A system for dynamic prediction of path losses in mobile communication networks

Abstract

A system for dynamic prediction of path losses in mobile communication networks, comprising: a transmitter (Tx) configured to transmit electromagnetic signals within a defined frequency range; a receiver (Rx) configured to measure the received signal strength; a dynamic path loss model including a variable path loss exponent (Nv), where Nv is a time and material density dependent variable that adjusts the predicted path loss based on real-time environmental changes; a computing module configured to: i. Real-time monitoring and analysis of environmental data, including material density and movement within the signal propagation path; ii. Update Nv based on observed variations in environmental conditions. iii. Recalculate path loss using the updated dynamic model to optimize signal coverage and network performance.

Description

FIELD OF THE INVENTIONThe present disclosure relates to a system for dynamically predicting path losses in mobile radio networks. More particularly, the present invention relates to a system having a path loss model for predicting signal strength at a receiver (Rx) from a transmitter (Tx) based on environmental factors. The present invention is particularly complex for the "Future Mobile Networks (FMCNs)" which are expected to become more intelligent and proactive thanks to new functions, resulting in more agility and performance. FMCNs are faced with dense and frequent traffic from people and devices in closed and open environments. One of the most frequently occurring events is the mass storm and the accumulation of people sets in "affected areas (AiQ)" which cover the capacity requirement, i.e. the "location-time capacity".BACKGROUND OF THE INVENTIONCellular networks use path loss models to predict signal propagation and optimize network performance. Traditional models such as the Friis, Okumura-Hata, and COST-231 Hata models are based on static environmental conditions that take into account morphological characteristics such as vegetation, water, concrete, etc. Therefore, these propagation models do not take account of the environmental dynamics. The path loss models predict the signal level at the receiving location depending on the distance between transmitter and receiver, the radiation power, the frequency of the transmission signal and the condition of the environment. Environments.However, in real environments, dynamic changes such as variations in human quantity or moving obstacles often occur. These static models cannot take these into account. This results in suboptimal network performance, especially in high density or rapid changes environments.Current models originate from static environmental conditions, which can lead to inefficiencies in dynamic changes such as sudden man accumulations or environmental changes that affect signal propagation.This invention addresses the constraints of static path loss models used in mobile radio networks. The dynamic path loss models predict the signal strength of a transmitter (Tx) at a receiver (Rx) based on dynamic environmental factors.SUMMARY OF THE INVENTIONThe present disclosure relates to a system for dynamically predicting radio link losses in mobile radio networks. The invention includes a dynamic radio path loss model that uses a time and material dependent variable radio path loss exponent (Nv) to account for real time changes in environmental conditions. By continuously monitoring and adapting to dynamic factors such as material density, obstacle movements and environmental changes, the model provides more accurate radio path loss predictions. This allows smart networks to proactively optimize signal coverage and performance in dynamic and dense environments.The present disclosure aims to provide a system for dynamically predicting path losses in mobile radio networks. The system comprises: a transmitter (Tx) that transmits electromagnetic signals within a defined frequency range; a receiver (Rx) that measures received signal strength; a dynamic path attenuation model having a variable path attenuation exponent (Nv), where Nv is a time and material density dependent variable that adjusts predicted path attenuation based on real-time environmental changes; a computing module configured to: monitor and analyze real-time environmental data including material density and motion within the propagation path of the signal; update Nv based on observed variations in environmental conditions; and recompute path attenuation using the updated dynamic model to optimize signal coverage and network performance.It is an object of the present disclosure to provide a system for dynamically predicting path losses in mobile radio networks. First, there is a free space between T x and R x. The medium therebetween, however, gradually solidifies in small material steps, which material may be of any kind, for example metal, glass, concrete or even humans.Another object of the present disclosure is to develop a dynamic path loss model that adapts to real-time environmental changes, thus improving signal strength prediction in cellular networks.Another object of the present disclosure is to improve network performance in high density or rapidly changing environments by including a time and material dependent variable path loss exponent (Nv).Another object of the present disclosure is to enable intelligent and proactive network optimization for better coverage and capacity in different propagation environments.In order to further clarify the advantages and features of the present disclosure, the invention will be described in more detail with reference to specific embodiments illustrated in the accompanying drawings. These drawings illustrate only typical embodiments of the invention and are therefore not to be considered as limiting the scope thereof. The invention will be described and explained in more detail with reference to the accompanying drawings.BRIEF DESCRIPTION OF THE FIGURESThese and other features, aspects, and advantages of the present disclosure will become more fully understood when the following detailed description is read with reference to the accompanying drawings, in which like characters represent like parts throughout. The following applies here: FIG. 1 shows a block diagram of a system for dynamically predicting path losses in mobile communication networks according to an embodiment of the present disclosure. FIG. 2 is a diagram illustrating the investigation of the effects of the presence of humans in a network area according to an embodiment of the present disclosure.Those skilled in the art will also appreciate that the elements in the drawings are shown for simplicity and are not necessarily to scale. For example, the flowcharts illustrate the method using the key steps to improve understanding of aspects of the present disclosure. In addition, regarding the construction of the apparatus, individual or multiple components of the apparatus may be represented by conventional symbols in the drawings. The drawings may only show the specific details relevant to understanding the embodiments of the present disclosure in order not to obscure the drawings with details readily apparent to those skilled in the art after the present description.DETAILED DESCRIPTION:In order to aid in the understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and will be described in an comprehensible manner. However, the scope of the invention is not limited thereby. Changes and further modifications of the illustrated system, as well as further applications of the principles of the invention, are possible, as would normally occur to a person skilled in the art.It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be limiting thereof.References throughout this specification to "one aspect," "another aspect," or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the phrases "in one embodiment," "in another embodiment," and similar phrases in this specification may or may not refer to the same embodiment.The terms "comprise", "comprising", or variations thereof cover a nonexclusive inclusion. A process or method comprising a list of steps includes not only those steps, but also other steps not expressly listed or inherent to the process or method. Likewise, the phrase "comprises... for" one or more devices, subsystems, elements, structures, or components does not exclude, without further limitations, the existence of further devices, subsystems, elements, structures, or components, or additional devices, subsystems, elements, structures, or components.Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by one of ordinary skill in the art. The systems, methods, and examples provided herein are for illustrative purposes only and are not to be considered limiting.Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.FIG. 1 shows a block diagram of a system ( 100) for dynamically predicting link losses in mobile communication networks according to an embodiment of the present disclosure.Referring to FIG. 1, the system (100) includes a transmitter (Tx) (102) configured to emit electromagnetic signals within a defined frequency range; a receiver (Rx) (104) configured to measure received signal strength; a dynamic path loss model (106) having a variable path loss exponent (Nv), where Nv is a time and material density dependent variable that adjusts predicted path loss based on real-time environmental changes; a computing module (108) configured to: monitor and analyze real-time environmental data including material density and motion within the propagation path of the signal; update Nv based on observed variations in the environmental conditions; and recompute the path loss using the updated dynamic model to optimize signal coverage and network performance.In one embodiment, the variant path loss exponent (Nv) is determined based on the following factors: type and concentration of materials in the signal propagation path, rate of change of material density over time, and speed and volume density of moving obstacles within the region of interest.In one embodiment, the system (100) further comprises: a data acquisition module (110) configured to record and process environmental measurements including human quantity density, obstacle composition, and motion patterns; and a machine learning processor (112) trained to predict future environmental changes and dynamically adjust Nv for proactive network optimization.In one embodiment, the dynamic path loss model is applied to: optimize base station placement and deployment strategies (BS) in high density environments; and improve smart network performance by adapting to real-time environmental dynamics.In one embodiment, the transmitter (Tx) and the receiver (Rx) are configured to operate within a predefined frequency range that is not affected by existing cellular services to ensure accurate detection of environmental data.In one embodiment, the computing module (108) provides a graphical representation of signal strength variations over time and real-time warnings with substantial variations in path loss to enable immediate network adjustments.In one embodiment, the variability factor (Nv) may be used to train the smart networks to properly behave in the different propagation environments.Conventional propagation models are basically static and primarily take into account the morphological properties of the respective target area (AoI), such as vegetation, bodies of water and concrete structures. These models do not account for environmental dynamics and are based on empirical fine tuning of baseline path attenuation models through comprehensive field measurements. Theoretically, a static user at a fixed distance from a base station should experience a constant signal strength throughout its presence at that location. However, in practice, considerable fluctuations in signal strength are observed even if transmitter (Tx) and receiver (Rx) remain static within the target area. While conventional radio environments studied at a macroscopic level may consider such time-dependent fluctuations to be insignificant, future networks are expected to be very sensitive to environmental dynamics. These networks will likely contain intelligent, self-configuring functions for parameters and locations, whereby static propagation models are not sufficient to account for variations in the network environment. As a result, such models may not provide enough information to allow smart networks to effectively respond to dynamic environmental changes.The present invention relates to a system with a dynamic path loss model that includes a "variant path loss exponent" (Nv) that adjusts the path loss based on time dependent changes in material density and environmental conditions, where Nv takes into account the type, density, and motion of obstructions in the signal path.Experiments with controlled human density variations demonstrated that signal loss correlated with the density and type of obstacles. The derived model is consistent with these empirical results.The proposed system is suitable for intelligent and adaptive networks, particularly in variable density environments (e.g., concerts, urban areas), and contributes to optimizing network performance by dynamically adjusting coverage and capacity.Theoretically, a user in a communication network should not detect any variations in path loss as long as transmitter and receiver remain static. In reality, however, considerable fluctuations in the signal levels occur due to the medium between transmitter and receiver. This medium is gradually compacted in small steps, which may be made of materials such as metal, glass, concrete or even humans. As this incremental material δm belonging to the region of interest (Ao I), the path loss exponent must increase slightly from its static value. Consequently, the static path attenuation also changes and develops into a time-dependent function.The variable path loss exponent (Nv) is introduced as a material and time dependent variable that takes into account these dynamic changes in the network environment. At each time t, Nvquantifies the additional path loss caused by the dynamic material present. As the material density increases at constant speed and the material properties remain the same over time and space, the received signal strength decreases linearly in the logarithmic domain. If constant velocity and material properties do not vary over time and space, B(m) ρ m remains constant.FIG. 2 is a diagram illustrating the investigation of the effects of the presence of humans in a network area according to an embodiment of the present disclosure.In order to verify the proposed system, an experiment is performed (see FIG. 2 ) in which an area of 200×100 meters on a large open area is selected. Points P1 and P2 were chosen for the placement of Tx and Rx, respectively.The present invention is concerned with the variability of the path loss caused by non-static elements, so-called materials, entering the path between transmitter (Tx) and receiver (Rx). As shown in FIG. 2, measurements were first made in a free space environment. Subsequently, persons were introduced stepwise in a half of the area in steps of 10, 50, 100, 200, etc. up to 1000 persons. The signal measurements at the receiver site were recorded at each step for 20 minutes. A 1400 MHz frequency band was used to avoid interference from other mobile and point-to-point services. All power values were measured in dB, frequencies in MHz, and distances were recorded in meters.The significant and constant drops in signal strength observed throughout the measurement period confirm that these changes are due to the accumulation of people in the signal path rather than to momentary disturbances.To ensure uniform distribution, the participants moved within the defined range during the measurements. From 1200 signal values recorded per group were recorded in the computer system. Second randomly select data points to analyze signal levels relative to human density. The resulting data showed a clear regression relationship, with the signal level following the equation y=78x-29. Similar trends were observed in other data sets at different times, confirming consistency of the results. Deviations from the regression line have been attributed to factors such as Fresnel radius, multipath losses and ground reflections, which for simplicity have been omitted from the basic path loss model. The human density was not high enough to make incremental material changes (δm) infinitially small, thereby minimizing granularity effects.The invention relates to a system incorporating this time dependent variation into the basic path loss model. For this purpose, a variable path loss exponent (Nv) is introduced, which takes account of current changes in the environment. This model was empirically validated by signal measurements under dynamic conditions and showed its ability to account for time dependent variations in propagation environments.The drawings and the foregoing description show examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be divided into multiple functional elements. Elements of one embodiment may be added to another embodiment. For example, the order of the processes described herein may be changed and is not limited to the manner described herein. Moreover, the actions of a flow chart need not be performed in the order shown; nor do all actions necessarily need to be performed. Also, actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is by no means limited by these specific examples. Numerous variations, whether or not explicitly stated in the specification, such as differences in structure, dimensions, and material use, are possible. The scope of the embodiments is at least as broad as recited in the following claims.Advantages, other advantages and solutions to problems have been described above with reference to specific embodiments. However, the advantages, merits, solutions to problems and any components that may result in an advantage, benefit or solution being introduced or enhanced are not to be understood as critical, required or essential features or components of individual or all claims.REFERENCES100 A System for Dynamic Prediction of Path Losses in Mobile Communication Networks. 102 transmitter (Tx) 104 receiver (Rx) 106 dynamic path loss model 108 computing module 110 data acquisition module 112 processor for machine learning 202 open area 204 resident area

Claims

A system for dynamically predicting path losses in mobile communication networks, comprising: a transmitter (Tx) configured to emit electromagnetic signals within a defined frequency range; a receiver (Rx) configured to measure the received signal strength; a dynamic path loss model including a variable path loss exponent (Nv), wherein Nv is a time and material density dependent variable that adjusts the predicted path loss based on real-time environmental changes; a computing module configured to: i. monitor and analyze environmental data in real-time including material density and motion within the propagation path of the signal; Update Nv based on observed environmental variations. iii. recompute path loss using the updated dynamic model to optimize signal coverage and network performance.The system of claim 1, wherein the variable path loss exponent (Nv) is determined based on the following indications: a. type and concentration of materials in the signal propagation path; b. the rate of change of material density over time; c. the velocity and volume density of moving obstacles within the region of interest.The system of claim 1, further comprising: a data acquisition module configured to record and process environmental measurements including human set density, obstacle composition, and motion patterns; a machine learning processor trained to predict future environmental changes and dynamically adjust Nv to enable proactive network optimization.The system of claim 1, wherein the dynamic path loss model is applied to: optimizing base station (BS) placement and deployment strategies in high density environments; and improving smart network performance by adapting to the real-time dynamics of the environment.The system of claim 1, wherein the transmitter (Tx) and the receiver (Rx) are configured to operate within a predefined frequency range that is not affected by existing mobile services to ensure accurate detection of environmental data.The system of claim 1, wherein the computing module provides: a graphical representation of signal strength variations over time; and a real-time alert in response to significant variations in path loss to enable immediate network adjustments.The system of claim 1, wherein the variability factor (Nv) can be used to train the smart networks to properly behave in the different propagation environments.

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