Control device for radio access network
By generating and selectively replacing learning models based on measured inference speeds, the control device for RANs addresses the issue of decreased inference speed, ensuring efficient RAN control operations.
Patent Information
- Application Number
- JP2023194466
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- Filing Date
- 2023-11-15
- Publication Date
- 2025-05-27
AI Technical Summary
The inference speed of learning models used for RAN control may decrease due to limitations in computing resources and varying RAN control situations, leading to incomplete inference within target times.
A control device for RANs that performs machine learning to generate learning models, transmits these models to another control device, and determines whether to replace them with faster models based on measured inference speeds and reference values.
This approach effectively suppresses decreases in inference speed, ensuring timely and efficient RAN control operations.
Smart Images
Figure 2025081004000001_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to control technologies for radio access networks (RANs).
Background Art
[0002] FIG. 1 shows a control configuration of a RAN proposed by the Open Radio Access Network (O-RAN) Alliance. As shown in FIG. 1, a first control function for performing long-term control and a second control function for performing short-term control are defined. In O-RAN, the first control function is called a Non-Real-Time RAN Intelligent Controller (Non-RT RIC), and the second control function is called a Near-Real-Time RAN Intelligent Controller (Near-RT RIC).
[0003] The first control function and the second control function are connected via an A1 interface. The second control function controls components of the RAN, such as a Central Unit (CU) and a Distributed Unit (DU), via an E2 interface. Note that controlling the CU and DU includes notifying / setting various parameter values used by the CU and DU in their processing to the CU and DU, and instructing the CU and DU to execute some operation. Further, in the following description, the term "RAN" is used as a general term for its components. Therefore, for example, "controlling the RAN" means controlling components of the RAN, such as the CU and DU. The first control function, the second control function, and the RAN are further connected via an O1 interface. The O1 interface can be used to transmit traffic data, performance data, fault data, etc. measured / detected and accumulated by the RAN.
[0004] Non-Patent Document 1 discloses a configuration that utilizes machine learning for RAN control. Specifically, Non-Patent Document 1 discloses performing machine learning based on various learning data collected from the RAN and the like to generate a learning model, making inferences using the learning model, and controlling the RAN according to the inference results. In one of the multiple configurations disclosed in Non-Patent Document 1, a first control function generates a learning model. And a second control function controls the RAN using the learning model generated by the first control function. Furthermore, in one of the multiple configurations disclosed in Non-Patent Document 1, the first control function generates a learning model and distributes it to the second control function. The second control function updates the learning model received from the first control function by machine learning and controls the RAN using the learning model.
[0005] Non-Patent Document 2 and Non-Patent Document 3 disclose various RAN control contents based on the inference results of the learning model. As an example, the RAN control contents include beamforming control, radio resource allocation, traffic prediction, CU and DU placement control by virtualization technology, and the like.
Prior Art Documents
Non-Patent Documents
[0006]
Non-Patent Document 1
Non-Patent Document 2
Non-Patent Document 3
Summary of the Invention
Problems to be Solved by the Invention
[0007] For example, when the second control function controls the RAN based on the learning model received from the first control function or the learning model obtained by modifying the learning model, it is necessary to complete the inference within the target time according to the control content. However, in the learning model received from the first control function or the learning model obtained by modifying the learning model, there may be cases where the inference cannot be completed within the target time. For example, the second control function executes various RAN controls using a plurality of learning models generated for each of the plurality of control contents. Therefore, due to the limitation of the computing resources of the second control function, the inference speed using each individual learning model may vary. Furthermore, the inference speed may also vary depending on changes in the situation of the RAN to be controlled and the like. In the present disclosure, the inference speed may be defined as the time from when data is input to the learning model until the inference result is obtained. However, it is also possible to define the inference speed as the time from when data is input to the learning model until the inference result is obtained and the control based on the inference result is completed.
[0008] The present disclosure provides a technique for suppressing a decrease in the inference speed due to the learning model used for RAN control.
Means for Solving the Problems
[0009] According to one aspect of the present invention, a control device for a radio access network (RAN) includes a generation means for performing machine learning based on learning data to generate a first learning model, a transmission means for transmitting the first learning model to another control device that controls the RAN using the first learning model, obtaining one or more measurement values of the inference speed by the first learning model from the another control device, and based on the one or more measurement values and a reference value of the inference speed, determining whether to transmit to the another control device a second learning model that is used for the same control as the first learning model and has a faster inference speed than the first learning model. When the determination means determines to transmit the second learning model to the another control device, the transmission means transmits the second learning model to the another control device.
Advantages of the Invention
[0010] According to the present disclosure, it is possible to suppress a decrease in the inference speed of the learning model used for controlling the RAN.
Brief Description of the Drawings
[0011]
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Embodiments for Carrying Out the Invention
[0012] Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. Note that the following embodiments do not limit the invention according to the claims, and not all combinations of the features described in the embodiments are essential for the invention. Two or more of the features described in the embodiments may be arbitrarily combined. Also, the same or similar configurations are given the same reference numerals, and redundant descriptions are omitted.
[0013] <First Embodiment> FIG. 2 shows the control configuration of the RAN according to this embodiment. The configuration of FIG. 2 is basically the same as the configuration of FIG. 1. The first control device 100 performs long-term control of the RAN, for example, a device that implements the function of the Non-RT RIC of O-RAN. The second control device 200 is a device that controls the RAN at a shorter period than the first control device 100, for example, a device that implements the function of the Near-RT RIC of O-RAN. The first control device 100 is connected to one or more second control devices 200. In the example of FIG. 2, the first control device 100 is connected to three second control devices 200, which is an illustration, and the number of second control devices 200 connected to the first control device 100 can be any number of 1 or more.
[0014] In this embodiment, the first control device 100 performs machine learning to generate a learning model, and distributes the generated learning model to each second control device 200. Note that the first control device 100 generates a learning model corresponding to each of one or more control contents by each second control device 200 and distributes it to each second control device 200. Each second control device 200 is associated with a geographical area, and uses one or more learning models obtained from the first control device 100 to control the components of the RAN arranged in the associated geographical area. Note that what the second control device 200 controls is a part of the geographical area associated with the second control device 200 within the RAN of the mobile communication network, and this part of the geographical area associated with the second control device 200 within the RAN of the mobile communication network is also denoted as the RAN.
[0015] FIG. 3 is a configuration diagram of the first control device 100. The communication unit 14 performs communication processing with the second control device 200 via the A1 interface and communication processing with the second control device 200 and the RAN via the O1 interface. The learning data collected from the RAN via the O1 interface is stored in the storage unit 12. In this embodiment, it is assumed that the learning data is collected from the RAN, but a configuration in which the learning data is collected from the second control device 200 may also be used. When collecting the learning data from the second control device 200, the O1 interface or the A1 interface may be used. The learning unit 11 performs machine learning based on the learning data stored in the storage unit 12 to generate a learning model. The learning unit 11 transmits the generated learning model to each second control device 200 via the A1 interface of the communication unit 14. Note that the O1 interface can also be used to transmit the learning model to each second control device 200. The control unit 13 transmits and receives control messages to and from each second control device 200 via the communication unit 14. In this embodiment, the control unit 13 transmits and receives control messages to and from each second control device 200 using the O1 interface, but a configuration using the A1 interface may also be used. Further, the control unit 13 controls the learning unit 11.
[0016] FIG. 4 is a configuration diagram of the second control device 200. The inference control unit 21 performs inference based on the learning model received from the first control device 100 via the communication unit 24 and controls the RAN via the E2 interface. The control unit 23 transmits and receives control messages to and from the first control device 100 via the communication unit 24. For example, when the control unit 23 receives a measurement request from the first control device 100, the control unit 23 causes the measurement unit 22 to measure the inference speed using the learning model by the inference control unit 21. When the control unit 23 receives measurement data indicating the measurement result of the inference speed from the measurement unit 22, the control unit 23 transmits the measurement result to the first control device 100 by a control message.
[0017] The measurement unit 22 measures the inference speed based on the measurement instruction from the control unit 23 and outputs measurement data indicating the measurement result to the control unit 23. The measurement unit 23 is configured to measure the inference speed based on at least one of, for example, measurement method A and measurement method B. Measurement method A measures the inference speed when the inference control unit 21 actually controls the RAN. Measurement method B measures the inference speed by causing the inference control unit 21 to execute inference with the test data as the input of the learning model. The test data used in measurement method B is, for example, stored in the measurement unit 22 in advance. Alternatively, the test data used in measurement method B is, for example, received from the first control device 100 together with the measurement request. In the present embodiment, the first control device 100 acquires learning data from the RAN. However, when the first control device 100 acquires learning data from the second control device 200, the second control device 200 acquires learning data from the RAN via, for example, the O1 interface or the E2 interface, and transmits the learning data to the first control device 100 via the A1 interface or the O1 interface.
[0018] FIG. 5 is a sequence diagram of the method according to the present embodiment. In S1, the control unit 13 of the first control device 100 transmits a measurement request message instructing the measurement of the inference speed by the learning model to the second control device 200. When the second control device 200 uses a plurality of learning models corresponding to a plurality of control contents, the measurement request message may include information indicating the "target model", which is the learning model for which the inference speed is to be measured. Note that the number of "target models" may be one or plural. Further, when the second control device 200 is capable of executing both measurement method A and measurement method B, the measurement request message may include information specifying the measurement method. When specifying measurement method B, the measurement request message may include test data. Furthermore, the measurement request message may include information indicating the measurement period of the inference speed.
[0019] In S2, the control unit 23 of the second control device 200 causes the measurement unit 22 to measure the inference speed of the target model according to the measurement request message. In S3, the control unit 23 of the second control device 200 transmits the measurement result to the first control device 100.
[0020] FIG. 6 shows an example of the measurement result transmitted in S3 when the measurement requests for the inference speeds of the two learning models, the target model #1 and the target model #2, are received in S1. The measurement unit 22 repeatedly measures the inference speed during the measurement period indicated by the measurement request message. According to FIG. 6, for the target model #1, the inference speed is measured at times T#11, T#12, T#13, ···. In FIG. 6, the measured values of the inference speed at times T#11, T#12, and T#13 are 120 ms, 114 ms, and 150 ms, respectively. Similarly, according to FIG. 6, for the target model #2, the inference speed is measured at times T#21, T#22, T#23, ···. In FIG. 6, the measured values of the inference speed at times T#21, T#22, and T#23 are 40 ms, 48 ms, and 50 ms, respectively.
[0021] For example, in the case of measurement method A, the measurement unit 22 measures the inference speed in the inference that the inference control unit 21 executes for each of its control cycles. Also, in the case of measurement method B, the measurement unit 22 measures the inference speed using test data at a predetermined period.
[0022] Furthermore, the measurement result may also include information indicating the reference values of the inference speeds of the target model #1 and the target model #2, respectively. According to FIG. 6, the reference value of the inference speed by the target model #1 is 100 ms, and the reference value of the inference speed by the target model #2 is 50 ms. The reference value of the inference speed may be stored in advance in the control unit 23, for example. Alternatively, the reference value of the inference speed may be dynamically determined by the control unit 23 based on some index, for example. Note that when the learning model is associated with the control content and the reference value of the inference speed determined according to the control content is also set in the first control device 100, it is not necessary to include the reference value of the inference speed in the measurement result.
[0023] In S4, based on the measurement results received in S3, the control unit 13 of the first control device 100 determines whether it is necessary to distribute to the second control device 200 another learning model with a faster inference speed and the same control content for each of the target model #1 and the target model #2. This determination can be made based on a comparison between a predetermined measurement value among one or more measurement values of the inference speed of the target model acquired in S3, or the average value of the one or more measurement values, and the reference value of the inference speed of the target model.
[0024] As an example, the predetermined measurement value among the one or more measurement values is the minimum measurement value among the one or more measurement values. In this case, the control unit 13 may determine that it is necessary to distribute to the second control device 200 another learning model with a faster inference speed and the same control content when the minimum measurement value among the one or more measurement values exceeds the reference value. Also, as another example, the predetermined measurement value among the one or more measurement values is the maximum measurement value among the one or more measurement values. In this case, the control unit 13 may determine that it is necessary to distribute to the second control device 200 another learning model with a faster inference speed and the same control content when the maximum measurement value among the one or more measurement values exceeds the reference value.
[0025] As yet another example, the predetermined measurement value among the one or more measurement values is the measurement value of a predetermined rank in descending order of the one or more measurement values, or the measurement value corresponding to a predetermined percentile value among the one or more measurement values. Also in this case, the control unit 13 may determine that it is necessary to distribute to the second control device 200 another learning model with a faster inference speed and the same control content when the predetermined measurement value exceeds the reference value.
[0026] Furthermore, instead of the predetermined measurement value among the one or more measurement values, a configuration may be adopted in which the control unit 13 determines that it is necessary to distribute to the second control device 200 another learning model with a faster inference speed and the same control content when a value obtained by performing a predetermined calculation on the one or more measurement values, such as the average value of the one or more measurement values, exceeds the reference value.
[0027] For example, according to FIG. 6, the reference value of the inference speed of the target model #2 is 50 ms, and the measured values of the inference speed are 40 ms, 48 ms, 50 ms, ···, and there are measured values below the reference value. For example, when it is determined that it is necessary to distribute another learning model with a faster inference speed to the second control device 200 with the same control content when the minimum measured value exceeds the reference value, the control unit 13 determines that for the target model #2, it is not necessary to distribute another learning model with a faster inference speed to the second control device 200 with the same control content.
[0028] On the other hand, according to FIG. 6, the reference value of the inference speed of the target model #1 is 100 ms, and the measured values of the inference speed are 120 ms, 114 ms, 150 ms, ···, and there are measured values exceeding the reference value. For example, when it is determined that it is necessary to distribute another learning model with a faster inference speed to the second control device 200 with the same control content when the minimum measured value exceeds the reference value, the control unit 13 determines that for the target model #1, it is necessary to distribute another learning model with a faster inference speed to the second control device 200 with the same control content. In this case, the control unit 13 instructs the learning unit 11 to transmit another learning model with a faster inference speed to the second control device 200 with the same control content as the target model #1. The learning unit 11 transmits another learning model to the second control device 200 in S5 based on the instruction.
[0029] In addition, in the learning unit 11, a configuration can be adopted in which a plurality of learning models for a certain control content are generated in advance. The inference speeds of the plurality of learning models for one control content are different from each other. Also, in the learning unit 11, one learning model for a certain control content can be generated in advance, and another learning model with a faster inference speed than the said learning model can be generated by a compression technique, for example, a symmetric static quantization technique. Note that by compressing the learning model by the compression technique, the inference speed of the learning model becomes faster than that of the original learning model, but the inference performance generally deteriorates. Therefore, the control unit 13 can be configured to select, as the learning model to be transmitted to the second control device 200 in S5, for example, the learning model with the highest inference performance within a range where the average value, minimum value, and maximum value of the inference speed do not exceed the reference value.
[0030] With the above configuration, it is possible to suppress a decrease in the inference speed of the learning model used for RAN control.
[0031] <Second Embodiment> Subsequently, regarding the second embodiment, the differences from the first embodiment will be mainly described. The RAN control configuration in this embodiment is as shown in FIG. 2, and the configuration of the first control device 100 is as shown in FIG. 3.
[0032] FIG. 7 is a configuration diagram of the second control device 200 according to this embodiment. The second control device 200 of this embodiment includes a storage unit 26 that stores learning data, and a learning unit 25. The storage unit 26 stores learning data collected from the RAN via the O1 interface or the E2 interface. The learning unit 25 updates the basic learning model received from the first control device 100 via the communication unit 24 by machine learning based on the learning data to generate a learning model. The inference control unit 21 performs inference based on the learning data learned by the learning unit 25 and controls the RAN via the E2 interface.
[0033] In the present embodiment, even when there is no measurement request from the first control device 100, the control unit 23 causes the measurement unit 22 to measure the inference speed at a predetermined timing. Then, based on the measured value of the inference speed and a reference value, for example, compression processing of the learning model is performed so that the inference speed satisfies a predetermined condition. The predetermined condition may be, for example, the same as the condition for determining in S4 of FIG. 5 of the first embodiment that it is not necessary to distribute another learning model with a faster inference speed to the second control device 200 with the same control content. That is, for example, the predetermined condition may be that a predetermined measured value or the average value among one or more measured values of the measured inference speed does not exceed the reference value. Further, the control unit 23 executes the processing of FIG. 5 triggered by receiving a measurement request from the first control device 100.
[0034] Unlike the first embodiment, in the present embodiment, in the learning unit 25, the learning model can be updated using, for example, compression processing so that the inference speed satisfies a predetermined condition. However, depending on the basic learning model first received from the first control device 100, there may be a case where the learning model cannot be updated in the processing in the learning unit 25 so that the inference speed satisfies the predetermined condition. In that case, according to the sequence of FIG. 5, the first control device 100 can determine that the inference speed in the second control device 200 does not satisfy the predetermined condition. When the first control device 100 determines that the inference speed in the second control device 200 does not satisfy the predetermined condition, in S5, it transmits another basic learning model with a faster inference speed to the second control device 200 with the same control content as the learning model that does not satisfy the predetermined condition.
[0035] With the above configuration, it is possible to suppress a decrease in the inference speed due to the learning model used for RAN control. Note that, as in the first embodiment, in this embodiment as well, based on one or more measured values of the inference speed received from the second control device 200, the first control device 100 determines whether it is necessary to transmit another basic learning model with a higher inference speed to the second control device 200. However, when it is determined that the learning model cannot be updated to satisfy a predetermined condition with the basic learning model received by the second control device 200 from the first control device 100, the second control device 200 may be configured to request the first control device 100 for another basic learning model with a higher inference speed.
[0036] Note that the first control device 100 and the second control device 200 can be realized by one device, for example, one computer. Furthermore, the first control device 100 and the second control device 200 can be realized by a plurality of devices capable of communicating with each other, for example, a plurality of computers.
[0037] Furthermore, according to the present disclosure, a program executable by one or more processors is provided. When the program is executed by one or more processors of a device, the program includes instructions for causing the device to function as the first control device 100 or the second control device 200. Furthermore, according to the present disclosure, a non-transitory computer-readable storage medium storing the above program is provided. Furthermore, according to the present disclosure, a method executed by the first control device 100 and a method executed by the second control device 200 for suppressing a decrease in the inference speed due to the learning model used for RAN control, that is, the method shown in FIG. 5 and the like are provided. Furthermore, according to the present disclosure, a program for causing a device having one or more processors to execute the method executed by the first control device 100 and the method executed by the second control device 200, and a non-transitory computer-readable storage medium storing the program are provided.
[0038] The invention is not limited to the above embodiments, and various modifications and changes are possible within the scope of the gist of the invention.
[0039] With the above configuration, it is possible to suppress a decrease in the inference speed by the learning model used for RAN control. Therefore, it becomes possible to contribute to Goal 9 of the Sustainable Development Goals (SDGs) led by the United Nations, "Build resilient infrastructure, promote sustainable industrialization and foster innovation."
Explanation of Signs
[0040] 11: Learning unit, 13: Control unit, 14: Communication unit
Claims
1. A control device for a radio access network (RAN), comprising: a generation means for performing machine learning based on learning data to generate a first learning model; a transmission means for transmitting the first learning model to another control device that controls the RAN using the first learning model; acquiring one or more measured values of the inference speed by the first learning model from the other control device, and based on the one or more measured values and a reference value of the inference speed, determining whether to transmit to the other control device a second learning model that is used for the same control as the first learning model and has a faster inference speed than the first learning model; and when the determination means determines to transmit the second learning model to the other control device, the transmission means transmits the second learning model to the other control device.
2. The control device according to claim 1, wherein the second learning model is generated from the first learning model.
3. The control device according to claim 2, wherein the second learning model is generated by compressing the first learning model.
4. The control device according to claim 1, wherein the determination means determines to transmit the second learning model to the other control device when a predetermined measured value among the one or more measured values exceeds the reference value, or when a value based on the one or more measured values exceeds the reference value.
5. The control device according to claim 1, wherein the determination means instructs the other control device to perform measurements, and acquires the one or more measured values of the inference speed by the first learning model from the other control device as a response to the instruction.
6. The control device according to claim 1, wherein the determination means acquires the reference value from the other control device.
7. The control device according to claim 1, wherein the other control device is a device implementing Near-RT RIC.
8. The control device according to claim 1, wherein the control device is a device implementing Non-RT RIC.
9. A program that, when executed by one or more processors of a device having one or more processors, causes the device to function as the control device according to any one of claims 1 to 8.
10. A control device for a radio access network (RAN), comprising: a first control means for performing inference based on a first learning model received from another control device and controlling the RAN based on the inference result; Measuring means for measuring the inference speed by the first learning model; Second control means for performing control to transmit one or more measured values of the inference speed by the first learning model measured by the measuring means to the other control device; A control device comprising the above.
11. The control device according to claim 10, wherein the second control means transmits the one or more measured values to the other control device in response to a measurement instruction from the other control device.
12. The control device according to claim 10, wherein the second control means transmits a reference value of the inference speed by the first learning model to the other control device together with the one or more measured values.
13. The control device according to claim 10, wherein the other control device is a device implementing a Non-RT RIC.
14. The control device according to claim 10, wherein the control device is a device implementing a Near-RT RIC.
15. A program, which when executed by one or more processors of a device having one or more processors, causes the device to function as the control device according to any one of claims 10 to 14.