Characterization of dynamic radio signals in a radio frequency environment

Abstract

- A receiving entity configured to collect wireless signals within a radio frequency environment, - And a source entity that is mobile and transmitting the wireless signals within that environment A method implemented by a computing device for characterizing dynamic wireless signals in a radio frequency environment comprising: The method comprises: Receiving data regarding current measurements of wireless signals collected by the receiving entity, Determining data regarding source parameters (Φ) that depend on the source entity, Determining data regarding environmental parameters (Ψ) that depend on the radio frequency environment, -- Data regarding the current measurements, -- And data regarding the source and environmental parameters Determining updated values of the source and environmental parameters by calculating a similarity value determined between, Characterizing wireless signals within the environment based on the updated values of the source and environmental parameters A method.

Description

[Technical Field]

[0001] This disclosure relates to the art of characterizing radio signals having dynamic propagation in a radio frequency environment in a wireless communication system, and more particularly to monitoring such dynamic radio signals in a radio frequency environment having a moving radio source. Priority is claimed in European Patent Application No. 23306562.2, filed on September 20, 2023, the contents of which are incorporated herein by reference. [Background technology]

[0002] A radio signal propagating in a radio frequency environment from a transmitting entity (or source) to a receiving entity is considered. The characteristics and propagation conditions of such a radio signal depend, in particular, on the characteristics of both the transmitting and receiving entities (e.g., their respective locations in the environment, antenna power, dynamic characteristics, etc.), as well as the radio frequency environment.

[0003] In particular, in the context of wireless communication networks providing services to the environment, radio signals may propagate from a variety of sources that can be connected to the network (e.g., user equipment, vehicles, aircraft) to network infrastructure entities, such as base stations, gateways, and / or other network entities. Interference signals resulting from such propagation provide knowledge about the quality of the wireless communication network and the radio frequency characteristics of both the network infrastructure and the radio sources. Therefore, characterizing such radio signals in a radio frequency environment enables evaluation and further optimization of the radio characteristics of wireless communication systems and transmitting / receiving entities. For example, resource allocation or beamforming within the network architecture may be optimized based on monitoring such radio signals and / or knowledge of interference induced by the propagation of such radio signals in the environment.

[0004] Furthermore, in the specific case where the source (e.g., a connected vehicle moving on a road and communicating with the network infrastructure) is moving within a radio frequency environment, the propagation of the radio signal is dynamic (i.e., changes) as the radio signal source moves within the environment. Therefore, characterizing such radio signals on the scale of a radio communication network is complex because each receiving entity in the network infrastructure (e.g., a fixed base station) collects a time-varying, ephemeral signal as the moving source moves within and then outside the coverage of such receiving entities. Moreover, each receiving entity collects the radio signal within a given radio frequency environment with a known layout and physical characteristics of the environment, but has no knowledge of the transmitting entity (e.g., its position, trajectory, speed, etc.). Therefore, existing applications for characterizing dynamic radio signals within a radio frequency environment often provide little and / or inaccurate knowledge of the radio signal in the case of a moving source. [Overview of the project] [Means for solving the problem]

[0005] This disclosure aims to improve the situation.

[0006] A method is proposed for characterizing dynamic radio signals within a radio frequency environment using a computing device, and this radio frequency environment is: - At least one receiving entity configured to collect radio signals in a radio frequency environment, - At least one source entity moving toward a receiving entity, wherein the source entity transmits a radio signal within a radio frequency environment. Includes, This method, Receiving data on current measurements of radio signals collected by at least one receiving entity, Determining data related to source parameters, where the source parameters depend on the source entity. This involves determining data related to environmental parameters, where the environmental parameters depend on the radio frequency environment. --Data regarding current measurements, --At least data regarding source parameters and environmental parameters By calculating the similarity value determined between them, the updated values ​​for the source parameter and the updated values ​​for the environment parameter are determined. Characterizing radio signals in a radio frequency environment based on updated source parameter values ​​and updated environmental parameter values. Includes.

[0007] Preferably, the proposed disclosure enables the inference of radio frequency characteristics of a dynamic radio frequency environment based on prior knowledge of such an environment. Preferably, the proposed method enables the characterization of radio signals in such an environment when a source entity emitting such radio signals is moving within the environment.

[0008] In particular, this disclosure advantageously proposes distinguishing between two types of radio frequency features within an environment: source-specific features via source parameters and environment-specific features via environment parameters. Source parameters may be specific to the movement and radio transmission characteristics of a source entity, while environment parameters may be specific to the configuration of the network infrastructure within the environment, as well as to the radio propagation and reception characteristics within different zones of such an environment. For example, source parameters may change when a separate source entity is considered, or when a specific source entity moves. Environment parameters may change depending on the part (or zone) of the environment having different propagation characteristics due to obstacles present in the environment (e.g., the presence of buildings, trees, etc.) that affect the propagation of radio signals. Thus, this disclosure advantageously enables the distinction between various types of parameters having various dynamisms (one moving, the other stationary) and radio characteristics, both types of parameters affecting radio signals received by receiving entities of the network infrastructure. By distinguishing source parameters from environmental parameters, the characterization of dynamic radio signals in a radio frequency environment advantageously allows for the separate utilization of the dependence of the propagation characteristics of radio signals in the environment on each of the source and receiving entities. For example, updated knowledge of the environment (i.e., updated values ​​of environmental parameters) enables the identification of constant and / or at least predictable information within the radio frequency environment. Furthermore, updated values ​​of environmental parameters enable a good characterization of the next moving source entity that will transmit radio signals in the future.

[0009] Accordingly, this disclosure enables the characterization of dynamic radio signals in radio frequency environments by determining updated specific information, as described, so that such updated specific information can be utilized and / or reused for different radio entities. Thus, this disclosure saves computing resources on network infrastructure and provides accurate and updated information on radio frequency environments and the propagation of radio signals within such radio frequency environments, while taking into account the multi-dependencies and differentiated dynamics and radio characteristics contained in such information.

[0010] A radio frequency environment can be understood as a part of the physical environment in which a radio communication network is deployed (at least partially) and enables radio communication between entities in the environment through the exchange of radio signals. Such a radio frequency environment may be, for example, a traffic area, a roadway, or airspace.

[0011] Characterizing dynamic radio signals within a radio frequency environment can be understood as determining or estimating the values ​​of parameters that can describe the quality of radio frequency communications within that environment. For example, characterizing dynamic radio signals within an environment can be understood as determining updated information about the dynamic characteristics of mobile entities within the radio frequency environment regarding propagation characteristics and differences in the radio frequency environment, or determining the development of the radio frequency environment with respect to signaling and radio propagation characteristics. Characterizing dynamic radio signals can also be understood as monitoring radio signals propagating within a radio frequency environment.

[0012] A dynamic radio signal, also called a dynamic radio frequency signal, can be understood as a radio frequency signal transmitted by a source entity and received by a receiving entity within a radio frequency environment. A radio signal is called dynamic because it is at least due to the movement of the source entity within the radio frequency environment. The receiving entity may be stationary (i.e., at rest, not moving) within the radio frequency environment. Unless otherwise specified, the term radio signal as used in this disclosure refers to a dynamic radio signal. In particular, such a radio signal may undergo several phenomena as it propagates through the radio frequency environment. For example, a radio signal may undergo reflection, refraction, absorption, and diffraction within the radio frequency environment. In another example, a radio signal may interfere with other radio signals and / or various physical obstacles in the environment (e.g., vehicles, buildings, trees, infrastructure, etc.), thus inducing interference within the radio frequency environment. Therefore, since propagation is influenced by various parameters relating to both the source and receiving entities, such a radio signal may also be called, or include, an interference signal, and characterizing a dynamic radio signal may include characterizing a dynamic interference signal. In particular, transmitted radio signals are different from the corresponding received radio signals. Subsequently, dynamic radio signals in the radio frequency environment can be characterized based on at least a measurement of such received radio signals by the source entity.

[0013] A source entity can be understood as a radio entity configured to emit radio signals. Such radio frequency signals may be, for example, control messages, data messages, broadcasted information, sidelink information, etc. A source entity may be, for example, a connected vehicle, user equipment, a mobile device, or an IoT device. In particular, a source entity is considered dynamic (i.e., moving) within the radio frequency environment. Therefore, the radio signal received by the receiving entity will vary depending on its position, the trajectory of the source entity, and the propagation conditions of the portion of the environment between the receiving entity's position and the source entity's position.

[0014] A receiving entity can be understood as a network entity in a radio frequency environment configured to collect radio signals propagating within the environment. The radio frequency environment may, in particular, include multiple receiving entities. Such receiving entities may have fixed (i.e., immobile) locations within the environment. A receiving entity may, for example, be a base station managing geographically divided coverage zones within the radio frequency environment. In particular, each receiving entity may collect radio signals within its coverage zone in the radio frequency environment.

[0015] A computing device can be understood as a network entity linked to a radio frequency environment and configured to characterize dynamic radio signals in such an environment. Such a computing device may be, for example, a core network entity, a network management device, or any other network entity configured to collect and process radio frequency signals. In one embodiment, such a computing device may be linked to a plurality of receiving entities from the radio frequency environment and may be configured to receive data relating to measurements of radio signals performed by the plurality of receiving entities.

[0016] The similarity value can be understood as a value that reflects the level of similarity or closeness between various data. In particular, it can be understood as a value that reflects the level of similarity between current data derived from current measurements in a radio frequency environment and other data, such as previously stored setting data derived from previous measurements in a radio frequency environment.

[0017] Data relating to current measurements of a radio signal can be understood as any information directly or indirectly derived from measurements collected by one or more receiving entities. For example, such data may be current measurements of a radio signal collected by a receiving entity. Such data may also include information related to such current measurements, such as a timestamp, an identifier of the receiving entity that collected such current measurements, and / or specific reception conditions for the current measurements.

[0018] Data relating to source parameters can be understood as arbitrary data directly or indirectly derived from the values ​​of one or more source parameters relating to a source entity. Such data relating to source parameters is specific to each source entity. Such data relating to source parameters may be set, received, calculated, determined, measured, inferred, and / or estimated by a computing device. Source parameters can be understood as parameters that depend on the source entity emitting the radio signal and affect the characterization of the radio signal. In particular, such source parameters may change depending on the movement and / or radio capability of the source entity.

[0019] Determining updated values ​​for source parameters can be understood as determining updated information about those source parameters. Such updated values ​​may, in particular, reflect the current (or most recent) status of the source entity in the radio frequency environment at the present time when the proposed method is implemented (e.g., the updated trajectory, position, and velocity of the source entity).

[0020] Data relating to environmental parameters can be understood as arbitrary data directly or indirectly derived from the values ​​of one or more environmental parameters relating to the radio frequency environment. Such data relating to environmental parameters may be determined, stored, indexed, and more generally accessible to computing devices and / or any network entities of the environment's network infrastructure. Environmental parameters can be understood as parameters that may reflect the propagation characteristics of radio signals within a radio frequency environment, influencing the characterization of radio signals. In particular, such environmental parameters may rely at least in part on predictable, forecastable, constant, and / or pre-configured information relating to the radio frequency environment.

[0021] Determining updated values ​​for environmental parameters can be understood as determining updated information about those environmental parameters. Such updated values ​​may, in particular, reflect the current (or most recent) conditions of the radio frequency environment at the present time when the proposed method is implemented (e.g., updated re-subdivision of signal attenuation in the environment, updated coefficient values ​​of the path loss function).

[0022] Calculating a similarity value between data relating to current measurements and data relating to at least the source and environmental parameters can be understood as determining the level of similarity between a value reflecting the current and up-to-date state of the radio frequency environment and the currently used value relating to the radio frequency environment. In particular, such a similarity value may be obtained by inference based on current measurements of the radio signal and the permitted data. Thus, the similarity value can be understood as a function dependent on the values ​​of the source and environmental parameters. The updated values ​​of the source and environmental parameters can then be determined as values ​​that maximize the level of similarity between the data being compared. Similarly, the similarity value may reflect a gap or distance between the data being compared. In such cases, the updated values ​​of the source and environmental parameters can be determined as values ​​that minimize the gap between the data being compared.

[0023] The following features can be implemented in the relay method either separately or in combination with each other, at will.

[0024] According to one embodiment, the data relating to source parameters and the data relating to environmental parameters are - Previous estimates of source parameters, - Previous estimates of environmental parameters, -Values ​​stored in the lookup table, - A value pre-configured by the network entity in the radio frequency environment. - A list of candidate values ​​for the source parameter. - List of candidate values ​​for environmental parameters It is determined based on at least one of the following elements.

[0025] For example, in the case of a series of iterations of the proposed estimation method, previous estimates of the source and environment parameters can be understood as the values ​​of the source and environment parameters determined in previous iterations of the proposed method.

[0026] According to one embodiment, the similarity value further depends on prior data relating to previous measurements of radio signals in a radio frequency environment. According to such an embodiment, such prior data is -Previous measurements of radio signals in a radio frequency environment, -Previous estimated location of the source entity in the radio frequency environment, -Data derived from previous values ​​of source parameters, - Data derived from previous values ​​of environmental parameters It includes at least one of the following elements.

[0027] As a result, the proposed method relies on currently available data that is available to computing devices. Such currently available data, in particular, relies on previous data obtained directly or indirectly from previous measurements of radio signals in a radio frequency environment at previous times.

[0028] Previous measurements of the radio signal may be measurements collected by the receiving entity at an earlier time and provided to the computing device at or before the proposed method is implemented.

[0029] Data derived from previous values ​​of source parameters (each an environmental parameter) may be data obtained directly or indirectly from previous values ​​of source parameters (each an environmental parameter). Such data may be, for example, previous values ​​of source parameters (each an environmental parameter) or current values ​​of source parameters (each an environmental parameter) that are used and considered as the most up-to-date information (each an environmental parameter) regarding the source parameters when the method is performed. Such data may also be scalar values ​​and / or probability functions determined based on such values ​​of source parameters. For example, the data derived from previous values ​​(or current values) of source parameters (data derived from previous values ​​of each environmental parameter) may be p(Φ|Z n -) and p(Ψ|Z n It can be expressed as -), where Φ and Ψ are the previous (or current) values ​​of the source and environment parameters, and Z n - indicates the previous measurement of the wireless signal.

[0030] According to one embodiment, the source parameter is: - Source entity speed and, - Source entity's transmitted power and, - Source entity antenna gain and It includes at least one of the following elements.

[0031] According to one embodiment, the environmental parameters are: - Spatial redistribution of radio frequency signal attenuation in a radio frequency environment, - The coefficients of the modeling function for signal attenuation in a radio frequency environment, - The coefficients of the path loss function in the radio frequency environment, - Shadowing parameters in the radio frequency environment and It includes at least one of the following elements.

[0032] Shadowing parameters can be understood as parameters that characterize the shadowing path loss model in a radio frequency environment. For example, shadowing is,

number

[0033] According to one embodiment, the data relating to the source parameters, the data relating to the environmental parameters, the updated values ​​of the source parameters, and the updated values ​​of the environmental parameters are - Scalar values ​​and, - Probability function and It is one of the elements.

[0034] When the updated value is expressed as a probability function, the data derived from the previous values ​​of the source parameter and the data derived from the previous values ​​of the environmental parameter are used to determine the previous measurement of the radio signal (Z n The data regarding -) can be expressed as a given conditional probability.

[0035] According to one embodiment, the similarity value is - The likelihood determined by a Bayes-based algorithm, - Distance determined by a Euclidean-based algorithm and It is derived from at least one of the elements.

[0036] According to one embodiment, the similarity value is derived from the likelihood determined by a Bayesian-based algorithm, and the updated value of the source parameter is determined by marginalizing the similarity value for the data derived from the previous value of the environmental parameter, and the updated value of the environmental parameter is determined by marginalizing the similarity value for the data derived from the previous value of the source parameter.

[0037] According to one embodiment, the marginalization of the similarity value for the data derived from the previous value of the environmental parameter and for the data derived from the previous value of the source parameter are respectively

Number

Number

[0038] According to one embodiment, the updated value of the source parameter and the updated value of the environmental parameter are respectively p(Z n |Z n ,Φ)×p(Φ|Z n -), and p(Z n |Z n ,Ψ)×p(Ψ|Z n-) is proportional, and here, -p(Z n |Z n -,Φ) is a similarity value that is marginalized to the data derived from the previous values ​​of the environmental parameters. -p(Z n |Z n -,Ψ) is a similarity value that is marginalized to the data derived from the previous values ​​of the source parameters, -p(Φ|Z n -) is data derived from the previous value of the source parameter, -p(Ψ|Z n -) is data derived from previous values ​​of the environmental parameter (Ψ).

[0039] According to one embodiment, the similarity value is

number

[0040] According to such an embodiment, the location probability of the source entity is, p(s 1:n |Z n -,Φ,Ψ)=p(s n |s n -,Z n -,Φ,Ψ)×p(s n -|Z n -,Φ,Ψ) It is expressed as,

number

[0041] According to one embodiment, the updated values ​​of the source parameters and the updated values ​​of the environment parameters are

number

number

number

number

[0042] According to one embodiment, the method further includes transmitting target information to a network entity based on updated source parameter values ​​and updated environment parameter values.

[0043] As a result, updated source parameter values ​​and environmental parameter values ​​can be used to provide specific target information to network entities, such as to distinguish relevant information from obsolete information. For example, a computing device may notify a specific receiving entity that, based on the updated rate value of the source entity, data about the source parameter is stored for a given period and can be discarded after such a period (e.g., corresponding to the time it takes for the source entity to move out of the coverage zone of such receiving entity). Thus, updated source parameter values ​​and environmental parameter values ​​enable optimization of the storage and computing capabilities of the network infrastructure.

[0044] According to one embodiment, this method is Sending the updated source parameter values ​​to a selected first set of network entities included in the network infrastructure, Sending updated environmental parameter values ​​to a second selected set of network entities included in the network infrastructure. It also includes.

[0045] According to such an embodiment, a first set of network entities and a second set of network entities are, - The updated values ​​of the source parameters, - Update values ​​for environmental parameters, - The difference between the updated value and the previous value of the source parameter, - The difference between the updated environmental parameter value and the previous value, -The current measured value and, - The location of network entities included in the network infrastructure within the radio frequency environment and It is selected depending on at least one of the elements.

[0046] Another aspect of this disclosure proposes a computer-readable non-temporary recording medium on which the software is registered to implement this method when the software is executed by a processor.

[0047] Another aspect of the present disclosure proposes a computer device configured to characterize dynamic radio signals in a radio frequency environment, the computer device comprising an interface for connecting to a receiving entity in the radio frequency environment and a processing circuit for implementing the proposed method.

[0048] Another aspect of this disclosure proposes a computing device included in a network infrastructure that manages a wireless communication network deployed in a radio frequency environment, the computing device comprising at least one processing circuit for implementing the proposed method. [Brief explanation of the drawing]

[0049] [Figure 1] This is a schematic diagram of a radio frequency environment according to one embodiment.

[0050] [Figure 2] This is a schematic diagram of the architecture of the receiving entity, source entity, and computing entity according to one embodiment.

[0051] [Figure 3] This flowchart shows the steps for characterizing a dynamic radio signal in a radio frequency environment according to one embodiment. [Modes for carrying out the invention]

[0052] Refer to Figure 1 here. Figure 1 shows a portion of a radio frequency environment 1 according to one embodiment. Such a radio frequency environment 1 may be a traffic area, such as a road traffic area within an air traffic area. The radio frequency environment 1 is at least partially covered by a radio communication network that enables communication and signaling between various radio devices and radio entities. For that purpose, the radio frequency environment 1 may include a portion of a network infrastructure 3, 4 that supports the radio communication network. Such network infrastructure 3, 4 may comprise a plurality of network devices, such as access points, WLAN controllers, base stations, core network entities, network management centers, etc. In particular, network infrastructure 3, 4 may include network devices configured to manage, observe, measure, collect, and / or relay radio frequency signals transmitted by radio devices in the radio communication network. Such network devices may be called receiving entities 3. Such receiving entities 3 may be, for example, base stations. Such receiving entities 3 may have a fixed location within the radio frequency environment 1.

[0053] Furthermore, each such receiving entity 3 may be associated with a separate cell, also called coverage zones Z1, Z2, and Z3, in the radio frequency environment 1. Such coverage zones Z1, Z2, and Z3 are represented, for example, as closed dotted lines in Figure 1. In other words, each receiving entity 3 may collect radio frequency signals, simply called radio signals, within a demarcated geographic area corresponding to coverage zones Z1, Z2, and Z3. Coverage zones Z1, Z2, and Z3 may overlap, for example, in the case of coverage zones Z1 and Z2, and coverage zones Z2 and Z3. Such coverage zones Z1, Z2, and Z3 may have fixed coverage locations within the radio frequency environment 1. Such fixed coverage locations of coverage zones Z1, Z2, and Z3 may, in particular, depend on the fixed locations of the receiving entities 3.

[0054] Coverage properties and resource management within the radio frequency environment 1 may be managed by at least one network management device. Such a network management device may be part of the network infrastructure, or it may be, for example, a core network entity or a network management center. Such a network management device may be a remote server in particular for receiving entity 3. In particular, such a network management device may have knowledge of the radio frequency environment 1. In particular, the network management device may have knowledge of the fixed location of receiving entity 3 and / or the fixed coverage locations of coverage zones Z1, Z2, and Z3.

[0055] The radio frequency environment 1 may also include, in particular, radio devices configured to transmit radio signals within the radio frequency environment 1. Such radio signals may be, for example, data messages and / or control messages transmitted to other radio devices and / or network devices. Such radio devices may be referred to as source entities 2, as they are the sources of radio frequency signals collected by receiving entity 3 in the context of this disclosure. Source entity 2 may be, for example, user equipment, connected vehicles, aircraft, and / or IoT devices. In particular, in the context of this disclosure, source entity 2 is considered non-static (or dynamic) with respect to receiving entity 3. For example, as shown in Figure 1, source entity 2 may be a vehicle moving along a road section within the radio frequency environment 1, such that the location of source entity 2 changes over time as source entity 2 moves within the radio frequency environment 1. In particular, as source entity 2 moves, source entity 2 may switch between separate coverage zones Z1, Z2, and Z3. For example, referring to Figure 1, the moving source entity 2 is in coverage zone Z1, and as source entity 2 moves in the direction of the arrow shown, it will successively pass through coverage zones Z2 and Z3. From the perspective of a given receiving entity 3, receiving entity 3 collects only the radio signals transmitted by source entity 2 during a defined time period, while the location of source entity 2 belongs to the corresponding coverage zone.

[0056] In the circumstances of this disclosure, a radio signal transmitted by source entity 2 propagates through the radio frequency environment 1 according to its physical and radio characteristics, depending on several factors relating to source entity 2 transmitting the radio signal, receiving entity 3 collecting such radio signal, and the radio frequency environment 1 through which such radio signal propagates. In particular, such signal propagation may change the behavior of the radio signal once it is transmitted by source entity 2 and may undergo and / or induce several phenomena when the radio signal interacts with the radio frequency environment 1. For example, a radio signal may induce interference effects, and therefore the radio frequency environment 1 may be a radio interference environment 1, where interference effects occur when the radio signal is transmitted by source entity 2 and collected by receiving entity 3, and the radio signal transmitted on the source entity side is different from the corresponding received radio signal on the receiving entity side. Furthermore, such a radio interference environment 1 is dynamic because the propagation of the radio signal (and therefore the interference signal) changes as source entity 2 moves relative to the radio frequency environment 1. In another example, because source entity 2 moves relative to the radio frequency environment 1, the behavior of the radio signal is affected throughout its propagation, and the radio signal may be subject to several other wave phenomena such as reflection, refraction, diffraction, and absorption. Therefore, monitoring such a radio signal requires characterizing such a dynamic radio signal.

[0057] One aspect of this disclosure proposes characterizing radio signals within a radio frequency environment 1. To that end, as shown in Figure 2, a computing device 4 is proposed to perform the steps detailed in Figure 3 to estimate the physical and behavioral characteristics of a radio signal propagating through the radio frequency environment 1 when a radio signal is transmitted by a moving source entity 2. In particular aspects of this disclosure, it is also proposed to characterize interference within the radio frequency environment 1 when a radio signal induces interference effects. Thus, the disclosure applicable to radio signals may also be applicable to specific situations of interfering signals for characterizing dynamic interference within the radio frequency environment 1.

[0058] especially, - Source characteristics Φ, which can be understood as characteristics or factors of wireless signal propagation that depend on source entity 2, -Environmental characteristics Ψ that can be understood as characteristics of radio signal propagation that depend on propagation conditions in the radio frequency environment 1 and We propose estimating wireless signal characteristics by distinguishing between them.

[0059] In reality, radio signal propagation occurring within a radio frequency environment 1 depends on several factors. Such factors may, in particular, be source characteristics (or parameters) Φ relating to source entity 2. For example, source parameter Φ is: - The speed of source entity 2, and / or - The trajectory of source entity 2, and / or -Transmit power of source entity 2, and / or - Antenna gain of source entity 2 It may include.

[0060] The propagation of radio signals within a radio frequency environment 1 may also depend on environmental characteristics (or parameters) Ψ relating to the radio frequency environment 1. For example, the environmental parameter Ψ is: - Spatial redistribution of radio frequency signal attenuation within radio frequency environment 1, and / or - Coefficients of the modeling function for signal attenuation in radio frequency environment 1, and / or - The coefficients of the predefined path loss function in the radio frequency environment 1, and / or - Shadowing parameters in radio frequency environment 1 It may include.

[0061] Therefore, the proposed estimation method relies on the fact that the propagation of radio signals depends on multiple factors with varying levels of predictability. In fact, some parameters, such as the spatial repartitioning of radio frequency signal attenuation in radio frequency environment 1, have slow fluctuations on the network side and are repeatable and / or predictable, while other parameters, such as the velocity or trajectory of source entity 2, are unknown on the network side.

[0062] As a result, a radio signal transmitted by a moving source entity 2 and received by one or more receiving entities 2 has a dynamic nature because the source entity 2 is moving. Furthermore, since the propagation conditions between the source entity 2 and each receiving entity 3 occur in parts of the radio frequency environment 1 where physical obstacles (e.g., trees, buildings, etc., as shown in Figure 1) and other physical properties that affect signal propagation are known, such a radio signal also has a partially predictable nature.

[0063] Refer to Figure 2 here. Figure 2 shows a schematic architecture of various entities 2, 3, 4, and 5 involved in the radio frequency environment 1 on which dynamic radio signals propagate and which we propose to be characterized by this estimation method. Such entities may include, in particular, a computing device 4, one or more receiving entities 3, one or more source entities 2, and one or more remote servers 5.

[0064] A computing device 4 for implementing an estimation method is proposed, which will be explained in more detail while illustrating Figure 3. The computing device 4 may, in particular, be a network device included in a network infrastructure that supports a wireless communication network deployed in a radio frequency environment 1. For example, the computing device 4 may be a network management device that manages network deployment in or a part thereof in the radio frequency environment 1. The computing device 4 may be a remote server relative to the radio frequency environment 1. The computing device 4 may also be a core network entity of a wireless communication network. The computing device 4 may be configured to manage network resources for providing connectivity within the radio frequency environment 1. The computing device 4 may also be configured to provide system information to network devices (e.g., to a receiving entity 3) and to other devices in the radio frequency environment 1 (e.g., to a source entity 2, such as a user device). The computing device 4 is configured to at least implement an estimation method for characterizing radio signals propagating within the radio frequency environment 1. For that purpose, such a computing device 4 may include at least processing circuitry for implementing the estimation method. Such processing circuitry may, in particular, rely on a processing unit 41 and a memory unit 42. Computing device 4 may also receive and / or transmit signal and data messages to and from other network entities and / or other devices. For example, computing device 4 may receive data from receiving entity 3 regarding collected radio signals. Computing device 4 may also retrieve data stored in a database from one or more remote servers 5. Computing device 4 may also transmit information (e.g., via broadcast) to receiving entity 3 and / or other devices such as source entity 2. For this purpose, computing device 4 may also include a communication interface 40.

[0065] A receiving entity 3 is configured to collect at least radio signals transmitted by one or more source entities 2. Such a receiving entity 3 may be, for example, a base station. In particular, each receiving entity 3 is configured to collect radio signals propagating within the corresponding coverage zones Z1, Z2, and Z3 managed by that receiving entity 3. For example, referring to Figure 1, a receiving entity 3 on the left side of Figure 1, managing coverage zone Z1, may collect radio signals transmitted by a source entity 2 corresponding to a black vehicle. If such a source entity 2 moves in the direction of the indicated arrow and enters coverage zone Z2 (and Z3 respectively), then a receiving entity 3 in the center of Figure 1 (and the right side respectively), managing coverage zone Z2 (and Z3 respectively), may collect radio signals transmitted by such a source entity 2. In another example, referring to Figure 1, a receiving entity 3 on the right side of Figure 1, managing coverage zone Z3, may collect radio signals transmitted by a source entity 2 corresponding to a white vehicle. For that purpose, each receiving entity 3 may include a communication interface 30. Such a communication interface 30 may include means for radio frequency communication (e.g., a receiving antenna). Furthermore, each receiving entity 3 may also be configured to determine information to transmit to other devices in the radio frequency environment 1, such as a source entity 2, other receiving entities 3, and / or a computing device 4. For that purpose, each receiving entity 3 may include a processing unit 31 and a memory unit 32.

[0066] Source entity 2 is configured to transmit radio signals at least within the radio frequency environment 1. Such transmitted radio signals may be transmitted data messages to, for example, an entity on the network infrastructure (e.g., in the context of V2N communication), to another source entity (e.g., in the context of V2V communication between vehicles), or to any other device in general (e.g., in the context of V2X communication). For this purpose, each source entity 2 may include a processing unit 21, a memory unit 22, and a communication interface 20 including means for radio frequency communication (e.g., a radiating antenna).

[0067] The remote server 5 may be configured to store a database relating to the wireless communication network. Such a database may be accessed wirelessly by devices such as network entities (e.g., computing device 4, receiving entity 3).

[0068] Refer to Figure 3 here. Figure 3 details the steps of the proposed method for estimating the characteristics of a dynamic radio signal propagating within a radio frequency environment, as shown in Figure 1. Such steps may be performed by a computing device 4, as described below. However, alternatively, such a method may be performed by another network entity having data acquisition and processing capabilities. For clarity, only one source entity 2 is considered to be the source of the radio signal. However, such a method may be generalized to multiple source entities 2. This method is performed within a radio frequency environment 1 (for example, the computing device 4 has access to currently available information, and the source entities 2 have access to the currently unknown location s within the environment 1). n Current time t related to the current situation within (having) n This method is thought to be implemented at the following time points: t1, t2, ..., t n-1 This can be carried out following the previous iteration.

[0069] In particular, we propose two distinct embodiments of the estimation method. In a preferred first embodiment, the estimation method may be based on a Bayesian method. In a second embodiment, the estimation method may be based on a Euclidean method. The following description of the steps of the estimation method details both embodiments.

[0070] In step 300, data relating to radio signals transmitted in radio frequency environment 1 is collected. Such data may be current measurements of radio signals performed by receiving entity 3 in each of the coverage zones Z1, Z2, and Z3 of environment 1. In one embodiment, such data may be interference signals measured by receiving entity 3 from radio signals transmitted by a given source entity 2.

[0071] In particular, in step 300, the computing device 4 may concentrate data on current measurements of radio signals collected by multiple receiving entities 3 into their respective partitioned coverage zones Z1, Z2, and Z3. Thus, such data is measured by the receiving entities 3 and transmitted to the computing device 4. Such data on current measurements of radio signals may also include data associated with the conditions of the measurement of such radio signals, such as a timestamp, the conditions for receiving such radio signals by the receiving entities 3, an identifier, the location of the receiving entities 3, and / or any other information relating to the receiving entities 3.

[0072] Therefore, in step 300, the computing device 4 acquires data relating to current measurements of radio signals currently occurring in the radio frequency environment 1. The computing device 4 may also acquire data relating to the conditions under which such radio signals are measured by the receiving entity 3 (or more entities) 3, the time at which such radio signals are measured, and the radio conditions of such radio signals (e.g., the received transmit power of the radio signals). The computing device 4 may also acquire any other data determined or estimated by the receiving entity based on such received radio signals, which enables the characterization of current radio signals, their propagation, and / or source entity 2 that emits such radio signals.

[0073] In one embodiment, such data relating to current measurements of a radio signal may be collected immediately by the computing device 4, i.e., such measurements are received by the receiving entity 3 and immediately transmitted to the computing device 4. In another embodiment, such measurements may be transmitted by the receiving entity 3 within a predefined time window, such that the computing device 4 receives the data from the receiving entity 3 in batch processing at a predefined time. For example, such a predefined time window may be pre-configured within the network infrastructure, such that the receiving entity 3 is configured to potentially store data relating to received radio signals and to transmit those measurements and associated data within such a predefined time window. Thus, the expression “current measurements” can be understood as measurements collected in near real-time in the radio frequency environment 1, or as the most updated measurements collected in the environment 1 with respect to a predefined time window (or a predetermined periodicity).

[0074] In step 300, the computing device 4 takes the previous measurement of the radio signal, i.e., the current time t nData relating to measurements taken at a prior time may be obtained. Such prior measurements may be obtained in step 300, or at the current time t when step 300 occurs. n The preceding time t1, t2, ..., t n-1 In this context, it may be transmitted to computing device 4 by receiving entity 3.

[0075] In an optional step following step 300 (not shown in Figure 3), data cleaning may be performed on the collected data relating to the current measurement of the radio signal. In such an optional data cleaning step, the data received in step 300 may also be filtered based on predefined geographical and / or temporal criteria. For example, measurement values ​​collected with timestamps exceeding a predefined time interval may be discarded, as such measurement values ​​may be considered outdated. In another example, if the estimation method is performed on a demarcated geographical area of ​​the radio frequency environment 1, measurement values ​​collected by a receiving entity 3 having a fixed location outside such demarcated geographical area may be discarded.

[0076] In step 310, data relating to the source parameter Φ is determined. The source parameter Φ may be, for example, the velocity v of source entity 2, the transmitter power of source entity 2, and / or the antenna gain of source entity 2.

[0077] Such data relating to the source parameter Φ may be the value of the source parameter Φ. Such a value of the source parameter Φ may be a value output in a previous iteration of the proposed estimation method, or it may depend on such a value. Such a value of the source parameter Φ may also be determined based on data retrieved from a database stored, for example, on a remote server 5. In such a case, the data relating to the source parameter Φ may be selected by the computing device 4 within the database, depending on the radio frequency environment 1 being characterized. For example, if the part of environment 1 under consideration is a country road or a highway, the data relating to the source parameter Φ corresponding to the speed of the source entity 2 may be a speed value in the range of 70 km / h to 130 km / h. The value of the source parameter Φ may also be set by the computing device 4, for example, as an initialized value of the source parameter Φ.

[0078] In the first embodiment of this disclosure, data relating to the source parameter Φ can be expressed as a probability function. For example, such data relating to the source parameter Φ can be expressed as p(Φ|Z n It can be expressed as -).

[0079] Here, -Φ is a set of source parameters, -Z n - represents data relating to previous measurements of the radio signal collected by computing device 4.

[0080] In a second embodiment of the present disclosure, data relating to the source parameter Φ may be represented as a scalar value. In such a second embodiment, the data relating to the source parameter Φ may be a list of discrete candidate values ​​for the source parameter Φ. In particular, such discrete candidate values ​​may be listed based on previous values ​​of the source parameter Φ (for example, as output by the estimation method in a previous iteration). For example, if the previous value of the transmitter power of source entity 2 is 10 kilowatts (kW), the list of candidate values ​​for the source parameter Φ may be a finite number of values ​​around 10 kW (e.g., 100), such as [9, 9.05, 9.1, ..., 10.9, 10.95, 11].

[0081] In step 320, data relating to the environmental parameter Ψ is determined. The environmental parameter Ψ may include parameters of the propagation loss model in the radio frequency environment 1, such as a set of spatial subdivision values ​​for radio frequency signal attenuation in the radio frequency environment 1, coefficients of the modeling function for signal attenuation in the radio frequency environment 1, coefficients of the path loss function in the radio frequency environment 1, and / or shadowing parameters in the radio frequency environment 1.

[0082] For example, the propagation loss model may be modeled by a signal attenuation map of environment 1, and such a signal attenuation map is known by the network infrastructure, particularly by computing device 4. For example, the propagation loss model may take the form of a lookup table (or LUT) that maps propagation loss depending on the location of source entity 2. In another example, the propagation loss model may take the form of a path loss function of a radio signal propagating from source entity 2 to a given receiving entity 3, and this may be, for example, A + B × log 10 (d 2-3 ) It can be expressed as follows.

[0083] Here, -A and B are coefficients of the path loss function, -d 2-3This is the distance between source entity 2 and receiving entity 3.

[0084] Therefore, the coefficients A and B of the path loss function can be included in the set of environmental parameters Ψ.

[0085] Similar to the source parameter Φ, the data for the environmental parameter Ψ may be the value of the environmental parameter Ψ. Such a value for the environmental parameter Ψ may be a value output in a previous iteration of the proposed estimation method, or it may depend on such a value. Such a value for the environmental parameter Ψ may also be determined based on data retrieved from a database, for example, data stored on a remote server 5. In such a case, the data for the environmental parameter Ψ may be selected by the computing device 4 in the database, depending on the radio frequency environment 1 through which the radio signal to be characterized propagates. For example, if the part of environment 1 under consideration includes many buildings and other physical obstacles, the signal attenuation value (expressed in decibels or dB) may be important with respect to less obstructed areas of the radio frequency environment 1. The value of the source parameter Φ may also be set by the computing device 4, for example, as an initialized value for the source parameter Φ. For example, the signal attenuation may be set to vary from 50 dB to 100 dB.

[0086] In the first embodiment of this disclosure, data relating to the environmental parameter Ψ can be expressed as a probability function. For example, such data relating to the source parameter Φ can be expressed as p(Ψ|Z n It can be expressed as -).

[0087] Here, -Ψ is a set of environmental parameters, -Z n - represents data relating to previous measurements of the radio signal collected by computing device 4.

[0088] In a second embodiment of this disclosure, data relating to the environmental parameter Ψ may be represented as a scalar value. In such a second embodiment, the data relating to the environmental parameter Ψ may be a list of discrete candidate values ​​for the environmental parameter Ψ. In particular, such discrete candidate values ​​may be listed based on previous values ​​of the environmental parameter Ψ (for example, as output by the estimation method in a previous iteration).

[0089] In step 330, previous data is determined and / or retrieved by computing device 4.

[0090] The previous data determined in step 330 is, for example, the current time t n Previous estimated positions s1, s2, ..., s of source entity 2 prior to n-1 This may include: The location s of source entity 2 is a priori unknown by the network infrastructure, and such location s is a potential parameter whose successive values ​​are estimated by the network infrastructure. Such a previously estimated location of source entity 2 may be estimated by receiving entity 3, computing device 4, or any other network entity, for example, based on collected measurements of the radio signal. Such a previously estimated location of source entity 2 may also be retrieved from a database, for example, based on a lookup table or configuration by computing device 4 when previous measurements of the radio signal are unavailable.

[0091] The previous data determined in step 330 is also the current time t n Previous time t1, t2..., t n-1 The associated radio signal Z n - May include previous measurements of the wireless signal Z. n -Such previous measurements are, for example, previous times t1, t2..., t n-1In this case, typically, the source entity 2 may have been received from the receiving entity 3 by the computing device 4 in a previous iteration of the proposed estimation method. Such previous estimated locations of the source entity 2 may also be retrieved from a database based on a lookup table or configuration by the computing device 4, for example, when previous measurements of the radio signal are not available.

[0092] In step 330, based on prior data available to the computing device 4, the computing device 4 may also determine estimates of the source parameter Φ and the environmental parameter Ψ based on such prior data. Such estimates of the source parameter Φ and the environmental parameter Ψ based on such prior data may be, for example, p(Ψ|Z n -) and p(Φ|Z n It may also be in the form of a probability function such as -).

[0093] In step 340, the updated values ​​for the source parameter Φ and the environment parameter Ψ are determined.

[0094] For that purpose, the current time t n The similarity value calculated in step 340 is, -Data regarding the current measurement of the radio signal collected in step 300, - Data on at least the source parameter Φ and the environmental parameter Ψ It is determined between these two points.

[0095] In the first embodiment of this disclosure, the similarity value is the wireless signal Z n The data relating to the current measurements can be calculated as a value to compare with available data obtained, collected, measured, determined, and / or estimated by computing device 4. Such available data may include, in particular, the following: -Data regarding source parameter Φ, -Data regarding environmental parameter Ψ, - Wireless signal Z within the radio frequency environment 1 n - Data regarding previous measurements thereof, such previous measurements being, for example, at the current time t n collected by the receiving entity 3 at a previous time of t - Data regarding previous estimations within the radio frequency environment 1 for potential parameters such as the position of the source entity 2 in particular. Such data is, for example, the previous estimated positions s1, s2,..., s of the source entity 2 associated with previous times t1, t2,..., t n-1 associated with previous times t1, t2,..., t n-1 may be included.

[0096] According to a first embodiment of the present disclosure, a similarity value can be represented as

Number

[0097] Here,[[]] - p(Z n |Z n -, Φ, Ψ) is the similarity value,[[]] - s 1:n = [s1, s2,..., s n is a set of consecutive positions of the source entity 2 within the environment 1, s n - = [s1, s2,..., s n-1 is the previous position of the source entity 2, s n is the unknown current position of the source entity 2,[[]] - Z n is data related to the current measurement collected in step 300,[[]] - Z n - is data regarding previous measurements of the wireless signal,[[]] - Φ is a source parameter,[[]] - Ψ is an environmental parameter,[[]] - p(Z n |Z n -, Φ, Ψ, s 1:n ) is the previous measurement Z n- is a likelihood value that reflects the similarity of the data to the current measurement for a set of continuous positions of source parameter Φ, environmental parameter Ψ, and source entity 2. -p(s 1:n |Z n -,Φ,Ψ) is the previous measurement Z n - This is the positional probability of source entity 2 based on source parameter Φ and environmental parameter Ψ.

[0098] The formula for the positional probability of source entity 2 is p(s) 1:n |Z n -Φ,Ψ) represents the data available to computing device 4 (generally, to the network infrastructure side), i.e., the wireless signal Z n - This can be interpreted as a prediction of the current position of source entity 2, estimated based on data regarding previous measurements of -, data regarding source parameter Φ, and data regarding environmental parameter Ψ.

[0099] Such a positional probability p(s) of source entity 2 1:n |Z n -,Φ,Ψ) in particular, p(s 1:n |Z n -,Φ,Ψ)=p(s n |s n -,Z n -,Φ,Ψ)×p(s n -|Z n -,Φ,Ψ) It can be expressed as follows.

[0100] Furthermore,

number

[0101] Here, -p(s n |s n -,Z n -,Φ,Ψ) is the probability of the current position of source entity 2 given the available data. -p(s n -|Zn -,Φ,Ψ) are included in the data regarding previous measurements of the wireless signal, -v, Δt n σ is a parameter of a predefined mobility model for the movement of the source entity, and that mobility model is, s n =s n-1 +v*Δt n +w, It is expressed as follows.

[0102] Here, -s n This is the current position of source entity 2, -s n-1 This is the previous position of source entity 2, -v is the velocity of source entity 2, -Δt n This is the current time t associated with the current position of source entity 2. n And the previous time t associated with the previous position of source entity 2. n-1 This is the time gap between and -w is modeling noise that follows a normal distribution with a null mean and a standard deviation of σ.

[0103] In this example, we consider Gaussian noise for the mobility model of source entity 2. Alternatively, other predefined mobility models for the movement of source entity 2 may be considered, in which case the equation p(s) n |s n -,Z n The proportional relationship between -,Φ,Ψ) may be different.

[0104] Such similarity value p(Z n |Z n Based on the formula -Φ,Ψ), step 340 includes a substep that marginalizes the similarity values ​​as follows:

number

[0105] and

number

[0106] Here, -Z n This is data regarding current measurements, -Z n - indicates data related to previous measurements, -Φ is a source parameter, -Ψ is an environmental parameter, -p(Z n |Z n -,Φ,Ψ) are similarity values, -p(Ψ|Z n -) and p(Φ|Z n -) is data included in previous estimates within radio frequency environment 1.

[0107] After such a marginalization step, the updated values ​​of the source parameter Φ and the environment parameter Ψ can be determined as follows:

number

[0108] and

number

[0109] In a second embodiment of the present disclosure, the updated values ​​of the source parameter Φ and the environmental parameter Ψ are determined by calculating a similarity value with respect to the Euclidean distance between the data relating to the current measurement of the radio signal collected in step 300 and the discrete candidate values ​​of the source parameter Φ and the environmental parameter Ψ.

[0110] More specifically, in a second embodiment of this disclosure, the similarity value may be calculated according to the Euclidean method as follows:

number

[0111] Here, -

number

[0112] Next, the updated values ​​of the source parameter Φ and the environmental parameter Ψ can be determined as candidate values ​​for the source parameter Φ and the environmental parameter Ψ that minimize such distance-related similarity values. In other words, the updated values ​​of the source parameter Φ and the environmental parameter Ψ can be determined.

number

[0113] In the optional step 350, the updated values ​​of the source parameter Φ and the environment parameter Ψ determined in step 340 may be used by the network infrastructure. In particular, such use may take advantage of the distinction between the source parameter and the environment parameter.

[0114] For example, after determining the updated values ​​of the source parameter Φ and the environment parameter Ψ, the computing device 4 may transmit some of these updated values ​​(in particular, along with other information) to various network entities of the network architecture supporting the wireless communication network deployed in the radio frequency environment 1 (e.g., via broadcast, groupcast, or unicast). Such updated values ​​may also be part of the support information provided to the network entities, particularly the core network, for example, to improve the management of radio resources or to optimize the network's radio architecture (e.g., beamforming, link adaptation, etc.).

[0115] In one example, such updated values ​​may be used to determine how long each characteristic collected and / or determined by a given receiving entity 3 can be stored and used by such receiving entity 3. For example, if the estimation method provides an updated speed value of an environmental parameter Ψ having a path loss index and a source parameter Φ of 10 meters per second, and assuming that receiving entity 3 covers a 2000-meter zone, such receiving entity 3 may retain the source parameter for 200 seconds. However, if the estimation method detects a source entity 2 with a nearly null speed, the source parameter Φ for such source entity 2 may be retained for a longer period of time. In other words, the updated values ​​of the source parameter Φ and environmental parameter Ψ may be used in step 350 to distinguish older data from relevant data for various network entities. Such information may be transmitted to various network entities by a computing device 4, for example, via a timer, Boolean information, or a list of selected parameters whose values ​​are retained or discarded.

[0116] To provide target information to various network entities, updated values ​​of source parameters Φ and environmental parameters Ψ can also be advantageously utilized. For example, updated values ​​of source parameter Φ can be shared with receiving entity 3, which manages the coverage zone on the estimated trajectory path of source entity 2 (and thus will receive radio signals from such source entity 2 in the future). Such updated values ​​of source parameter Φ allow the network infrastructure to characterize radio signals transmitted by such source entity 2 in each batch of received radio signals without having to process all measurements. In another example, updated values ​​of environmental parameters Ψ can be shared with receiving entity 3, which manages the portion of radio frequency environment 1 under consideration, and since environmental parameter Ψ is source-independent, it enables the characterization of dynamic radio signals within such portion even when such radio signals are generated by other source entities 2.

[0117] Therefore, the proposed method enables the entire network infrastructure to optimize radio knowledge within the radio frequency environment 1, while saving processing resources and time for characterizing radio signals, monitoring radio signal propagation, and optimizing data storage for network entities. [Explanation of Symbols]

[0118] 1. Radio frequency environment 2 Source Entities 3 Receiving Entity 4. Computing Devices Z1, Z2, Z3 coverage zones 20, 30, 40 Communication Interfaces 21, 31, 41 Processing Units 22, 32, 42 memory units

Claims

1. A method performed by a computing device (4) for characterizing dynamic radio signals in a radio frequency environment (1), wherein the radio frequency environment (1) is - At least one receiving entity (3) configured to collect the radio signal within the radio frequency environment (1), - At least one source entity (2) moving relative to the receiving entity (3), wherein the source entity (2) transmits the radio signal within the radio frequency environment (1). Includes, The method described above is Step (300) of receiving data relating to the current measurement of the radio signal collected by the at least one receiving entity (3), Step (310) of determining data relating to a source parameter (Φ), wherein the source parameter (Φ) depends on the source entity (2), Step (320) of determining data relating to environmental parameters (Ψ), wherein the environmental parameters depend on the radio frequency environment (1), --The aforementioned data relating to the current measurement values, --At least the data relating to the source parameter (Φ) and the environmental parameter (Ψ) Step (340) to determine the updated values ​​of the source parameter (Φ) and the updated values ​​of the environment parameter (Ψ) by calculating the similarity value determined between them. A step of characterizing the radio signal in the radio frequency environment (1) based on the updated value of the source parameter (Φ) and the updated value of the environmental parameter (Ψ). Methods that include...

2. The data relating to the source parameter (Φ) and the data relating to the environmental parameter (Ψ) are, - Previous estimates of the source parameter (Φ), - Previous estimates of the aforementioned environmental parameter (Ψ), - Values ​​stored in the lookup table, - A value pre-set by the network entity of the aforementioned radio frequency environment (1), - A list of candidate values ​​(P) for the aforementioned source parameter (Φ), - List of candidate values ​​(Q) for the aforementioned environmental parameter (Ψ) The method according to claim 1, determined based on at least one of the following elements.

3. The method according to any one of claims 1 and 2, wherein the similarity value further depends on previous data relating to previous measurements of the radio signal in the radio frequency environment (1).

4. The aforementioned previous data, - The radio signal (Z) in the radio frequency environment (1) ｎ -) Previous measurement values, - Previous estimated position (s) of the source entity (2) in the radio frequency environment (1) １ , . . , s ｎ－１ ), - Data derived from the previous value of the source parameter (Φ), - Data derived from previous values ​​of the aforementioned environmental parameter (Ψ) The method according to claim 3, comprising at least one of the elements.

5. The aforementioned source parameter (Φ) is - The speed of the source entity (2) and - The transmission power of the source entity (2) and - The antenna gain of the source entity (2) and The method according to any one of claims 1 to 4, comprising at least one of the elements.

6. The aforementioned environmental parameters are - Spatial redistribution of radio frequency signal attenuation within the aforementioned radio frequency environment (1), - The coefficients of the modeling function for signal attenuation in the aforementioned radio frequency environment (1), - The coefficients of the path loss function in the aforementioned radio frequency environment (1), - Shadowing parameters in the aforementioned radio frequency environment (1) and The method according to any one of claims 1 to 5, comprising at least one of the elements of:

7. The data relating to the source parameter (Φ), the data relating to the environmental parameter (Ψ), the updated value of the source parameter (Φ), and the updated value of the environmental parameter (Ψ) are, - Scalar values ​​and, - Probability function and The method according to any one of claims 1 to 6, which is an element of the method.

8. The aforementioned similarity value is - Likelihood determined by a Bayesian algorithm, - Distance determined by a Euclidean-based algorithm and A method according to any one of claims 1 to 7, derived from at least one of the elements of the following:

9. The method according to any one of claims 1 to 8, wherein the similarity value is derived from a likelihood determined by a Bayes-based algorithm, the updated value of the source parameter (Φ) is determined by marginalizing the similarity value to data derived from a previous value of the environment parameter (Ψ), and the updated value of the environment parameter (Ψ) is determined by marginalizing the similarity value to data derived from a previous value of the source parameter (Φ).

10. The marginalization of the similarity value to the data derived from the previous value of the environmental parameter (Ψ), and to the data derived from the previous value of the source parameter (Φ), respectively, [Math 1] and [Math 2] It is expressed as, Here, -Z ｎ However, the above data pertains to the current measurement value, -Z ｎ - is the data relating to the previous measurement values, -Φ is the source parameter, -Ψ is the aforementioned environmental parameter, -p(Z ｎ | Z ｎ -,Φ,Ψ) are the similarity values, -p(Ψ|Z) ｎ -) is the data derived from the previous value of the environmental parameter (Ψ), -p(Φ|Z ｎ -), which is the data derived from a previous value of the source parameter (Φ), The method according to claim 9.

11. The updated value of the source parameter (Φ) and the updated value of the environmental parameter (Ψ) are, respectively, p(Z ｎ | Z ｎ -,Φ) × p(Φ|Z ｎ -), and p(Z ｎ | Z ｎ -,Ψ) × p(Ψ|Z ｎ -) is proportional to, Here, -p(Z ｎ | Z ｎ -Φ) is the similarity value that is marginalized to the data derived from the previous value of the environmental parameter (Ψ), -p(Z ｎ | Z ｎ -,Ψ) is the similarity value that is marginalized to the data derived from the previous value of the source parameter (Φ), -p(Φ|Z ｎ -) is the data derived from the previous value of the source parameter (Φ), -p(Ψ|Z) ｎ The method according to claim 10, wherein -) is the data derived from a previous value of the environmental parameter (Ψ).

12. The aforementioned similarity value is [Math 3] It is expressed as, Here, -p(Z ｎ | Z ｎ -,Φ,Ψ) are the similarity values, -s １：ｎ =[s １ ,s ２ , . . , s ｎ ] is a set of consecutive positions of the source entity (2) in the environment, s ｎ -=[s １ ,s ２ , . . , s ｎ－１ ] is the previous position of the source entity (2), s ｎ However, this is the unknown current position of the source entity (2), -Z ｎ However, the above data pertains to the current measurement value, -Z ｎ - is included in the data relating to previous measurements of the wireless signal, -Φ is the source parameter, -Ψ is the aforementioned environmental parameter, -p(Z ｎ | Z ｎ -,Φ,Ψ,s １：ｎ ) is data relating to the similarity value given the set of consecutive positions of the source entity (2), -p(s １：ｎ | Z ｎ The method according to any one of claims 1 to 11, wherein -, Φ, Ψ) is the position probability of the source entity (2).

13. The positional probability of the source entity (2) is, p (s) １：ｎ |Z ｎ -,Φ,Ψ)=p(s ｎ |s ｎ -,Z ｎ -,Φ,Ψ)×π(s ｎ -|Z ｎ (-, Φ, Ψ) It is expressed as, [Math 4] And, Here, -p(s ｎ | s ｎ -, Z ｎ -,Φ,Ψ) is the probability of the current position of the source entity (2) given the data relating to previous measurements of the radio signal, -p(s ｎ - | Z ｎ -,Φ,Ψ) are included in the data relating to previous measurements of the wireless signal, -v, Δt ｎ σ is a parameter of a predefined mobility model of the movement of the source entity (2), and the mobility model is, s ｎ =s ｎ－１ +v*Δt ｎ +w, It is expressed as, Here, -s ｎ However, this is the current position of the source entity (2), -s ｎ－１ However, this is the previous position of the source entity (2), -v is the velocity of the source entity (2), -Δt ｎ However, the current time t associated with the current position of the source entity (2) ｎ and the previous time t associated with the previous position of the source entity (2). ｎ－１ This is the time gap between and The method according to claim 12, wherein -w is modeling noise that follows a normal distribution having a null mean and a standard deviation of σ.

14. The updated values ​​of the source parameter (Φ) and the environmental parameter (Ψ) are determined as follows: [Math 5] Here, the similarity value is expressed as follows: [Math 6] Here, － [Number 7] However, the aforementioned similarity value, -P is a set of candidate values ​​for the source parameter (Φ), -Q is a set of candidate values ​​for the environmental parameter (Ψ), -S is a set of candidate positions of the source entity (2), -Φ is the source parameter, -Ψ is the aforementioned environmental parameter, -s is a candidate position value of the source entity (2) among the set of positions S, -K is the number of receiving entities (3) in the radio frequency environment (1), -Z ｋ，ｎ However, the data relating to the current measurement of the radio signal collected by the k-th receiving entity (3) out of the K receiving entities (3) is, -L ｋ However, this is the current position of the k-th receiving entity (3) out of the K receiving entities (3), -P ｔ However, this is the transmission power of the source entity (2) when transmitting the wireless signal, - A and B are path loss parameters of a predefined propagation loss model of the radio signal in the radio frequency environment (1), and the propagation loss model is [Number 8] It is expressed as, The method according to any one of claims 1 to 8, wherein PL is the propagation loss value of the transmitted power of the radio signal transmitted by the source entity (2).

15. A computer device (4) configured to characterize dynamic radio signals in a radio frequency environment (1), wherein the computer device (4) comprises a connection interface (40) to a receiving entity (3) in the radio frequency environment, and processing circuits (41, 42) for implementing the method according to any one of claims 1 to 14.

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