WO2005055628A1

WO2005055628A1 - Method and system, computer program with program code means and computer program-product, for establishing a card which describes a propagation behaviour of a communication signal, transmitted from a base station in a communication network

Info

Publication number: WO2005055628A1
Application number: PCT/EP2004/053122
Authority: WO
Inventors: Joachim Bamberger; Marian Grigoras; Clemens Hoffmann; Uwe Hanebeck; Patrick Rössler
Original assignee: Siemens Aktiengesellschaft
Priority date: 2003-12-04
Filing date: 2004-11-26
Publication date: 2005-06-16
Also published as: DE10356656A1; TW200525167A

Abstract

The invention relates to the modeling of a propagation behaviour of a communication signal, transmitted from a base station in a communication network. A physical characteristic of said communication signal, associated with a selected position, is measured at selected positions in the communication network. The model for the propagation behaviour is determined by means of the selected positions and the associated measured physical characteristics of the communication signal. The measurements at the selected positions are carried out by means of an autonomous mobile unit.

Description

description

Method and arrangement as well as computer program with program code means and computer program product for determining a card for describing a propagation behavior of a communication signal emitted by a base station in a communication network

The invention relates to a modeling and mapping of a propagation behavior of a communication signal emitted by a base station in a communication network.

Radio communication systems are based on e.g. B. ireless LAN, Bluetooth, GSM or DECT are used in a wide variety of areas. They are omnipresent in industrial production plants and office environments, but also in healthcare.

Propagation properties of an electromagnetic field, which is generated by the communication system emitting communication signals, essentially determine the performance of the communication system with regard to area coverage, availability and transmission rate.

On the one hand, radio network operators are interested in the distribution of the field propagation properties or signal characteristics, such as. For example, to determine electromechanical field strength, bit error rate, signal-to-noise ratio, etc. in order to be able to plan the radio network optimally, to be able to demonstrate commissioned system properties within the framework of quality assurance after installation of the network, or error states during operation of the system to be able to diagnose. On the other hand, the network service providers are interested in being able to offer their customers location-dependent services.

The position of the receiving device must be known for this. Since only data that is generated during normal network operation should be used for the position estimates, it is advisable to consider the signal characteristics here as well.

Approaches and procedures are known from the prior art which are concerned with the location of end devices, such as DECT

Handsets or wireless LAN-equipped PDAs and notebooks, in radio networks.

In some approaches, such as that known from [1], the localization is based solely on the network topology. The position of the end device is determined on the basis of the base station to which it is connected and its connection history.

However, the accuracy of such a method is low, since only a very large area around the base station to which the terminal is connected can be specified as a possible location.

Other known methods try to estimate the position based on the received field strengths of all available transmitters. In some cases, a detailed physical model for wave propagation is used. However, detailed information about the surroundings is required for this.

From [2] it is known to use knowledge of the electromagnetic properties of the various walls in the building. Such knowledge is generally not available. For this reason, a field strength map is usually first created, which is later used for localization. A point estimate of the receiver position is often made on the basis of the field strength map [3], [4].

[5] describes a recursive stochastic nonlinear filter method for position estimation of DECT mobile phones.

In most of these procedures known from the prior art, the card is measured by hand, an effort that is difficult to justify in practical use.

The invention is therefore based on the object of specifying a simple, less complex and automatable procedure for generating such a card.

This object is achieved by the method and the arrangement as well as by the computer program with program code means and the computer program product for determining a card for describing a propagation behavior of a communication signal emitted by a base station in a communication network with the features according to the respective independent claim.

In the method for determining the card, a physical property of the communication signal associated with the respective selected position is measured at selected positions in the communication network. The physical property characterizes the propagation behavior of the communication signal.

Using the selected positions or using corresponding location information of the selected positions and the associated measured physical properties A model for the propagation behavior is determined for the shafts of the communication signal.

Using the model for the propagation behavior, the map will be determined, whereby the model itself can already be called a map or the model itself can already be the map.

According to the invention, the measurements at the selected positions are carried out using an autonomous mobile unit, such as an autonomous robot. The autonomous mobile unit is set up, for example by means of a corresponding measuring device, for receiving the communication signal and for measuring the physical property of the communication signal.

The arrangement for determining the card has an autonomous mobile unit, which is set up, for example by a corresponding measuring device, for receiving the communication signal and for measuring a physical property of the communication signal.

The physical property characterizes the propagation behavior of the communication signal.

The autonomous mobile unit performs measurements at selected positions in the communication network. The physical property of the communication signal associated with the respectively selected position is measured at selected positions in the communication network.

Furthermore, the arrangement has an evaluation unit which can be used using the selected positions or using a ModeXl for the propagation behavior is determined by ending the corresponding location information of the selected positions and the associated measured physical properties of the communication signal. The evaluation unit also determines the map using the model for the propagation behavior.

It should also be noted here that the model itself can already be called a card or that the model itself can be the card.

An advantage of the invention is that the autonomous mobile unit can carry out the measurements autonomously and in an automated manner and the card can thereby be generated automatically.

This in turn enables the card to be updated continuously even while the communication network is in operation, so that it adapts to changes in the network structure (e.g. failures of individual transmitter stations).

The computer program with program code means is set up to carry out all the steps according to the inventive method for determining the card when the program is executed on a computer.

The computer program product with program code means stored on a machine-readable carrier is set up to carry out all the steps according to the inventive method for determining the card when the program is executed on a computer.

The arrangement and the computer program with program code means, set up to carry out all the steps according to the inventive see performing localization processes when the program is executed on a computer, and the computer program product with program code means stored on a machine-readable medium, set up to carry out all steps according to the inventive localization process when the program is executed on a computer are particularly suitable to carry out the method according to the invention or one of its further developments explained below.

Preferred developments of the invention result from the dependent claims.

The further developments described below relate both to the method and to the arrangement and the software-technical implementations.

The invention and the further developments described below can be implemented both in software and in hardware, for example using a special electrical circuit.

Furthermore, an implementation of the invention or a further development described below is possible by means of a computer-readable storage medium, on which the computer program with program code means, which carries out the invention or further developments, is stored.

The invention or any further development described below can also be implemented by a computer program product which has a storage medium on which the computer program with program code means which carries out the invention or further developments is stored. In the case of communication in a communication network, such as a radio network, between a mobile communication device (mobile station), for example a mobile telephone, and a base station, for example an omnidirectional antenna or an omnidirectional antenna or one or more sectoral antennas, data, the communication signals, are converted into signal packets - ten, so-called bursts.

Based on (measurable) physical properties of the transmitted or radiated communication signals or signal packets, various distance-relevant parameters can be determined, which in turn serve as the basis for the determination of radiation or signal characteristics. can be used by (signal) transmitters.

Such a distance-relevant, i.e. The distance-dependent parameter is, for example, a field strength of a communication signal or signal packet, a bit error rate or a signal-to-noise ratio.

The field strength of a broadcast communication signal has a natural dependence on the distance from a transmitter, the (conversational) base station, and therefore provides information about the propagation behavior (propagation characteristic) of the transmitter.

This dependency between the field strength, generally the physical property, and the distance from the transmitter or the (receiving) location or the (receiving) position in the communication network can be described by physical models which describe a propagation behavior of signals , In a so-called forward model, the physical property of the communication signal is described as a function of a position in the communication network or a distance.

A backward or inverse model describes the position in the communication network as a function of the physical property of the communication signal.

A suitable model of the forward model type, similar to that from [5], has, for example, a deterministic part (partial model) and a stochastic part (partial model).

The deterministic part describes the dependency between the physical property of the communication signal and a position in the communication network; the stochastic part describes an uncertainty of the deterministic part.

The uncertainty of the deterministic component can be an uncertainty of the communication signal, in particular a measurement noise, and / or an uncertainty of the dependency, in particular an uncertainty of the propagation model.

Larger communication networks generally have a plurality or a multiplicity of base stations, each of which emits a communication signal.

Here it is advisable to determine several maps or models, one map or model each showing the spread behavior describes a communication signal emitted by a base station in the communication network.

The multiple cards or models can be combined into one overall card.

Furthermore, it may be expedient to have the selected positions or their location information determined by the autonomous mobile unit. For this purpose, the autonomous mobile unit can be equipped with a position measuring system for determining the position of the autonomous mobile unit in the communication network and / or a path planning system for determining a trajectory in the communication network.

For example, odometers and / or image-processing position measuring systems as well as systems for dead reckoning are known.

The map or model created by the inventive procedure can be the basis for numerous applications in communication networks.

For example, the card or the model and / or the overall map or the overall model can be used for planning and / or installation and / or commissioning and / or diagnosis of error states and / or quality assurance of the communication network.

The model for the propagation behavior and / or the map and / or the models for the propagation behavior and / or the overall map can also be used / becomes a localization of at least one mobile communication device in the communication network which has at least one mobile Communication device is set up to receive the communication signal and / or to receive the communication signals.

In this case, the communication signals that can be received at this position or their physical properties are measured at a position to be determined in the communication network. The position to be determined can then be determined using the map or the model and the measured physical quantities.

It can also be provided that the map or ^* w. to make the model independent of localization. The map can then be continuously updated using the autonomous mobile system and the inventive procedure, while using already created maps, mobile communication devices can be located in the communication network.

The invention or the inventive creation of the card is particularly suitable for use in the environment of a digital, cellular mobile radio system, such as a GSM network, and there, for example, for the localization of a GSM telephone (mobile phone).

When using the invention, only the data available to the mobile phone are used, and there are no costly changes to be made either to the GSM network or to mobile stations in the GSM network.

The invention is also suitable for use in the environment of further digital, cellular mobile radio systems, such as a WLAN, a network based on Bluetooth or a DECT. Network, and there for example for the localization of a DECT mobile phone.

In figures, an embodiment of the invention provides ones shown, which will be explained in ^* greater detail below.

Show it

Figure 1 procedure for creating a field strength map with subsequent localization according to an embodiment;

Figure 2 procedure for creating a field strength map with simultaneous localization according to an embodiment;

FIG. 3 sketch which shows a schematic comparison of model errors and measurement noise;

FIG. 4 omnidirectional robot according to an exemplary embodiment;

Figure 5 position detection by image processing;

Figure 6 is a sketch illustrating a comparison of a position estimate based on dead reckoning and camera navigation;

FIG. 7 first (sub) card for a first base station in the radio network according to an exemplary embodiment;

Figure 8 second (sub) card for a second base station in the radio network according to an embodiment. Exemplary embodiment: automatic mapping of the signal characteristics in a radio network

In the exemplary embodiment it is represented as follows:

First, the basics of mapping radio networks are described. A map for describing the propagation characteristics of base stations in a radio network and their creation is then described. It describes how the map is composed of a deterministic and a stochastic part. A location based on this map is also described.

This is followed by a description of an experimental verification of the procedure presented. Here a robot system for the automatic measurement of the field strengths is described as well as the quality of the cards.

1. Mapping

During normal operation, radio network receivers, such as B. DECT handsets, the field strengths of all available transmitters.

If the exact positions of the transmitting stations are known and a suitable physical model for the propagation of electromagnetic waves exists, this data can be used to estimate the position of the user.

In practical scenarios, the positions of the transmitting stations are mostly not known or are only vaguely known and are affected by changes. An exact physical model for wave propagation is difficult or impossible to create, especially for indoor areas, since it depends in a complex way on the properties of the building [6].

Therefore, a map of the field strengths of the individual transmitters is first created as follows. The terminals are then located using this card.

The field strength map can be determined by the measurement model

yk = = h (x _k ) + v _k (1)

describe the logarithmic measured signal strengths of all transmitters

? k, ς = 10 ^• logio (2) 1mW

with ζ = 1 ... N as a deterministic non-linear function hς (x _k ) depending on the position coordinates k ⁼ L ^x lk ^x 2 k _{.1 of} the receiver. _k describes the additive error of the logarithmic measurement. This corresponds to the multiplicative uncertainty that results from the unknown damping.

In the radio networks under consideration, the transmitters can be identified by a unique ID that is transmitted during communication. In this way, each measurement can be assigned to exactly one of the measurement equations, which significantly simplifies the estimation problem. Even in the localization phase, normal network operation should not be interfered with. Additional sensors on the user or the receiving device are not desired. Therefore, the localization has to be based solely on the field strength measurements of the receiver.

2. Mapping and localization

As shown in FIG. 1, a field strength map 120 is first created 115 in the mapping phase 100 by the robot 110 on the basis of measurements 111 and an estimated reference position 112. This map 120 consists of partial maps 121 which describe the transmission characteristics of a base station in each case.

In the later localization phase 150, several (human) users 160, who use a receiving device, e.g. B. carry a DECT cell phone or a PDA equipped with Wireiess LAN or Bluetooth, can be localized 165 using the map 120.

2 shows a simultaneous execution of the two phases, the mapping phase 100 and the localization phase 150.

The robot 110 updates the map 120 while the users 160 are already localized 165, 170.

This ensures that the card 120 is always up-to-date, in which changes to the network, such. B. Failure or replacement of individual transmitters, flow immediately.

Mapping phase (100)

Deterministic measurement model In the deterministic measurement model on which the map is created, it is assumed that the logarithmic received field strength of a transmitter decreases linearly with distance.

With two-dimensional position coordinates x _k , the linear drop in the logarithmic field strength of the base station ζ can be determined by the measurement equation

hς (x _k ) = - ^ (x _k - m _ς ) ^τ + Δ _ς (3).

In the N equations thus obtained (one for each base station), the total of 6N parameters from m _ς , _ς U d Δ _{ς are} to be estimated.

Stochastic sub-model

The stochastic sub-model of the uncertainties must take into account both the measurement noise and the deviation between the actual transmitter characteristics and the measurement model.

model uncertainties

The model uncertainties represent the difference between the exact model of the logarithmic received field strength and the deterministic measurement model h _ς (x _k ).

Figure 3 shows this difference.

On the one hand, this error stems from the model. On the other hand, it also represents local deviations that result from the specific properties of the building. These include z. B. to understand different damping or reflection properties of the different materials. The model uni- Securities are shown here with the mean μ ^ and the standard deviation

described.

measurement noise

The measurement noise represents the temporal variation of the measurements at a point. In FIG. 3 it can be seen, by way of example, how the measurement values deviate from the actual transmitter characteristic.

(2)

The measurement noise can also be determined by mean μ ^ _ς 'and (2)

Describe standard deviation σ.

Combination of uncertainties.

The uncertainty of the overall model is the combination of model uncertainty and measurement noise. In order to obtain a simple model, it is assumed here that the uncertainties are independent and uncorrelated.

In this case, the total uncertainty of each transmitter ς = 1, ..., N can be determined by a distribution with the mean

and the standard deviation

σv, ς><iv ¹ ,> ςf, + ( ^σ2 ,> ς ^λ2 (5)

describe,

Localization phase (150) In the localization phase 150, several terminals should be able to be localized at the same time.

Since the location information is required on the server side and not on the client side for location-dependent services, the localization should, if possible, be carried out on a central computer on which the map information is also stored.

This results in the following requirements for the localization algorithm.

The computing power should be distributed as freely as possible between the different localization applications. Therefore, an anytime algorithm with adjustable accuracy is required.

This provides a meaningful result at all times. An early termination of the calculation therefore only affects the accuracy of the position estimate, but not its availability. The accuracy of the estimate is increased with a longer runtime.

Since nonlinearities can occur when estimating the position using the field strength maps, a nonlinear filter method must be used.

This also uses a model for the movement of the user to improve the quality of the estimate and to enable the combination of measurements.

Suitable filtering methods for localization are, for example, the Progressive Bayes filter [7], which finds an optimal solution, or the PDSME filter [5]. The latter is suboptimal, but it is much simpler and has already been tested for use in localization problems in radio networks.

When locating a mobile communication device, one or more measurements of the electric field strength are then carried out at the location to be determined.

A location estimate can be carried out using these measurements and the field strength maps created previously.

For this purpose, for example, the method known from [12] based on pattern matching together with a spatial sampling of the field strength maps, the method known from [5] based on non-linear filter techniques, or a solution of the navigation equations can be used.

The solution of the navigation equations (6) with the location vector x, the measured field strengths E ^mess - E ^mess for the base stations or antennas 1 - N, and the location-dependent maps or models, the field strengths Eχ (x) - Ejτ (), happens, for example, by means of a zero search method, or by minimizing the quadratic equation error sum e (7).

0 (6)

N e = i (E _± () - E ;. (7) i = l

3. Experimental verification To verify the procedure presented above, measurements are first carried out in a WLAN, a so-called DUKATH network [8].

Since there is interest in location-based services such. For example, to transfer additional material to the PDA, the possibility must be offered to localize a large number of users at the same time.

However, the results are not limited to wireless LAN and the DUKATH network. You can also on other radio networks such. B. according to the Bluetooth or DECT standards.

technical structure

The map is created by an autonomous mobile robot (Fig. 4, 400 or 110).

It is an OmniBase platform [9].

For the mapping, the platform was provided with a special structure (Fig. 4) on which a Compaq iPAQ Pocket PC 401 with a Lucent Orinoco Wireless LAN card is attached.

As the robot 400 drives through the building, the iPAQ 401 measures the field strengths of all available transmission stations.

In a network based on IEEE 802.11 [10], the base stations continuously send beacon signals. These signals are used by mobile devices to find out the station to which they can get the best connection. These beacon signals are evaluated when measuring the field strengths. The network affiliation of the stations is irrelevant. The network card used for the measurement behaves passively, ie it does not send any signals.

The measurement results are transmitted to the robot 400 via a serial interface, where they are further processed together with the reference position estimated by the robot.

robot localization

The OmniBase platform

The omni-directional robot platform OmniBase has a modular structure. It has four identical wheel modules, each with a standard wheel.

Each drive module houses two drive motors, one for steering and one for driving the wheel. Since the eight motors of the wheel modules can be controlled independently of each other, the platform can be freely moved in three degrees of freedom (position and orientation).

The drives are coordinated by means of a low-level control in the platform. The platform always runs on instantaneous circles around the so-called ICR (Instantaneous Center of Rotation).

In order to achieve a low-vibration ride, all four wheels have to be adjusted on consistent tracks around the ICR.

The path planning takes place in a higher-level control computer, with the communication between the control computer and the low-level control of the platform taking place via a CORBAS interface.

It provides three functions: - Querying the position of the robot estimated by the odometry in global coordinates, setting the position in global coordinates and setting the speeds.

The speed vector u = [ ^χ kYkΨkF indicate the target speeds along the x and y axes of the global coordinate system and the angular speed of the orientation of the robot.

The platform is therefore controlled in global coordinates.

image processing

The robot 400 uses a ceiling camera 402 to locate itself on the uniform ceiling pattern in rooms.

The ceiling consists, for example, of white square ceiling panels with a side length of 1200mm. There are gray struts between the plates. The intersections of the ceiling struts are considered landmarks. They are on a 1200 mm grid.

A corner filter from the Intel Open Source Computer Vision Library [11] is used to identify the landmarks.

Four corner points, which form a square with a side length between 20 pixels and 35 pixels, are recognized as landmarks. A square is a figure made up of corner points if the

Side lengths are almost the same (tolerance: 2 pixels) and the angles are almost 90 °.

5 shows a camera image 500 in which a landmark was recognized.

The measured position of the robot x ^m [x ^llL y ψ j results from the actual position of the Landmar and its position in the camera image.

localization

Since the position of the robot is determined in global coordinates in the image evaluation, the measurement equation can be used

(8th)

simplify.

The system error measurement error v _k is assumed to be uncorrelated and normally distributed around the mean zero with the covariance R.

A prediction step is carried out in each time step. Where x? , the predicted location of the robot, determined by odometry. The error covariance is determined by

^c l = ^c ? -ι ^{+ Q} < ⁹ >

Simplified, where Q stands for the system error covariance matrix, C ^p for the priore and C ^e for the posterior covariance matrix.

Whenever a measurement date is available, ie a landmark has been recognized, a measurement step is carried out. This does not have to be the case in every time step. The so-called Kalman gain K _k can be simplified as because of the simple measurement equation

represent. and the posterior co-

(11)

and

- = c £ - κ (12)

The estimated position of the robot, - _k e is then set as the current position on the platform.

Fig. 6 shows the error of the pure dead reckoning compared to the camera navigation.

For this purpose, a corridor of a building was traversed ten times in the radio network, which corresponds to a total distance of approx. 700 m. After the ten passes, the deviation in orientation was more than 45 °. The deviation in position is several meters. If the image processing is added, the error of the position estimate is approximately 15 cm on average.

mapping

When creating the map, the 6 parameters from equation 3 are estimated for each base station, the reference position of the robot for x ^ and the measured field strength of the transmitter ς at that point for h _ς {x _k )

is used.

The estimate is made using the least squares method.

The partial cards for two base stations can be seen in FIGS. In addition, the actual measured values and the path traveled by the robot are shown. It can be seen in FIGS. 7 and 8 that the actual measured values are well described by the model.

The overall uncertainty of the system can be estimated from the deviation of the measurement model from the measurement values. The partial uncertainties are unknown. The measurement noise could be e.g. B. Determine that the mean and standard deviation of several measurements are calculated at one point. However, since the measurements are made along different paths, there is generally only one measurement per path point.

Since transmitting stations of the radio network are located on different floors of buildings, in some cases, than the robot, the accuracy of the model can be increased if necessary by expanding the position vector to a four-dimensional position vector.

In this case z would be added for the height and also ψ for the orientation in the x-y plane.

Since this also increases the number of parameters to be estimated from 6N to 15N, significantly more measurements would be required to create the map. Ideally, measurement trips would also be carried out on other floors.

In the above, a system according to the present invention was presented that automatically measures and maps the local signal characteristics of radio networks.

One application for this is the creation of maps with the help of which users can be localized in radio networks. The maps created describe the field strength distributions of all available transmitting stations in an analytical form. The model error and the measurement noise are modeled stochastically. This means that analytical stochastic filtering methods can be used for localization based on these maps.

Measurements were carried out with the omni-directional robot platform Omni-Base in a radio network.

The following writings are cited in this document:

[1] Peyrard, F., Soutou, C, Mercier, JJ: Mobile Stations Localization in a WLAN, in: Proceedings of the 25th Annual IEEE Conference on Local Computer Networks (LCN'00), Tampa, Florida (2000) 136-142

[2] Hassan-Ali, M., Pahlavan, K.: A New Statistical Model for Site-Specific Indoor Radio Propagation Prediction Based on Geometry Optics and Geometry Probability. IEEE Transactions on Wireless Communications 1 (2002) 112-124

[3] Howard, A., Siddiqi, S., Sukhatme, G.S .: An Experimental Study of Localization Using Wireless Ethernet. In: Appears in: Proceedings of the 4th International Conference on Field and Service Robotics, Japan (2003)

[4] Bahl, P., Padmanabhan, V.N. : RADAR: An In-Building RF-based User Location and Tracking System. In: Proceedings of IEEE INFOCOM 2000. Volume 2., Tel Aviv, Israel (2000) 775-784

[5] Rauh, A., Briechle, K., Hanebeck, U.D., Bamberger, J., Hoffmann, C.: Localization of DECT Mobile Phones Based on a New Nonlinear Filtering Technique. In: Proceedings of SPIE Vol. 5084, AeroSense Symposium, Orlando, Florida (2003)

[6] Fleury, B.H., Leuthold, P.E .: Radiowave Propagation in Mobile Communications: An Overview of European Research. IEEE Communications Magazine 34 (1996) 70-81

[7] Hanebeck, UD, Briechle, K., Rauh, A.: Progressive Bayes: A New Framework for Nonlinear State Estimation. In: Proceedings of SPIE Vol. 5099, AeroSense Symposium, lando, florida (2003)

[8] Hettler, A., Wigand, R.: DUKATH - Wireless University of Karlsruhe (TH) (as of July 2003) http: //www.uni- karlsruhe. de / _DUKATH /.

[9] Hanebeck, U.D., Saldic, N., Freyberger, F., Schmidt, G.: Modular wheelset systems for omnidirectional mobile robots. In: Robotics 2000 conference (VDI / VDEGesellschaft Mess- und Automatisierungstechnik), VDI reports 1552, Berlin (2000) 39 - 44

[10] IEEE: IEEE Std 802.11-1997 Information Technology - Telecommunications And Information Exchange Between Systems - Local And Metropolitan Area Networks-specific Requirements - Part 11: Wireless Lan Medium Access Control (MAC) And Physical Layer (PHY) Specifications. (1997)

[11] Intel Corporation: Open Source Computer Vision Library. (As of July 2003) http: // www. intel. co / research / mrl / research / opencv /

[12] DE patent application with application number 10345255.9

Claims

claims

1.Method for determining a map for describing a propagation behavior of a communication signal emitted by a base station in a communication network, in which a physical property of the communication signal associated with the respective selected position is measured at selected positions in the communication network, which physical property is the propagation behavior of the Characterized communication signal, in which a model for the propagation behavior is determined using the selected positions and the associated measured physical properties of the communication signal, in which the map is determined using the model for the propagation behavior, characterized in that the measurements is carried out at the selected positions using an autonomous mobile unit which is set up for receiving the communication signal and for measuring ng the physical property of the communication signal.

2. The method according to claim 1, in which the autonomous mobile unit is a mobile robot and / or the communication network is a radio network, in particular a radio network based on wireless LAN or Bluetooth or GSM or DECT, and / or the measured physical property of the Communication signal is a property of propagation of an electromagnetic field, in particular a field strength or a bit error rate or signal-to-noise ratio.

3.A method according to one of the preceding claims, in which the model describes the physical property of the communication signal as a function of a position in the communications network or the model describes a position as a function of the physical property.

4.A method according to one of the preceding claims, in which the model is a non-linear propagation model with a deterministic component and a stochastic component, which deterministic component describes a dependency between the physical property of the communication signal and a position in the communication network and which stochastic component describes an uncertainty of the deterministic part.

5.Procedure according to the preceding claim, in which the uncertainty of the deterministic part

Uncertainty of the communication signal, especially one

Measurement noise and / or an uncertainty of the dependency, in particular an uncertainty of the propagation model, are or are.

6.The method according to any one of the preceding claims, wherein the model is the card.

7.A method according to one of the preceding claims, in which a plurality of cards are determined, each card describing the propagation behavior of a communication signal emitted by a base station in the communication network.

8. Method according to one of the preceding claims, in which an overall map and / or an overall model of the overall propagation behavior of the communication signals is formed by previous base stations.

9. The method according to any one of the preceding claims, wherein the autonomous mobile unit has a position measuring system for determining the position of the autonomous mobile unit in the communication network and / or a path planning system

Determination of a trajectory in the communication network.

10. The method according to the preceding claim, wherein the position measuring system comprises an odometer and / or an image-processing position measuring system and / or a system for a dead reckoning.

11. The method according to any one of the preceding claims, wherein the selected positions are determined using the autonomous mobile unit.

12. Method according to one of the preceding claims, in which the card and / or the overall card are used / is used for planning and / or installation and / or commissioning and / or diagnosis of error states and / or quality assurance of the communication network.

13.The method according to one of the preceding claims, in which the model for the propagation behavior and / or the map and / or the models for the propagation behavior and / or the overall map are used / is used to localize at least one mobile communication device. device in the communication network, which is set up at least one mobile communication device for receiving the communication signal and / or for receiving the communication signals.

14.The method according to any one of the preceding claims, wherein the map and / or the overall map are updated.

15. The method according to the preceding claim, in which the update of the map and / or the overall map takes place simultaneously with the location of a mobile communication device.

16. Arrangement for determining a card for describing a propagation behavior of a communication signal emitted by a base station in a communication network, with an autonomous mobile unit with a measuring device for receiving the communication signal and for measuring a physical property of the communication signal, the physical property being a propagation behavior of the Characterized communication signal,

with the autonomous mobile unit which is set up to carry out measurements at selected positions in the communication network, the physical property of the communication signal associated with the respectively selected position being measurable by the autonomous mobile unit at selected positions in the communication network is

- With an evaluation unit with which, using the selected positions and the associated measured physical properties of the communication signal Model for the propagation behavior can be determined and with which the map can be determined using the model for the propagation behavior.

17. Computer program with program code means to carry out all steps according to claim 1 when the program is executed on a computer.

18. Computer program with program code means according to claim 17, which are stored on a computer-readable data carrier.