Control device for wireless access network

Abstract

A control unit (Non-RT RIC) for a non-real-time system and a control unit (Near-RT RIC) for a near-real-time system are layered, and learning reasoning units (11, 12, 13, 16, 17, 18), which control a wireless access network on the basis of the result of reasoning by applying latest data to a learning model generated on the basis of data collected from an O-RAN base station device (10), are disposed in the control unit for the near-real-time system. Furthermore, on the basis of the history of the collected data, whether or not a concept drift has occurred is detected, and if the occurrence of the concept drift is detected, some (19, 20) of a re-learning unit that re-learns the learning model by the learning reasoning unit is disposed in a control unit of a near real-time system, and the other (14, 15) is disposed in a control unit of a non-real-time system.

Description

Technical Field

[0001] The present invention relates to a control device for a wireless access network, and more particularly to a control device for a wireless access network having a function of relearning a learning model generated by learning data collected from the wireless access network. Background Art

[0002] In the Radio Access Network (RAN), the functions of the previously integrated base station are divided into the CU (Centralized Unit) for session processing, the DU (Distributed Unit) for baseband processing, and the RU (Radio Unit) for wireless processing. The O-RAN Alliance (Open Radio Access Network Alliance) is promoting research on specifications for opening up the interface specifications between the units.

[0003] In the Beyond 5G (beyond the fifth generation communication technology) system, it is expected that the performance such as throughput, communication latency, and number of connections will be further improved, and a variety of services (such as robot control, connected cars, AR / VR, etc.) will be provided. AI (artificial intelligence) / ML (machine learning) is attracting attention as a key technology for realizing these.

[0004] In non-patent literature 1 and 2, the following situation is studied: in order to maximize network performance within the limited network resources in RAN, AI / ML is applied to various purposes such as beamforming control, wireless resource allocation, traffic prediction, base station function configuration, etc.

[0005] Non-patent document 3 discloses a technology of generating a learning model by learning based on data collected from the RAN, performing inference using the data collected from the RAN and the learning model, and controlling the RAN based on the inference result.

[0006] However, as time passes and the environment changes, the characteristics of data used for inference may change from the data at the time of learning (concept drift), which may reduce the inference performance of the model.

[0007] In response to the technical problems described above, the inventors of the present invention proposed an AI system that accumulates and monitors data related to AI / ML learning and reasoning based on an O-RAN base station device, detects concept drift, and performs relearning, and filed a patent application (Patent Document 1).

[0008] Figure 5 This is a functional block diagram showing the conventional structure of an AI system that detects concept drift and performs relearning.

[0009] The data collection unit 11 repeatedly collects the latest data from the O-RAN base station device 10, provides the collected latest data (collected data) to the AI / ML learning unit 12 and the AI / ML reasoning unit 13, and accumulates the collected latest data (collected data) in the data accumulation unit 14. The collected data accumulated in the data accumulation unit 14 is managed by the AI / ML database 15. The AI / ML learning unit 12 learns the collected data and generates a learning model for controlling the O-RAN base station device 10.

[0010] The AI / ML model management unit 16 manages the learning model generated in the past by the AI / ML learning unit 12. The AI / ML reasoning unit 13 performs reasoning based on the newly collected data and the learning model collected by the data collection unit 11, and outputs the reasoning result to the control unit 17 and the reasoning performance measurement unit 18. The control unit 17 controls the O-RAN base station device 10 based on the reasoning result.

[0011] The inference performance measurement unit 18 determines the inference performance based on the latest data collected after the control unit 17 controls the O-RAN base station device 10 based on the inference result and the inference result, and stores inference performance data indicating the determined inference performance in the AI / ML database 15 .

[0012] The concept drift detection unit 19 periodically acquires at least one of the collected data and the inference performance data from the AI / ML database 15 to determine whether a concept drift has occurred. If the occurrence of a concept drift is detected, the concept drift detection unit 19 instructs the relearning control unit 20 to generate a new learning model (relearning). The relearning control unit 20 provides the AI / ML learning unit 12 with data for relearning and instructs relearning.

[0013] If re-learning is instructed, the AI / ML learning unit 12 generates a new learning model based on the newly collected data by the data collection unit 11, and outputs the new learning model to the AI / ML model management unit 16. The AI / ML model management unit 16 compares the current learning model used by the AI / ML reasoning unit 13 with the new learning model, and outputs the new learning model to the AI / ML reasoning unit 13 if the reasoning performance based on the new learning model is higher than the reasoning performance based on the current learning model.

[0014] Thereafter, the AI / ML reasoning unit 13 performs reasoning using the new learning model. If the reasoning performance based on the new learning model is lower than the reasoning performance based on the current learning model, the AI / ML model management unit 16 can instruct the AI / ML learning unit 12 to relearn.

[0015] Prior art literature

[0016] Patent Literature

[0017] Patent Document 1: Japanese Patent Application No. 2022-046347

[0018] Non-patent literature

[0019] Non-patent document 1: MEMorocho-Cayamcela, H.Lee and W.Lim, "Machine Learning for 5G / B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions," in IEEE Access, vol.7, pp.137184-137206, 2019

[0020] Non-patent literature 2: J.Kaur, MAKhan, M.Iftikhar, M.Imran and Q.Emad Ul Haq, "Machine Learning Techniques for 5G and Beyond," in IEEE Access, vol.9, pp.23472-23488, 2021

[0021] Non-patent document 3: O-RAN Alliance, “AI / ML workflow description and requirements,” O-RAN.WG2.AIML-vo1.03, Jul.2021 Summary of the invention

[0022] Problems to be solved by the invention

[0023] like Figure 6 As shown, the RAN intelligent controller (RIC) responsible for controlling and optimizing RAN functions has a hierarchical structure of "Non-RT (Real Time) RIC" which is a non-real-time system component with different control cycles and "Near-RT RIC" which is a near-real-time system component.

[0024] Here, there are different characteristics as follows: Non-RT RIC has a large area that becomes the control object when the control period is more than 1 sec. On the contrary, Near-RT RIC has a small area that becomes the control object when the control period is 10msec to 1sec. Therefore, the optimal configuration of each functional block related to AI / ML of Near-RT RIC and Non-RT RIC has been studied in the past.

[0025] For example, it is assumed that the Non-RT RIC is set up in the machine room and the Near-RT RIC is configured at the edge site such as the roof of the building. In this case, the following technical problems may arise: Prioritizing real-time performance, not only the functions involved in AI / ML learning, but also all the functions involved in AI / ML re-learning are configured in the Near-RT RIC.

[0026] First, the processing load of Near-RT RIC increases. That is, edge sites have power and space limitations, so they cannot be equipped with sufficient computers.

[0027] Second, only the information below the Near-RT RIC level can be used for concept drift detection. That is, since the information of neighboring regions cannot be used, the detection of concept drift is delayed.

[0028] For example, if road construction occurs in a certain area and the volume of connected car traffic changes, the change in vehicle traffic will also affect adjacent areas. In this case, if only the information of the own area is monitored, the environmental changes caused by the road construction cannot be detected immediately, so the follow-up of re-learning of environmental changes becomes low.

[0029] Means used to solve problems

[0030] The object of the present invention is to provide a control device for a wireless access network as follows: solving the above-mentioned technical problems and optimizing the configuration of Near-RT RIC and Non-RT RIC of functional blocks related to AI / ML.

[0031] In order to achieve the above-mentioned purpose, the control device of the wireless access network formed by hierarchical control units of a non-real-time system and a near-real-time system of the present invention comprises: a learning and reasoning unit, which generates a learning model based on data collected from the wireless access network, and controls the wireless access network based on the result inferred by applying the latest collected data to the learning model; and a re-learning unit, which detects whether a concept drift occurs based on the history of the collected data, and if the occurrence of concept drift is detected, causes the learning and reasoning unit to re-learn the learning model, and the learning and reasoning unit is configured in the control unit of the near-real-time system, and the re-learning unit is dispersedly configured in each control unit of the near-real-time system and the non-real-time system.

[0032] Effects of the Invention

[0033] According to the present invention, the following effects are achieved.

[0034] (1) The functions involved in learning and reasoning are arranged in the control unit of the near real-time system, and the functions involved in re-learning are dispersedly arranged in the control units of the near real-time system and the non-real-time system, thereby reducing the processing load of the control unit of the near real-time system.

[0035] (2) Since part of the functions related to relearning is arranged in the control unit of the non-real-time system, even if the computer resources of the edge site are limited, the learning model can be relearned.

[0036] (3) Since the functions related to the detection and relearning control of concept drift are arranged in the control unit of the near real-time system, the detection and relearning of concept drift can be performed at high speed using only the information of the lower level of the control unit of the near real-time system.

[0037] Other features and advantages of the present invention will become apparent from the following description with reference to the accompanying drawings. In the accompanying drawings, the same or similar components are denoted by the same reference numerals. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are included in and constitute a part of the specification, illustrate embodiments of the present invention, and together with the description, are used to explain the principles of the present invention.

[0039] Figure 1 This is a functional block diagram showing the configuration of a main part of an O-RAN control device according to an embodiment of the present invention.

[0040] Figure 2 This diagram shows an example of data added to the A1 interface in order to implement a request for information to the AI / ML database.

[0041] Figure 3 This is a diagram showing an example of a combination of inference performance data and network information transmitted via the A1 interface in a tabular format.

[0042] Figure 4 It is a sequential flow chart showing the actions of the present invention.

[0043] Figure 5 This is a functional block diagram showing the conventional structure of an AI system that detects concept drift and performs relearning.

[0044] Figure 6 It is a functional block diagram of the RAN Intelligent Controller (RIC). DETAILED DESCRIPTION

[0045] Hereinafter, the embodiments are described in detail with reference to the accompanying drawings. In addition, the following embodiments do not limit the invention involved in the technical solution. In addition, not all of the feature combinations described in the embodiments are necessary for the invention. Two or more of the multiple features described in the embodiments can be combined arbitrarily. In addition, the same or identical structures are marked with the same symbols, and repeated descriptions are omitted.

[0046] Figure 1This is a functional block diagram showing the structure of the main part of the O-RAN control device involved in one embodiment of the present invention, and the illustration of the structure that is not necessary for the description of the present invention is omitted. In addition, the same reference numerals as those described above represent the same or equivalent parts. The feature of this embodiment is that the functions involved in AI / ML learning are configured in Near-RT RIC, and the functions involved in AI / ML re-learning are configured in Non-RT RIC.

[0047] The O-RAN control device is composed of O-CU / O-DU31, Near-RT RIC32 and Non-RT RIC33. Each function can notify each other through various interfaces including O1 interface, A1 interface and E2 interface specified by O-RAN Alliance. The O-RAN base station device 10 is configured in O-CU / O-DU31.

[0048] The Near-RT RIC32 is mainly configured with a data collection unit 11, an AI / ML learning unit 12, an AI / ML reasoning unit 13, an AI / ML model management unit 16, a control unit 17 and an reasoning performance measurement unit 18 as functions related to AI / ML learning and reasoning.

[0049] The Near-RT RIC 32 is also provided with a concept drift detection unit 19 and a relearning control unit 20 as part of the functions related to relearning of the learning model. The Non-RT RIC 33 is provided with a data storage unit 14 and an AI / ML database 15 as other functions related to relearning of the learning model.

[0050] In the Near-RT RIC 32, the data storage unit 14 collects the latest data from the O-RAN base station device 10, and sends the latest data to the data storage unit 14 of the Non-RT RIC 33 via the O1 interface. The inference performance measurement unit 18 sends the inference performance data to the AI / ML model management unit 16 and the concept drift detection unit 19, and sends the inference performance data to the AI / ML database 15 of the Non-RT RIC 33 via the A1 interface. The concept drift detection unit 19 sends the detection of concept drift to the AI / ML database 15 of the Non-RT RIC 33 via the A1 interface.

[0051] As described above, in this embodiment, the functions related to relearning are distributed in the Non-RT RIC 31 and the Near-RT RIC 32 , and therefore the following three pieces of information are particularly added to the A1 interface as information for detecting concept drift.

[0052] (1) Request for information from AI / ML database 15

[0053] (2) Sending of designated information

[0054] (3) Drift detection notification

[0055] In order to realize the request for information of the (1) AI / ML database 15, in this embodiment, "data type", "object of acquired data", "object of acquired data" and "interval of data acquisition" are added. "Data type" includes "inference performance data" and "network information (information collected from the base station through the O1 interface)".

[0056] In order to transmit the information designated in (2) above, in this embodiment, each data is transmitted in a table format.

[0057] In order to notify the above-mentioned (3) drift detection, in this embodiment, the Near-RT RIC can request the Non-RT RIC to relearn data.

[0058] The following is a more detailed description of the information added to the A1 interface. Regarding the request for information on the (1) AI / ML database, for the “inference performance data” in the “data type”, the following are listed in O-RAN WG2: Figure 2 Indicators shown.

[0059] In this index, the index for binary classification problems, the index for multiclass classification problems, and the index for regression classification problems are respectively specified. Figure 2 By selecting a plurality of values ​​as "inference performance data", the average value and median value of the specified period can be obtained.

[0060] Regarding "network information" in the "data type" described above, the following IDs (0 to 4) are assigned to elements of the network information in advance, and the network information can be specified by the IDs.

[0061] 0: Throughput

[0062] 1: Communication delay

[0063] 2: Wireless resource utilization

[0064] 3: Computer resource usage

[0065] 4: Transmission path resource utilization

[0066] For the "object of data to be acquired", for example, the object of acquiring inference performance data and network information is specified by cell ID, slice ID and UEID. In the "interval of data acquisition", the interval of data acquisition is specified.

[0067] Regarding the sending of the specified information (2), in this embodiment, Figure 3 An example is shown in which m sets of i types of inference performance data (p1 to pi) and c types of network information (n1 to nc) specified by requesting information from the (1) AI / ML database are sent in a table format at a specified time interval.

[0068] Regarding the notification of drift detection (re-learning data request) (3), in this embodiment, when the concept drift detection unit 19 detects concept drift, re-learning data request is made to the Non-RT RIC. At this time, the target learning model is specified by the ID.

[0069] Figure 4 This is a sequence flow chart showing the operation of this embodiment, and the description here focuses on the communication between the O-CU / O-DU, Near-RT RIC, and Non-RT RIC.

[0070] In this embodiment, communication between the O-CU / O-DU and the Near-RT RIC is performed via the E2 interface, and communication between the Near-RT RIC and the Non-RT RIC is performed via the O1 interface or the A1 interface.

[0071] The O-CU / O-DU repeatedly transmits the latest data of the O-RAN base station apparatus 10 to the Near-RT RIC at a predetermined period. In the present embodiment, at time t1, the O-CU / O-DU transmits the latest data to the Near-RT RIC via the E2 interface.

[0072] In the Near-RT RIC, the latest data is acquired by the data collection unit 11. The data collection unit 11 sends the latest data to the Non-RT RIC via the O1 interface at time t2. The AI / ML reasoning unit 13 applies the latest data to the current learning model to perform reasoning, and notifies the control unit 17 and the reasoning performance measurement unit 18 of the reasoning result. The control unit 17 instructs the O-RAN base station device 10 of the O-CU / O-DU to control based on the reasoning result.

[0073] The inference performance measuring unit 18 estimates the inference performance based on the latest data collected after the control unit 17 controls the O-RAN base station device 10 based on the inference result and the inference result, and transmits the inference performance data based on the latest data to the Non-RT RIC via the O1 interface at time t4. The latest data and the inference performance data are stored in the AI / ML database 15.

[0074] On the other hand, the Near-RT RIC periodically requests information for detecting concept drift from the Non-RT RIC. In this embodiment, at time t5, the concept drift detection unit 16 of the Near-RT RIC requests the loss value from the Non-RTRIC via the A1 interface as the throughput and inference performance data of the service in each cell under each Near-RT RIC.

[0075] At time t6, the requested information is transmitted from the Non-RT RIC to the Near-RT RIC via the A1 interface. In the Near-RT RIC, each time the requested information is acquired, the concept drift detection unit 19 detects concept drift based on the acquired information.

[0076] Thereafter, if the concept drift detection unit 19 detects concept drift at time t7, the concept drift detection is notified to the Non-RT RIC via the A1 interface at time t8. At time t9, the AI / ML database 15 of the Non-RT RIC responds to the notification of the concept drift detection and sends the relearning data to the Near-RT RIC via the A1 interface. In the Near-RTRIC, the relearning control unit 20 notifies the AI / ML learning unit 12 of the relearning instruction and the relearning data.

[0077] The AI / ML learning unit 12 relearns the learning model at time t10, and updates and registers the relearned learning model in the AI / ML model management unit 16. Therefore, when the latest data is received, control based on the relearned learning model is performed.

[0078] According to this embodiment, the functions involved in learning reasoning, the detection of concept drift in the functions involved in relearning, and the functions involved in the control of relearning are configured in the Near-RT RIC. On the other hand, the functions involved in the accumulation of data for the detection of concept drift in the functions involved in relearning are configured in the Non-RT RIC, thereby reducing the processing load of the Near-RT RIC.

[0079] Therefore, even in an environment where concept drift frequently occurs, if the computer resources at the edge site are relatively abundant, concept drift can be detected immediately, and the ability to follow environmental changes can be improved.

[0080] As a result, depending on the implementation method, it can contribute to Goal 9 of the United Nations-led Sustainable Development Goals (SDGs) to "build resilient infrastructure and promote inclusive and sustainable industry" and Goal 11 to "make cities inclusive, safe, resilient and sustainable."

[0081] The present invention is not limited to the above-described embodiments, and various changes and modifications can be made without departing from the spirit and scope of the present invention. Therefore, in order to disclose the scope of the present invention, the following claims are attached.

[0082] This application claims priority based on Japanese patent application No. 2022-149390 filed on September 20, 2022, the entire contents of which are incorporated herein by reference.

Claims

1. A control device for a wireless access network, wherein a control unit of a non-real-time system and a control unit of a near-real-time system are hierarchically connected, wherein: The control device of the wireless access network comprises: a learning inference unit that generates a learning model based on data collected from the wireless access network, and controls the wireless access network based on a result inferred by applying the latest collected data to the learning model; as well as a re-learning unit that detects whether a concept drift occurs based on the history of the collected data, and if the occurrence of a concept drift is detected, causes the learning and reasoning unit to re-learn the learning model, The learning and inference unit is arranged in the control unit of the near real-time system, and the re-learning unit is dispersedly arranged in each control unit of the near real-time system and the non-real-time system.

2. The control device for a wireless access network according to claim 1, characterized in that: The learning reasoning unit comprises: a data collection unit that collects the latest data from the wireless access network; A learning unit, which generates a learning model based on the collected data; a control unit configured to control the wireless access network based on a result inferred by applying the collected data to the learning model; as well as The inference performance measuring unit measures the inference performance based on the collected data and the inference result.

3. The control device for a wireless access network according to claim 2, characterized in that: The relearning unit comprises: A database storing the collected data and the inference performance; a concept drift detection unit that detects the occurrence of concept drift based on the data accumulated in the database and the reasoning performance; as well as a re-learning control unit, which, if the occurrence of the concept drift is detected, sends information for causing the learning inference unit to re-learn the learning model; The database is configured in a control unit of the non-real-time system.

4. The control device for a wireless access network according to claim 3, characterized in that: A request for information on the database is notified from the control unit of the near real-time system to the control unit of the non-real-time system via the A1 interface.

5. The control device for a wireless access network according to claim 4, characterized in that: The control unit of the non-real-time system notifies the control unit of the near-real-time system of the requested information via the A1 interface. The concept drift detection unit detects occurrence of concept drift based on the notified information.

6. The control device for a wireless access network according to claim 5, characterized in that: The occurrence of the concept drift detected by the concept drift detection unit is notified from the control unit of the near real-time system to the control unit of the non-real-time system via the A1 interface.

7. The control device for a wireless access network according to claim 6, characterized in that: When the occurrence of the concept drift is notified, the database disposed in the control unit of the non-real-time system transmits data used for re-learning the learning model to the control unit of the near-real-time system via the A1 interface.

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