creating the network performance monitoring system (respectively the network deployment planning system or the network operating parameter optimization system), so that it carries out the steps of the process detailed above, in relation to the to 3 (in any of the different embodiments, or in a combination of these embodiments). Indeed, these steps can be carried out indifferently on a reprogrammable calculation machine (a PC computer, a DSP processor or a microcontroller) executing a program comprising a sequence of instructions, or on a dedicated calculation machine (for example a set of logic gates like an FPGA or an ASIC, or any other hardware module).

In the case where the network performance monitoring system 5 (respectively the network deployment planning system or the network operating parameter optimization system) is produced with a reprogrammable calculation machine, the corresponding program (c that is to say the sequence of instructions) can 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 readable partially or entirely by a computer or processor.

Claims

Method for estimating parameters characteristic of 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 signal useful 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 the logarithmic scale;
- estimation (E6) of said parameters characteristic of a reception quality at said location from said calculated maximum, said estimate comprising a calculation (E3) of an average of a product of the two signal to interference plus noise ratios determined at 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 the linear scale is implemented from a calculation of a mean and a variance on the logarithmic scale of a random variable defined as the inverse of the product of said two signal to interference plus noise ratios determined on the 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 the 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,
designates the average on a logarithmic scale of said product ,
designates the variance on a logarithmic scale of said product , And
.
Method for estimating parameters characteristic of a reception quality according to any one of claims 1 to 3, characterized in that said estimation of said characteristic parameters also includes a determination (E4) of a correlation coefficient between said two ratios signal over interference plus noise determined on a logarithmic scale, depending on:
- said average of said product of said two signal to interference plus noise ratios determined on the calculated linear scale;
- means and variances of said two signal to interference plus noise ratios determined on the 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 the logarithmic scale,
And respectively designate said variances of said two signal-to-interference plus noise ratios determined on a logarithmic scale, and
.
Method for estimating parameters characteristic 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 means and variances of said two signal to interference plus noise ratios determined on the logarithmic scale.
Method for estimating parameters characteristic of reception quality according to claim 6, characterized in that said distribution of said calculated maximum is calculated according to the formula:
,
Or :
is the function of the probability density of the reduced standard centered normal distribution,
is the distribution function of the standard normal distribution,
And respectively designate said averages of said two signal to interference plus noise ratios determined on the logarithmic scale,
And respectively designate said variances of said two signal to interference plus noise ratios determined on the logarithmic scale, and
is said correlation coefficient between said two signal to interference plus noise ratios determined on the 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 function of the probability density of the reduced standard centered 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 the logarithmic scale,
And respectively designate 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 the 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 function of the probability density of the reduced standard centered normal distribution,
is the distribution function of the standard normal distribution,
And respectively designate said averages of said two signal to interference plus noise ratios determined on the logarithmic scale,
And respectively designate said variances of said two signal to interference plus noise ratios determined on the 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 parameters characteristic 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 parameters characteristic of 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 based on 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 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 parameters characteristic of a reception quality at a location of said network according to one any 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 parameters characteristic of a reception quality at a location of said network of cellular radiocommunication 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.