A cell replacement system in beyond 5g and 6g networks

Abstract

The present invention relates to a system (1) which is developed for providing artificial intelligence and machine learning-aided cell change in beyond 5G and 6G mobile communication networks.

Description

[0001] DESCRIPTION

[0002] A CELL REPLACEMENT SYSTEM IN BEYOND 5G AND 6G

[0003] NETWORKS

[0004] Technical Field

[0005] The present invention relates to a system for providing artificial intelligence and machine learning-aided cell change in beyond 5G and 6G mobile communication networks.

[0006] Background of the Invention

[0007] Although 4G and 5G mobile communications systems have made significant make progress in providing high-speed data transmission and wide coverage, these technologies may remain incapable in some cases. Today, users need to download and upload a large amount of data anytime and anywhere and this becomes even more prominent with the widespread use of social media, video streaming, cloud services and other data-intensive applications. Furthermore, ensuring a seamless service experience is vital, especially in business, healthcare and emergency situations. Therefore, it has become inevitable to develop advanced technology solutions which provide higher speeds, lower latency and wider coverage beyond the existing network infrastructures. And this pushes the limits of mobile communication systems in a world where permanent connection and fast data access are critical.

[0008] Therefore, considering the studies and deficiencies included in the current technique, it is understood that there is a need for a system for providing artificial intelligence and machine learning-aided cell change in beyond 5G and 6G mobile communication networks. The United States patent document no. US2024107597, an application included in the state of the art, discloses enhancing wireless communication efficiency in 5G / 6G networks through AI / ML model management and deployment. The said invention presents methods of leveraging artificial intelligence and machine learning (AI / ML) models to enhance wireless communications efficiency in 5G / 6G networks. The processes comprise storing, configuring, and transferring AI / ML models within base stations and user equipment (UE) devices and this allows for localized decision-making and improved network performance. Dynamic model activation / deactivation, model compression / decompression, and encoding / decoding method negotiation are among the features. Periodic or condition-driven model updates ensure responsiveness capability to network changes and also model replacements enable upgrades and iterations. The system facilitates seamless handovers between base stations and shares information about model capabilities and UE specifics. Model storage and configuration can also be carried out in the UE and this empowers it for local decision-making in variable or challenging network conditions. These techniques contribute to significant performance, efficiency, and reliability improvements in 5G / 6G wireless networks.

[0009] Summary of the Invention

[0010] An object of the present invention is to realize a system which is developed for providing artificial intelligence and machine learning-aided cell change in beyond 5G and 6G mobile communication networks.

[0011] Detailed Description of the Invention

[0012] “A Cell Replacement System in Beyond 5G and 6G Networks” realized to fulfil the objective of the present invention is shown in the figure attached, in which:

[0013] Figure 1 is a schematic view of the inventive system. The components illustrated in the figure are individually numbered, where the numbers refer to the following:

[0014] 1. System

[0015] 2. Database

[0016] 3. Server

[0017] The inventive system (1) developed for providing artificial intelligence and machine learning-aided cell change in beyond 5G and 6G mobile communication networks comprises at least one database (2) which is configured to keep a record of signal level RSRP (Reference Signal Reference Power) and quality RSRQ (Reference Signal Received Quality) values and network data of cells where users and their neighbours are located; and at least one server (3) which is configured to reduce unsuccessful cell-to- cell (intercellular) handover by considering the trajectory followed by end users or user equipment (UE-User Equipment), the speed, the previous successful / unsuccessful cell-to-cell handover statistics at cell, base station or base station cluster resolution in addition to the main performance criteria of signal level RSRP and signal quality RSRQ; and to avoid ping-pong cases (a short-term remain in a cell or a very quick return to the old cell) except for unsuccessful cell-to-cell handover.

[0018] The database (2) included in the inventive system (1) is configured to establish communication and to exchange data with the server (3) by using any communication protocol.

[0019] The server (3) included in the inventive system (1) is configured to establish communication and to exchange data with the database (2) by using any communication protocol. The server (3) is configured to ensure that signal level RSRP and signal quality RSRQ values of the cell where the user and his / her neighbours is / are located, is measured and then controlled. The server (3) is configured to ensure that cell-to-cell handover is only carried out according to RSRP and / or RSRQ as previously defined by 3GPP standards if the measured RSRP and / or RSRQ values require cell-to-cell handover, if artificial intelligence / machine learning is supported in the measured cell / base station / cluster. The server (3) is configured to ensure that artificial intelligence or machine learning training data are controlled at the measured cell / base station / cluster level; and if there is no training data, to realize training based on signal level, quality, previous cell-to-cell handover, user device speed and trajectory information. The server (3) is configured to ensure that target cell estimation or prediction is carried out according to artificial intelligence algorithms, in cell-to-cell handovers according to the training data and the triggering RSRP and / or RSRQ values. The server (3) is configured to ensure that the cycle is continued at the user device until the need for cell-to-cell handover is fulfilled again. The server (3) is configured to enable the user device to return the routine measurements when there is no need for cell-to- cell handover.

[0020] Industrial Application of the Invention

[0021] With the inventive system (1), it is ensured to provide artificial intelligence and machine learning-aided cell change in beyond 5G and 6G mobile communication networks.

[0022] Within these basic concepts; it is possible to develop various embodiments of the inventive “A Cell Replacement System (1) in Beyond 5G and 6G Networks”; the invention cannot be limited to examples disclosed herein and it is essentially according to claims.

Claims

CLAIMS1. A system ( 1 ) for providing artificial intelligence and machine learning -aided cell change in beyond 5G and 6G mobile communication networks; comprising at least one database (2) which is configured to keep a record of signal level RSRP (Reference Signal Reference Power) and quality RSRQ (Reference Signal Received Quality) values and network data of the cells where users and their neighbours are located; and characterized by at least one server (3) which is configured to reduce unsuccessful cell- to-cell (intercellular) handover by considering the trajectory followed by end users or user equipment (UE-User Equipment), the speed, the previous successful / unsuccessful cell-to-cell handover statistics at cell, base station or base station cluster resolution in addition to the main performance criteria of signal level RSRP and signal quality RSRQ; and to avoid ping-pong cases (a short-term remain in a cell or a very quick return to the old cell) except for unsuccessful cell-to-cell handover.

2. A system ( 1 ) according to Claim 1 ; characterized by the database (2) which is configured to establish communication and to exchange data with the server (3) by using any communication protocol.

3. A system (1) according to Claim 1 or 2; characterized by the server (3) which is configured to establish communication and to exchange data with the database (2) by using any communication protocol.

4. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to is configured to ensure that signallevel RSRP and signal quality RSRQ values of the cell where the user and his / her neighbours is / are located, is measured and then controlled.

5. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to ensure that cell-to-cell handover is only carried out according to RSRP and / or RSRQ as previously defined by 3GPP standards if the measured RSRP and / or RSRQ values require cell-to- cell handover, if artificial intelligence / machine learning is supported in the measured cell / base station / cluster.

6. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to ensure that artificial intelligence or machine learning training data are controlled at the measured cell / base station / cluster level; and if there is no training data, to realize training based on signal level, quality, previous cell-to-cell handover, user device speed and trajectory information.

7. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to ensure that target cell estimation or prediction is carried out according to artificial intelligence algorithms, in cell-to-cell handovers according to the training data and the triggering RSRP and / or RSRQ values.

8. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to ensure that the cycle is continued at the user device until the need for cell-to-cell handover is fulfilled again.

9. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to enable the user device to return the routine measurements when there is no need for cell-to-cell handover.

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