Patent Description:
As many wireless communication systems nowadays operate in a public band, one must cope with the coexistence of interference. The characterization of interference helps monitoring the radio environment and/or managing the radio resource. In particular, it is sought for characterization of wireless interference.

Interference monitoring in a radio environment where multiple interference sources exist requires usually an analysis of observation obtained from several devices. The problem is rather more complex when the sources move during the observations. The problem is usually to identify the interference as separated sources and to characterize then the following properties for each source:.

Prior art documents D1: <CIT> and D2: <CIT> relates respectively to a method and a device for transmitting data in a wireless communication network and to systems and methods for identifying a source of a radiofrequency interference in a wireless network.

The present disclosure aims to improve the situation.

To that end, it proposes a method for characterizing radiofrequency interference caused by a plurality of sources, comprising:.

More particularly, the aforesaid likelihood probability computing is based on both frequency observation and interference power observation.

It is proposed therefore to use both the frequency observations and, here in particular, the power observations to compute the likelihood probabilities, the power observations being related to the distance between the interference source and the observing device.

For example, the likelihood probability computing can involve a joint Bayesian inference of frequency observation and interference power observation. Alternatively, an Euclidean distance calculation approach can be used also.

In an embodiment, a database storing previous detections can be used. Typically, in an embodiment, the aforesaid likelihood probability computing can use such a database storing, for each source of said interference sources, at least data of the estimated location of said interference source and data of an occupation of at least two radiofrequency bands by said interference source.

Typically, these stored data can be derived from previous observations. Therefore, said data of a location of the interference source can be a data of a past location of the source, and said data of an occupation of the interference source can be typically a data of a past frequency occupation by said source.

The aforesaid database can be updated after each assignation of an observation to one interference source, in view of a next iteration of the method.

Therefore, the use of the database storing data of previous identifications of interferers (location/frequency band(s) of interference) can facilitate a current determination of multiple sources of interference. Reversely, after a determination of interferers, the content of the database is preferably updated so that it can be used efficiently for a next future iteration of the method.

In a particular embodiment, for each interference source, the database can provide at least one of:.

Therefore, the database can provide an average past position of a source, and more particularly this average can be weighted inversely as a function of at the date of determination of this source.

The average past position can be used in a context of a "hard decision" embodiment which is specified below, and the probability map can be used in a context of a "source sampling" embodiment explained below also, depending for example on a level of confidence of a current determination of a source of interference.

Moreover, for each interference source, the database can provide at least one of:.

The database can store, for each interference source, data of occurrences of previous observations which were already assigned to this interference source.

This information of previous observations associated to a source can help also for probability calculations of a current observation to be associated (or not) to this same source.

For a current observation, the likelihood probability of one of the interference sources to be related to said current observation is computed on the basis of a comparison between data of said database and data of said given observation, and the aforesaid comparison comprises a determination of:.

Therefore, the likelihood probability is computed on the basis of two items which can finally correspond to one same source:.

In the aforesaid "hard decision" embodiment, the assignation of at least one observation to one interference source can comprise:.

In this embodiment, the likelihood probabilities can be computed for example on the basis of Euclidian distance calculations of:.

In the "source sampling" embodiment, the assignation of at least one observation to one interference source comprises:.

For example, in view to choose the "hard decision" embodiment or the "source sampling" embodiment, each likelihood probability can be compared to a threshold and:.

Regarding the observing device, in an embodiment, the observing device can move in successive known positions while said interference sources are assumed to be in fixed positions.

For example, the observing device can be installed in a moving vehicle, such as a train, having a known trajectory, or can simply be equipped with a GPS to know its successive current positions.

Alternatively, the aforesaid scanning of plurality of radiofrequency bands can be performed by at least three observing devices having known spatial positions (fixed positions for example), said spatial positions being not aligned, and the sources positions determination can be performed by triangulation over the three observing devices.

The present disclosure can also aim at an observing device comprising a computer circuit to perform at least steps of the method as presented above and including typically:.

It also aims at a system comprising at least three of these observing devices for performing the sources positions determination of the method by triangulation.

It also aims at a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out at least steps of the method, including typically:.

It also aims at a non-transitory computer storage medium, storing instructions code of such a computer program.

More details and advantages of the present disclosure will be understood when reading the following description of embodiments given below as examples, and will appear from the related drawings where:.

Referring to <FIG>, given current observations I2 (mandatory) and an optional database I1 (the database can be built in prior steps of the method from precedent observations) of an environment comprising interferers for a communication system, the method proposes to analyse the interference in terms of frequency usage, geolocation characteristic and/or time usage. An interference analysis device performs observations in multiple frequencies, locations, and time instants.

The first input for each processing step is the interference database. For each separated current interference source, the database provides in step I1 the following information:.

The aforesaid "current observations" can consist in step I2 of:.

Indeed, the interference analysis device scans a given frequency band over a plurality of radiofrequency channels (for example sixteen channels in the examples of the figures commented below), each channel having its proper index.

Then, step S1 comprises a membership computation. Using the database of interference sources, the likelihood of the current observations for each source is computed based on a Bayesian Inference. The calculation measures:.

This probability reflects how strong the connection between an observation and a source is.

Then, step S2 comprises observation classification. Using the probability calculated in the previous step, step S1, the observation is assigned to a source. This step S2 can be performed according to several embodiments:.

Then, step S3 comprises the database update. Once the observation is associated to a source, the database of this source evolves according to the observation and is updated thus accordingly.

Finally, in step O1, the newly updated database can be used in a next iteration of the process.

The details for each step of the general method are described below.

In the interfered radio environment, it is assumed that the interference is generated by one or several radio sources. One source distinguishes to another by its geometrical position and/or its operating frequency band.

To be able to monitor the environment, the observation of interference is required. To that end, the observations can be obtained by any of the following devices:.

For the observing device, since there does not exist any signalling exchange with interference sources, the interference transmission is completely random. In other words, the device blindly observes the interference without knowing the interference location. Therefore, the observation data can be seen as a mixed signal.

Hereafter the following notations are adopted:.

For the estimation of the received power, it is assumed at the moment the observation Zn is obtained, that the interference source Sk is emitting. The received power (in dB for example) at the observing device can be modelled as <MAT> where a and b are two coefficients of path-loss model and ϑn,k takes into account a possible shadowing. The shadowing follows the multivariate Gaussian distribution with the correlation between two vehicles' positions Tn and Tm (such as two trains' positions for instance) being expressed as : <MAT> where ρ<NUM>, dc are two coefficients of shadowing model.

The operating frequency of the interference source Sk can be modelled by two parameters: <MAT> where fk denotes the starting frequency and Bk denotes the band width.

To be able to analyse the interference, it is needed to classify the observations into separated sources. A database for each source then can be built and updated according to its belonged observations. This database afterward serves the next observation classification as a prior knowledge.

Hereafter, Vn denotes the latent variable which indicates the source that the observation Zn is assigned to. At the beginning of the analysis of observation Zn, the following statements are assumed:.

The membership computation of step S1 can be based either on:.

Given the average position θk and average frequency φk, the membership computation can be performed by the Euclidean distance between the observation and the interference source. In the spatial domain, the distance is proposed to be computed as follows <MAT> and the distance in the frequency domain is proposed to be computed as follows <MAT>.

In another approach, the membership computation can be proposed to be computed as corresponding to the probability that the observation can be a part of a source. Hereafter is denoted the latent variable Vn, corresponding to the time instant n, to indicate which source the observation belongs to. For an observation Zn = (Tn,Wn,Fn), the following probability can be considered <MAT>.

Since an interference source is independent to others, the likelihood probability p(Zn|Vn,Z-n,V-n) can be detailed for all possible values of Vn as <MAT>.

Due to the fact that the power observation Wn is a function of distance between the device and the interferer, it has no connection with the frequency observation Fn which simply depends on the setting of the device. The probability in previous equation can be thus rewritten as: <MAT>.

As mentioned above, the power observation (in dB for instance) follows the gaussian distribution. Therefore the probability <MAT> can be expressed as: <MAT>.

In this expression, the terms <MAT>and <MAT> can be calculated as follows: <MAT> where:.

The probability in frequency observation can be divided into two terms p<NUM>, and p<NUM> as <MAT>. The design of p<NUM>, and p<NUM> can be proposed as follows:.

where α is a normalizing coefficient which makes the probability sum to one.

Then, for the implementation of step S2, the observation classification can be based then either on:.

The observation is classified by a hard decision according to its distance to the sources. In this sense, the observation is associated to the source with smallest distance, such as: <MAT>.

The observation classification can be derived by one of the following methods:.

In order to adopt the advantage of two both aforementioned methods, the rapidity of the "hard decision" method and the stability of the sampling method, a hybrid approach is proposed below.

First, the membership probability is normalized as follows <MAT>.

Second, a threshold for the normalized membership probability is defined. In the present case, the best normalized membership probability falls above this threshold, and it can be stated that the best source for the observation is evident, so that the hard decision method can be implemented. For the other cases, the best source is ambiguous, thus the sampling method should rather be implemented.

The database update of step S3 can be based either on:.

Supposing that the observation Zn is associated to the source k, then the average position and frequency can be updated as follows: <MAT>.

For the geolocation database, the following probability is computed for the update: <MAT>.

For the frequency database, the following probability is computed for the update <MAT>.

<FIG> show an example of an interference environment. The interference activities in the frequency domain is depicted in <FIG> and the respective geographical positions of interferers are shown in <FIG>.

In this interfering environment, a moving device observes the interference environment via a frequency hopping (FH) pattern, as presented typically in <FIG> for instance. Details of such interference observations can be obtained for example from document <CIT>.

The initial corresponding observations obtained on the moving device are showed in <FIG> (in the frequency domain) and in <FIG> (in the spatial domain).

To analyse the environment, the Bayesian inference is used for the membership computation and the hard decision is used for the classification, in this example. The estimation is performed from scratch without any initial database.

The result of observation classification is showed in <FIG> and <FIG> in the frequency domain and received power domain, respectively. In both figures, each label I1, I2, I3, I4 and I5 represents an interference source. As it can be seen, the method implementation makes it possible to estimate five interference sources and attribute the observations to each one of them. In <FIG>, the estimation of operation frequency and activation time are plotted for each interference source. In <FIG>, the contours of probability for the source's position resulted from the Bayesian update, are displayed along with the true position of the interference sources.

Therefore, the present disclosure allows the usage of a frequency hopping system, for instance in <NUM> ISM band, for characterizing the interference along a predefined trajectory such as a railroad, thereby making it possible to reduce the impact of such interference for railways equipment and/or communicating devices embarked in a train.

Claim 1:
A method for characterizing radiofrequency interference caused by a plurality of sources, comprising:
- using at least one observing device scanning successively a plurality of radiofrequency bands for performing interference measurements (I2) in said plurality of radiofrequency bands, in different space locations, and at different time instants, and providing observations at successive time instants of:
* a current observing frequency,
* a current position of the observing device, and
* a received interference power in an observed frequency band,
- computing (S1), for each interference source and each observation, a likelihood probability to attribute one observation to one interference source,
- using said likelihood probability computations to assign (S2) each of said observations to one interference source,
- for each interference source assigned with one of said observations, estimating a location and a frequency occupation of said interference source,
Wherein said likelihood probability computing (S1) is based on both frequency observation and interference power observation.