AI-Powered Dynamic Quality of Service Resource Allocation System and Method for UAV Traffic Management in 6G Networks

Abstract

The invention relates to a dynamic network optimization and resource allocation system and method that uses the flight plan and instantaneous position, speed and direction data of an unmanned aerial vehicle (10) using 6G telecommunication infrastructure, processes it in a dynamic QoD prediction and allocation engine (60) via a UTM server (50) to predict the target 6G base station (20B), queries the current network load of the target cell and calculates the required bandwidth, autonomously triggers a CAMARA QoD API call via an API server (40) before the transition between cells, reserves a high-priority network slice or QoS stream in the 6G core network (30), and cancels the QoD profile in the current 6G base station (20A) after the transition is completed, returning the resources to standard subscribers.

Description

1 TARIFF AI-Powered Dynamic Service Quality for UAV Traffic Management on 6G Networks Resource Allocation System and Method TECHNICAL FIELD The invention enables beyond-line-of-sight operations of unmanned aerial vehicles using 6G telecommunications infrastructure. In Beyond Visual Line of Sight (BVLOS) flights, network resources are dynamically utilized. It relates to a network optimization and resource allocation system and method for managing it. 10 The invention is particularly useful for unmanned aerial vehicle traffic management (UTM) systems with cellular networks. In its integration, the next 6G base station is determined using real-time telemetry data and flight route. station prediction and CAMARA with Open Gateway Proactive 15-bit bandwidth and low-latency sourcing for the target cell via APIs. It is related to its allocation. PREVIOUS TECHNIQUE With current technology, unmanned aerial vehicles can be connected to the network via 20 cellular networks using 4G and standard 5G technology. It is treated like a normal mobile subscriber (User Equipment, UE) and receives service throughout the flight. It involves switching between base stations that transmit signals. Such a cell-to-cell handover occurs. during this process, insufficient radio source and bandwidth in the new cell, especially direct continuity of telemetry data through high-resolution video transmission It affects. When resource scarcity occurs, video streaming freezes, telemetry 25 Data loss and connection interruptions may occur during beyond-line-of-sight flights. Since communication needs to continue even in the long leg of the flight, to the target cell After the incident, realizing the lack of resources necessitated maintaining communication continuity. This constitutes a delayed response in this regard. Therefore, it only relates to the existing cell connection. The classic mobile subscriber approach, based on the mobile unmanned aerial vehicle, will be the next 30 years. The communication source he will need in the cell should be made ready before the flight flow is disrupted. He is unable to bring it. The previous technique involved network slicing or services for unmanned aerial vehicles. Quality of service (QoS) profiles are usually defined statically before flight commencement. 35 Because conditions do not remain constant along the flight route, a predetermined profile can change instantaneously. changing air traffic, base station density and signal interference all at the same time. 2 It is unable to meet the demand, especially for load distribution between the cells the vehicle moves through. As it changes, the source level determined before the flight becomes the actual band in the next cell. The possibility arises that the width and the need for delay do not match. The flight of the static profile. Applying the same logic throughout, where high-priority resources are truly needed. mismatch in time and location between the actual network density and the data. 5 It can create a service that remains stable despite varying cellular network conditions. The quality approach dynamically addresses the communication requirements at the moment of transition to the target cell. It cannot meet the demand. CAMARA Quality on Demand (QoD) type API interfaces network 10 Although it allows the source to be adjusted according to demand, in previous practices... These calls are usually made manually or reactively after the need arises. is triggered. The QoD request is triggered after the unmanned aerial vehicle enters a dense cell. If initiated, the API call will configure the network's relevant policy and resource settings. The time elapsed between completions becomes critical. The target is 15 within this timeframe. Packet losses can occur because the necessary capacity is not yet available in the cell. Packet loss in telemetry and resolution video transmission affects not only data quality. It not only causes disruption, but also poses a flight safety risk in terms of unmanned aerial vehicle traffic management. This also gives rise to the following: Therefore, having the current API capability alone is not sufficient, This ability allows for the right cell to receive the information at the right time, 20 days before intercellular transfer occurs. It needs to be put into operation. The aforementioned shortcomings affect the next connection of the unmanned aerial vehicle while in motion. The resource requirement at the point can only be determined by fixed profiles defined before flight or by the target This shows that it cannot be managed by reactive requests initiated after entering the cell. 25 The bandwidth and low latency required the moment the inter-cell transition occurs. Lack of readiness manifests as video freezing, telemetry loss, connection interruption, and packet loss. This leads to the resulting technical consequences. In contrast, the entire flight path Continuously allocating high priority resources also means prioritizing network resources over other subscribers. This limits its efficient use. In this context, in the relevant technical field, the changing route and cell 30 monitoring conditions during flight, the necessary resource only in the target cell being approached. It can prepare the cells before the intercellular transition and is no longer needed after the transition is complete. a dynamic network management approach that can free up high-priority resources that are not currently available There is a need. 35 As a result of research conducted in the literature, the code numbered US20200154426A1, Optimization of radio resource allocation based on unmanned aerial vehicle flight path information 3 Patent titled "Optimization of Radio Source Allocation Based on Vehicle Flight Path Information" A document was found. This document concerns the flight path of the unmanned aerial vehicle. According to the information, network resources can be identified and resource reservations can be made in multiple cells. It relates to the dynamic use of flight plan information and network resources in the document. The resource to be associated with and used by the unmanned aerial vehicle in multiple cells is pre-determined as 5 It is stated that they can be separated. However, instantaneous GPS position and speed and vector data are obtained using UTM. a dynamic QoD estimation and allocation engine working in conjunction with the route of the target cell's current Calculating the necessary bandwidth by monitoring network load, Open Gateway autonomously triggering the CAMARA QoD API call before inter-cell migration and A 10 related to the integrated structure that cancels the QoD profile in the old cell after the transition. No evidence was found. As a result of research conducted in the literature, Protocol design for EP3698343A1 was identified. Unmanned Aerial System (UAS) Traffic Management (UTM) A patent document titled "Protocol Design for Traffic Management" was found. The aforementioned 15 The document covers mission route, data rate, and duration within the scope of unmanned aerial vehicle traffic management. with network resource requests based on information and radio resource reservations based on mobility It is related. The document mentions the source at the access node covering the next path segment. reservations can be made in advance and the next access node can be accessed according to mobility. It is stated that the transition was predictable. However, the instantaneous GPS position, speed, and vector were artificially determined. Targeting 6G base by processing in an intelligent-powered dynamic QoD prediction and allocation engine. Checking the current network load at the station, calculating the required bandwidth, Autonomous triggering of the CAMARA QoD API call via the Open Gateway. and a closed loop in which the old cell profile is canceled after intercellular transition. No evidence related to this has been found. 25 Ultimately, the problems mentioned above, which cannot be solved with current technology, are the subject of this technical analysis. This has made it necessary to make an innovation in the field. BRIEF DESCRIPTION OF THE INVENTION 30 The invention allows a moving unmanned aerial vehicle to source data at the next 6G base station. Identifying the need before intercellular transfer occurs and providing the necessary quality of service. The network uses route and telemetry data to prepare the source in advance at the target cell. It associates resource management with a closed-loop system. 35 4 The main purpose of the invention is to enable the unmanned aerial vehicle to target a 6G base station during flight. By anticipating the transition between cells, the necessary network resources are made available at the moment of the switch. The goal is to ensure that it happens. Another objective of the invention is to determine the required bandwidth by querying the existing network load in the target cell. To determine the width and make the CAMARA QoD API call between cells via the API server. It is to trigger autonomously before the transition. Another aim of the invention is to deliver a high-priority source only to the cells where it is needed. to use and cancel the QoD profile in the old cell after the transition is complete 10 The goal is to return resources to standard subscribers. Beyond line of sight for unmanned aerial vehicles using 6G telecommunications infrastructure. seamless communication, high-resolution video transmission, and ultra-low latency during flights. It is a dynamic network optimization and management system that enables telemetry transmission, and its feature is; 15  Unmanned aerial vehicles that generate telemetry data and transmit high-quality video. aircraft,  Existing 6G base stations that provide wireless radio access to unmanned aerial vehicles. station,  The cell through which the unmanned aerial vehicle will pass in the direction of its flight is 20 The target 6G base station provides wireless radio access.  6G core network that routes data traffic and hosts UPF and SMF network functions,  API server that opens the capabilities of the telecommunications network to external systems,  UTM server that manages the flight path, speed, and permissions of the unmanned aerial vehicle,  Flight plan received from the UTM server and real-time GPS location data and unmanned 25 Using the aircraft's speed and vector, the target 6G base station can be pre-selected. predicting by querying the current network load on the target 6G base station calculates the required bandwidth and the unmanned aerial vehicle targets the cell. Before entering, autonomously make the CAMARA QoD API call through the API server. The dynamic QoD estimation and allocation engine that triggers 30 It includes. The structure of the invention and the best understanding of its advantages, including additional components. This should be evaluated together with the figures explained below. 35 BRIEF DESCRIPTION OF THE FIGURES Figure 1 illustrates the interaction between the unmanned aerial vehicle, network units, and allocation engine in the system described in the invention. It is a schematic block diagram that shows the relationship between data and control. Figure 2 shows the general flow of the dynamic resource allocation and inter-cell switching process that is the subject of the invention. This is a flowchart showing the flowchart. 5 Figure 3 shows the sequential steps of the method: data sequential, estimation, allocation, transition, and resource release. It is a flowchart that shows the release relationship. The drawings do not necessarily need to be scaled and are necessary for understanding the invention. Details that are not present may have been overlooked. Furthermore, at least significantly 10 Identical elements or elements with similar functions are numbered the same way. It is shown. REFERENCE NUMBERS 10. Unmanned aerial vehicle 20A. Existing 6G base station. 20B. Target 6G base station. 30. 6G core network 40. API server 20 50th UTM server 60. Dynamic QoD estimation and allocation engine. Flight plan and real-time GPS location of the unmanned aerial vehicle from the 1001st UTM server. Transferring the data to the dynamic QoD estimation and allocation engine 25 1002. Velocity and vector of the unmanned aerial vehicle by the dynamic QoD estimation and allocation engine. Predicting the target 6G base station to be transitioned to by analyzing the data. 1003. Target 6G base estimated by the dynamic QoD estimation and allocation engine. Querying the current network load at the station and using the query result to develop unmanned aerial vehicles. Calculating the required bandwidth for the vehicle 30 1004. Dynamic QoD estimation and allocation engine to unmanned aerial vehicle target cell. just before entering and before switching between cells, via the API server Making a call to the CAMARA QoD API. 1005. Policy control functions on the 6G core network by the API server. updating and as a result of this update, certain unmanned aerial vehicles are targeting the 6G base station. reserving a high-priority network slice or QoS stream for the agent 6 1006. The unmanned aerial vehicle passes to the target 6G base station and is pre-reserved. Continuing telemetry and video transmission with zero interruption via the established source 1007. The drone was kept at the existing 6G base station after completing its pass. Canceling the QoD profile via API and returning resources to standard subscribers. DETAILED DESCRIPTION OF THE INVENTION This detailed explanation describes the subject of the invention: unmanned aerial vehicle traffic management in 6G networks. AI-powered dynamic quality of service resource allocation system and method for (UTM) 10 that will not create any limiting effect on a better understanding of the subject. This is explained with examples. The system is an unmanned aerial vehicle that generates telemetry data and transmits high-quality video. vehicle (10) provides wireless radio access to the unmanned aerial vehicle (10) in the existing cell. The existing 6G base station (20A) will connect to the cell via wireless radio 15 after the transition. The target 6G base station (20B) that provides access, routes data traffic and uses UPF and SMF, etc. 6G core network (30) containing network functions, telecommunications network capabilities to external systems The API server (40) opens the flight route, speed and permissions of the unmanned aerial vehicle (10). UTM server (50) that manages and predicts the target cell by processing route and telemetry data, Querying the network load, calculating the required bandwidth, and proactively making the QoD call. elements of the AI-powered dynamic QoD prediction and allocation engine (60) that triggers It consists of an API server (40), an Open Gateway interface in a configuration. It can be used as and can forward CAMARA API calls to the 6G core network (30). During flight, the unmanned aerial vehicle (10) provides high-speed and instantaneous position, speed and direction information. It generates high-resolution video and telemetry traffic. UTM server (50), unmanned aerial 25 Dynamic QoD estimation and allocation of flight plan and instantaneous GPS position data of the vehicle (10). It transfers to the engine (60). Dynamic QoD estimation and allocation engine (60), UTM server (50) as an intermediary layer on MEC or cloud between the telecom operator network and (50) It is working and constantly monitoring the flight path. This structure enables the unmanned aerial vehicle (10) to move. while flight plans and real-time telemetry inputs are evaluated in the same decision flow. 30 It provides coverage of the existing 6G base station (20A) with the unmanned aerial vehicle (10). When approaching its limit, the dynamic QoD estimation and allocation engine (60) of the unmanned aerial vehicle (10) By analyzing its speed and vector, the target 6G base station will enter the coverage area in the flight direction. It predicts the location of the station (20B). The target 6G base station (20B) is the flight. It is calculated within milliseconds based on the vector. Following the prediction, the dynamic 35 QoD estimation and allocation engine (60) estimates the available network load at the target 6G base station (20B). to interrogate and according to the communication needs of the unmanned aerial vehicle (10) with the cargo being interrogated. 7 It calculates the required bandwidth. Dynamic QoD estimation and allocation engine (60), unmanned aircraft (10) without physically entering the target cell and passing between cells CAMARA QoD API call through API server (40) just before it happens It triggers autonomously. The API server (40) externalizes the capabilities of the telecommunications network. By opening it to systems, it forwards this call to the 6G core network (30). The 6G core network (30), policy 5 By updating the control functions, specific unmanned aerial vehicles can be targeted at the 6G base station (20B). (10) Reserve a high priority network slice or QoS flow for and inter-cell switching It creates a low-latency profile with guaranteed bandwidth before it even happens. QoD call transition even if network density is high at the target 6G base station (20B) By doing this beforehand, the necessary resources are made available before the transition. 10 When the unmanned aerial vehicle (10) switches to the target 6G base station (20B), it is pre-reserved. It maintains telemetry and video transmission over the verified source with zero interruption. Freezing during broadcast due to the source disconnecting before the connection is reached. preventing the occurrence of zero-packet-loss inter-cell transitions. The aim is to achieve the following: When the transition is complete, the dynamic QoD estimation and allocation engine (60), 15 It cancels the QoD profile held at the current 6G base station (20A) via API. Thus, by downgrading the resource level, the high-priority resource becomes a standalone flight path. on which the unmanned aerial vehicle (10) is currently located or approaching by separating regions, resources that are no longer needed are returned to standard subscribers Efficient use of telecommunication network resources is protected. UTM server (50), 20 dynamic QoD prediction and allocation engine (60), API server (40) and 6G core network (30) The data and control relationship between them is established in a closed-loop manner. Dynamic QoD prediction and allocation engine (60) uses flight path and real-time data to identify the target cell and After determining the resource requirement, it autonomously initiates the QoD call, 6G core. The network (30) creates the target resource profile. This closed loop, the process is technical personnel 25 6G core network with UTM platform without relying on initiative or manual intervention (30) It ensures that it is carried out between them. The key elements of the invention are shown in the schematic block diagram with their data and control relationships. This is shown with their connections, and the workflow of the method is shown with flight plan and real-time position data 30 from receiving the target cell to estimating the target cell, from querying the network load to making a QoD call. from resource reservation to cancellation of the old profile after switching between cells It involves sequential technical relationships. In one configuration of the invention, the system is used in smart cities, disaster and emergency crisis management, and 35 Extensive use of autonomous cargo drones for logistics carrying aid supplies A major "National UAV Management" will operate as the basic communication infrastructure under its coordination. 8 and can be at the very heart of the "Telecommunications Network" architecture. The same structure Within this scope, telecommunications infrastructure from a network architect's perspective, and the application. BGP and VRF enable communication with core routers and base stations. This can be extended to include functions for autonomously updating tables. The steps involved in the process are as follows:  Flight plan and real-time GPS data of the unmanned aerial vehicle provided by the UTM server. Transfer of location data to the dynamic QoD estimation and allocation engine (1001),  Speed ​​of the unmanned aerial vehicle is determined by the dynamic QoD estimation and allocation engine. By analyzing the vector, the target 6G base station to be transitioned to was previously determined to be 10. estimated (1002),  Target 6G base estimated by dynamic QoD estimation and allocation engine Querying the current network load at the station and using the query result Calculation of required bandwidth for unmanned aerial vehicle (1003),  Dynamic QoD estimation and allocation engine by unmanned aerial vehicle target 15 API just before entering a cell and before switching between cells. Calling the CAMARA QoD API through the server (1004),  Policy control functions on the 6G core network by the API server updating and as a result of this update, specific features on the target 6G base station Reserved 20 high-priority network slices or QoS streams for unmanned aerial vehicles. (1005),  The unmanned aerial vehicle must pass over the target 6G base station and beforehand Telemetry and video transmission with zero downtime via reserved resource. continuation (1006),  After the unmanned aerial vehicle completes its transition, 25 on the existing 6G base station Cancellation of the maintained QoD profile via API and standardization of resources return to subscribers (1007). During the study, the unmanned aerial vehicle (10) was moving in accordance with the flight plan, instantaneously It transmits position, speed and direction information to the UTM server (50). UTM server (50) 30 Dynamic QoD estimation and allocation based on flight plan and real-time GPS location data. The engine (60) continuously monitors the route as an intermediary layer running on MEC or the cloud. When the unmanned aerial vehicle (10) approaches the coverage limit of the existing 6G base station (20A) Dynamic QoD estimation and allocation engine (60) uses velocity and vector information to target 6G base Estimating the location of the station (20B) within milliseconds, targeting the 35 base stations at the 6G base station. (20B) queries the current network load and calculates the required bandwidth. Then 9 Before the unmanned aerial vehicle (10) enters the target cell, it uses the API server (40) The CAMARA QoD API call is made autonomously via the API server (40). As a result of the transmitted call, the 6G core network (30) updates its policy control functions and by reserving a high-priority network slice or QoS stream at the target 6G base station (20B) Guaranteed bandwidth and low latency profile ready before inter-cell switching. 5 It makes it so. When the unmanned aerial vehicle (10) connects to the target 6G base station (20B) Telemetry and video transmission continues uninterrupted via the previously allocated source. After the transition is complete, the dynamic QoD estimation and allocation engine (60) is available on the 6G base By canceling the QoD profile held at the station (20A) via API, the resources can be standardized. It ensures that it is returned to subscribers. 10

Claims

REQUESTS 1. Beyond line of sight for unmanned aerial vehicles using 6G telecommunications infrastructure. uninterrupted communication, 3D video transmission and ultra-low during flights Dynamic network optimization and management system 5 that enables delayed telemetry transmission. Its characteristic is;  Generates telemetry data and enables high-quality video transmission. unmanned aerial vehicle (10),  Existing 6G base station that provides wireless radio access with unmanned aerial vehicle (10) station (20A), 10  In the cell where the unmanned aerial vehicle (10) will pass in the direction of flight. Target 6G base station (20B) providing wireless radio access,  6G cores that route data traffic and host UPF and SMF network functions network (30),  API server that opens the capabilities of the telecommunications network to external systems (40), 15  UTM that manages the flight route, speed and permissions of the unmanned aerial vehicle (10) server (50),  Flight plan and real-time GPS position data received from the UTM server (50) and Using the speed and vector of the unmanned aerial vehicle (10), target 6G base 20 at the target 6G base station (20B) that predicts the location of the base station (20B). by querying the current network load, it calculates the required bandwidth and API server (40) before the unmanned aerial vehicle (10) enters the target cell Dynamic QoD that autonomously triggers the CAMARA QoD API call. Prediction and allocation engine (60) It includes. 25 2. The system compliant with claim 1, and its feature is; dynamic QoD estimation and allocation engine (60), Intermediate between the UTM server (50) and the telecom operator network on MEC or cloud. It is structured to function in layers.

3. System compliant with Request 1, its feature is; 6G core network (30), API server (40) As a result of the CAMARA QoD API call made over, on the target 6G base station (20B) high priority network slice or QoS stream for specific unmanned aerial vehicle (10) It is configured to reserve funds. 35 11 4. The system complies with claim 1 and its feature is; dynamic QoD estimation and allocation engine (60), After the unmanned aerial vehicle (10) passed the target 6G base station (20B), the existing 6G Cancel the QoD profile held at the base station (20A) via API and the source It is configured to return the value to standard subscribers.

5. System compliant with claim 1, its feature is; UTM server (50) and 6G core network (30) the resource allocation relationship between them depends on the initiative or manual of the technical staff It is structured to operate in a closed loop without the need for intervention. It is the fact that.

6. Beyond line of sight for unmanned aerial vehicles using 6G telecommunications infrastructure. uninterrupted communication, 3D video transmission and ultra-low during flights Dynamic network optimization and management method that enables delayed telemetry transmission. Its characteristic is;  Flight plan and real-time GPS data of the unmanned aerial vehicle are provided by the UTM server. Transfer of location data to the dynamic QoD estimation and allocation engine (1001),  Speed ​​of the unmanned aerial vehicle is determined by the dynamic QoD estimation and allocation engine. and the target 6G base station to be transitioned to by analyzing the vector foreseeable (1002),  Target 6G base 20 estimated by the dynamic QoD estimation and allocation engine. Querying the current network load at the station and the query result. Calculating the required bandwidth for the unmanned aerial vehicle using (1003),  Dynamic QoD estimation and allocation engine by unmanned aerial vehicle target. API 25 just before entering a cell and before switching between cells. Calling the CAMARA QoD API through the server (1004),  Policy control functions on the 6G core network by the API server updating and as a result of this update, the target 6G base station high priority network slice or QoS stream for a specific unmanned aerial vehicle reservation (1005), 30  The unmanned aerial vehicle must pass over the target 6G base station and beforehand Telemetry and video transmission with zero downtime via reserved resource. continuation (1006),  After the unmanned aerial vehicle completes its pass, it will connect to the existing 6G base station. The QoD profile held is cancelled via API and resources are standardized to 35 return to subscribers (1007) 12 It includes the steps of the process.

7. The method is compliant with claim 6, and its feature is; unmanned aerial vehicle (10) existing 6G base When approaching the coverage limit of the base station (20A), the target 6G base station (20B) Predicting the flight path within milliseconds based on the flight vector takes 5 steps. It includes.

8. This is a method compliant with Claim 6, characterized by the fact that it allows for the sole flight of a high-priority network resource. on its route and the instantaneous location of the unmanned aerial vehicle (10) or It includes the step of separating the areas it is approaching. 10

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