Beyond 5G White Paper
Supplementary Volume“AI/ML Technologies”

Version 1.0
March 7, 2024
Beyond 5G Promotion Consortium
White Paper Subcommittee

Preface .............................................................................................................................. 4
AIOps for Autonomous Networks .................................................................................... 5
1.

Introduction .............................................................................................................. 52.

AIOps and Autonomous Networks .......................................................................... 6

3.

Network Failure Management by AIOps ................................................................. 8

4.

Conclusion ............................................................................................................... 9

REFERENCE ................................................................................................................. 10
Logic-Oriented Generative AI Technology for Autonomous Networks ......................... 11
1.

Introduction ............................................................................................................ 112.

Automation of Intent Translation and its Challenges ............................................ 11

3.

Intent Translation with Logic-oriented Generative AI ............................................ 13

4.

Conclusion ............................................................................................................. 15

REFERENCE ................................................................................................................. 15
Scalable AI/ML for Radio Cellular Access ..................................................................... 16
1.

Introduction ............................................................................................................ 162.

Lifecycle Management for AI/ML ........................................................................... 16

3.

Deep Reinforcement Learning for Uplink Power Control ...................................... 18

4.

Conclusion ............................................................................................................. 20

REFERENCE ................................................................................................................. 20
AI/ML-based Radio Propagation Prediction Technology ............................................... 21
1.

Introduction ............................................................................................................ 21

2.

DCNN-based Radio Propagation Prediction Model .............................................. 22

2.1

DCNN Configuration .............................................................................................. 22

2.2

Input Map Data ...................................................................................................... 23

3.

Performance of DCNN-based Model .................................................................... 24

3.1

Measurement Data ................................................................................................ 24

3.2

Evaluation Results ................................................................................................. 24

4.

Conclusion ............................................................................................................. 26

REFERENCE ................................................................................................................. 27
6G Network AI Architecture for Everyone-Centric Customized Services..................... 28

1

1.

Three AI Architectures........................................................................................... 28

1.1

Cloud AI ................................................................................................................. 28

1.2

Edge AI .................................................................................................................. 28

1.3

Network AI ............................................................................................................. 292.

System Model and Simulation Results.................................................................. 30

2.1

System Model ........................................................................................................ 30

2.2

Simulation Results ................................................................................................. 31

3.

Conclusion ............................................................................................................. 32

REFERENCE ................................................................................................................. 33
AI-based Application-aware RAN Optimization ........................................................... 34
1.

Introduction ............................................................................................................ 34

2.

Application-aware RAN Optimization .................................................................... 35

3.

Evaluation .............................................................................................................. 36

4.

Conclusion ............................................................................................................. 37

Acknowledgements ........................................................................................................ 38
REFERENCE ................................................................................................................. 382

【Revision History】
Ver.
Date
1.0 2024.3.7
Initial version

Contents3

NotePreface
The technological evolution of communication networks is rapidly progressing
towards the Beyond 5G era, and artificial intelligence (AI) and machine learning (ML)technologies will play a significant role in this evolution. These technologies will beutilized in various areas to enhance the capabilities of Beyond 5G. Specifically, AI/ML
will be used for automation of network operations and management, analysis of thecharacteristics of communications at each layer, optimization of computing andnetworking resources, and adaptation to meet the individual requirements of diverse
services/applications. When utilizing these AI/ML technologies, in addition to replacing
conventional technologies with learning-based technologies, it is necessary to develop
AI/ML technologies that incorporate the characteristics and knowledge of physical andmathematical models that have been developed over the years.
This white paper introduces leading-edge R&D efforts on AI/ML for Beyond 5G inJapan, with the aim of accelerating R&D to advance future communications andservices. This supplementary volume on AI/ML technologies includes six papers
categorized into three typical categories, as shown below:
AI/ML for Network Operations
1.

AIOps for Autonomous Networks

2.

Logic-Oriented Generative AI Technology for Autonomous Networks

AI/ML for Radio Access Management
3.

Scalable AI/ML for Radio Cellular Access

4.

AI/ML-based Radio Propagation Prediction Technology

AI/ML for User/Application-Centric Communications
5.

6G Network AI Architecture for Everyone-Centric Customized Services

6.

AI-based Application-aware RAN Optimization

This white paper was prepared with the generous support of many people who
participated in the White Paper Subcommittee. The cooperation of telecommunicationsindustry players and academia experts, as well as representatives of various industries
other than the communications industry has also been substantial. Thanks toeveryone’s participation and support, this white paper was able to cover a lot of useful
information for future business creation discussions between the industry, academia,
and government, and for investigating solutions to social issues, not only in thetelecommunications industry, but also across all industries. We hope that this white
paper will help Japan create a better future for society and promote significant global
activities.
Eiji Takahashi, NEC

4

AIOps for Autonomous Networks
Takuya Miyasaka, KDDI Research, Inc.
Minato Sakuraba, KDDI Research, Inc.
Tananun Orawiwattanakul, KDDI Research, Inc.
Atsushi Tagami, KDDI Research, Inc.

Abstract— This report provides an overview of Autonomous Networks expected to berealized in Beyond 5G. Furthermore, this report describes the details of network
operation by AI, which is a necessary element of the Autonomous Network, andespecially summarizes the strategy for managing network failures, and provides theoverall framework required for future network operation.
1.

Introduction
The fundamental role of the mobile network is to provide connectivity for user

equipment (UE). Furthermore, to achieve high-quality mobile service, the network
must meet the quality requirements of UE and the web services with which UE
communicates. In traditional network operations, human operators have played thisrole. Operators install the network equipment, such as base stations and servers, that
constitutes the mobile network, configure them appropriately, and replace them in theevent of failures. These critical tasks enable the mobile network to meet these quality
requirements around the clock.
In recent years, there has been a lot of standardization, research, and development
activities on Autonomous Networks [1,2], where the network autonomously performs
these tasks traditionally performed by human operators. As shown in Fig. 1, in theAutonomous Network, the network's configuration and control are managed
autonomously based on Intent information, which represents the requirements ofactual users of the network.

5Intent
Launch a 4K streaming service in Tokyo

PolicyCreate a new UPF container
with a transfer capacity of 30Mbps
Core

Domain Controller

Business Support System
User

Operation Support System

IntentTransport Network
Create MPLS-TE paths
with 30Mbps transfer capability

PolicyReserve 30Mbps radio resource
In target region’s base station
RAN

IntentPolicyFig. 1 General concept of Autonomous Network
Intent is more abstract information than policy, rules, and logic regarding the
network and represents an intention and expectation of the network's user. In theexample in Fig 1, the operational system, which consists of the Business Support
System (BSS) and the Operation Support System (OSS), receives an Intent from a user
who wants to launch a 4K streaming service in Tokyo, divides the Intent into each
network domain, translates it into an actual network control policy, and requests it toeach network domain. In some cases, Intent may also be sent directly to a domain
controller that controls each network domain without being translated into a policy inthe operational system. Since the domain controller has a more detailed understandingof the operational data of each network domain, a more detailed and accurate policy
translation can be expected. Each domain controller implements control of the relevant
network to ensure the quality specified in the policy.
2.

AIOps and Autonomous Networks
Artificial Intelligence for IT Operations (AIOps) is essential for achieving

Autonomous Networks. As described in the above section, in Autonomous Networks, itis necessary to translate abstract Intent received from users, e.g., “I want to launch a

4K streaming service in Tokyo”, into concrete policies and rules, e.g., “Creating MPLSTE paths with 30~Mbps transfer capability”. Intent allows different users to request
network services without using a technical language that they do not usually use, suchas a programming language. However, user Intent varies widely, making traditional
fixed rule-based translation difficult. Furthermore, it is essential to build the network6

policy translated from the Intent on the network infrastructure (RAN, Transport
Network, and Core) and to deal with network failures without human operators. To
address such issues, AIOps for Autonomous Networks requires three key elements: 1.
Intent translation, 2. Network resource management, and 3. Network failuremanagement.
In 1. Intent translation, users' abstract Intent is translated into a specific network
policy. Generative AI and the Large Language Model (LLM), which have been actively
researched and developed for practical use in recent years, can be applied to thisprocess. Furthermore, the interaction between a user and AI is beneficial not only forunderstanding the user's needs but also for negotiating with the user, for example,
negotiating alternative proposal by AI when network resources are insufficient.
In 2. Network resource management, based on the converted network policy, network
resources are reserved, and the user-requested network service is created and provided
to the user. An optimal resource allocation placement is determined to satisfy the
network policy, and network elements (e.g., virtual mobile core, MPLS-TE path, virtual
CU/DU) that constitute the user's network service are generated on demand. In
addition, network resources are not always prepared enough to always satisfy all user
requests and accommodating them may not be possible. In such cases, admission
control of user requests is necessary, and based on the request status (new requests,
cancellations), decisions must be made to maximize the profit of the network operator,
and automated decision-making, such as Deep Reinforcement Learning, can be applied[3].
Finally, in 3. Network failure management, when a network failure (e.g., HDD
failure, link down, restart) occurs in the created user network service, a series ofprocesses that detect the failure event, identify the root cause, and resolve the issue are
implemented. Various AI technologies, such as anomaly detection and classification,
are being considered and introduced. The next section of this report describes thedetailed technical aspects obtained through our research results.

7

3.

Network Failure Management by AIOps

Training and
Evaluation

Anomaly
Detection

Fault
RecoveryTrain Data / Test Data
Data
Preprocessing

Recovery
Workflow

Datasets
Generator

Data Store

Invoke Fault Event

Data

Data

Production Network

Test Network

Fig. 2 AIOps framework for Network Failure Management
Our proposed integrated framework [4] for network failure management with AIOps
is shown in Fig. 2, including data collection, anomaly detection, and fault recovery
functions. The framework has three phases: the data collection for AI model training,
the AI model training phase for AIOps, and the AI model inference phase from actual
operational data. Firstly, in the data collection phase, the network devices, such asservers and routers, that consist of the operation target network send statistical data.
Typical statistical data include CPU, memory, network, and other resource utilization
rates. Furthermore, user utilization data (e.g., # of sessions) related to mobile network
software such as PGW and UPF is also included. In addition, we have proposed amethod for anomaly detection and prediction based on Observability with Linux eBPF,
which is frequently used in cloud-native environments [5,6]. Since data is essential fortraining highly accurate AI models, more detailed data describing system behavior,such as eBPF, will be required in future mobile networks.
Secondly, in the AI model training phase, AI models are trained from operationaldata, such as CPU utilization rate and trouble tickets, obtained in the target network.
Since network failures are infrequent events in production networks, sufficient
operational data for AI models may not be obtained. Therefore, a test network
simulating the production network can be created to train precise AI models, andoperational data obtained from pseudo network failure generated in the test network
can be utilized as input data for the AI model.

8

Finally, in the AI model inference phase, the trained AI model detects a root cause of
network failure from the latest operational dataset and suggests an optimal recovery
workflow from the network failure, with anomaly detection and fault recovery function.
The anomaly detection function detects network failures and determines their root
causes. Within this framework, we have evaluated a comparative experiment thatinvolved measuring the performance of the fault analysis function using three AI
algorithms, multi-layer perceptron (MLP), random forest (RF), and support vector
machine (SVM), on the testbed network built by the virtualized network functions(VNFs) [7]. RF showed the highest accuracy, and F1 scores for three network failures:compute node down, network interface down, and CPU overload were 1.00, 0.96, and0.95, respectively. This difference in accuracy by AI algorithms is likely due to thedataset generated from the performance management (PM) data, and the increase intraining data, feature reduction, or balance adjustment of normal/abnormal samples
affected the accuracy.
Furthermore, we have proposed a scheme for fault recovery using reinforcement
learning (RL) [8]. The scheme can adapt to network topology and configuration changes
and has a data representation procedure to prepare a data set for RL, which is formed
as a matrix of network topology and fault state. The simulation results showed that
preparing enough training data requires a tremendous amount of failure injection andrecovery operation trials. The test network simulating the production network canpotentially shorten the time for trials in the training process. However, our simulation
also revealed that the behavior between the test network and the production network
infrastructures should be 87% coincident for application to the proposed scheme.
4.

Conclusion
This report described an overview of Autonomous Networks and AIOps. To benefit

from the convenience brought by Autonomous Networks, it is necessary to introduce
the concept of such Autonomous Networks and AIOps as the network architecture for
Beyond5G system. More specifically, it is essential to have architectural support tocreate an end-to-end network instance and control user policies on the network
instance based on user Intent. Furthermore, the Beyond5G system also needs tocentrally manage operational data from the RAN, Core, and Transport Network in an
integrated way and automatically train and deploy the optimal AI model for AIOps.

9

REFERENCE[1] A. Boasman-Patel, et al., “Autonomous Networks: Empowering Digital
Transformation for the Telecoms Industry,” White Paper, TM Forum, May 2019.
[2] 3GPP, “Technical Specification Group Services and System Aspects; Management
and orchestration; Levels of autonomous network,” 3GPP TS 28.100, ver. 17.1.0,
September 2022.
[3] T. Orawiwattanakul, et al. "Reinforcement Learning (RL) Based Admission Control
in Advance Bandwidth Reservation." IEEE/IFIP Network Operations and Management
Symposium (NOMS), 2024 (To be published).
[4] A. Tagami, et al. "Integration of Network and Artificial Intelligence toward the
Beyond 5G/6G Networks." IEICE Transactions on Communications 106.12 (2023):
1267-1274.
[5] J. Kawasaki, et al. "Failure Prediction in Cloud Native 5G Core With eBPF-based
Observability," 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring),
Florence, Italy, 2023, pp. 1-6, doi: 10.1109/VTC2023-Spring57618.2023.10200028.
[6] M. Sakuraba, et al. "An Anomaly Detection Approach by AIML in IP Networks with
eBPF-Based Observability," 2023 24st Asia-Pacific Network Operations andManagement Symposium (APNOMS), Sejong, Korea, Republic of, 2023, pp. 171-176.
[7] J. Kawasaki, et al. "Comparative analysis of network fault classification using
machine learning." NOMS 2020-2020 IEEE/IFIP Network Operations andManagement Symposium. IEEE, 2020.
[8] T. Miyamoto, et al. "Network Topology-Traceable Fault Recovery Framework withReinforcement Learning," Proc. of Advanced Information Networking and Applications,
AINA, pp.393–402, April 2021.

10

Logic-Oriented Generative AI Technology for Autonomous Networks
Takayuki Kuroda, NEC

Abstract—Autonomous network operation technology based on intent has been
attracting attention toward advanced automation of network operation. However, therealization of intent translation, which is the key to this technology, faces the challengeof achieving both flexibility and faithfulness. In this paper, we introduce a method torealize a logic-oriented generative AI, in which a logical search engine is enhanced by
AI/ML technology, in intent translation. The paper presents the position of thistechnique with respect to related techniques, and then briefly outlines the method.
1.

Introduction
Increasingly complex networks are becoming increasingly difficult to provide quickly

and reliably by manual means, and a high degree of automation of operations isrequired [1][2]. Intent-based networking is a promising foundational approach toautomate network operations [3]. Intent is information that expresses requirements inan abstract and declarative manner. According to intent-based automation techniques,a machine interprets the intent and performs the construction and operation of the
network. This allows users to easily build the desired network by simply entering highlevel requirements without having to enter detailed information.
To realize such a technology, the ability to translate intent into concrete network
configurations is essential. Conventional techniques for intent translation are known to
be based on deductive engines [5]. Another possible approach is to use an inductive
inference function, such as LLM, which has recently emerged. However, the former has
the problem of requiring manual addition of conditions and rules to increase flexibility.
The latter has been pointed out to have a problem of faithfulness [6]. Therefore, we
propose a mechanism that combines a deductive engine and an inductive AI so that theengine can search for effective solutions from a large solution space at high speed,
thereby achieving both flexibility and faithfulness. In this paper, we describe thechallenges of existing methods and outline the proposed technique.
2.

Automation of Intent Translation and its Challenges
Intent-based networking is a new technology that offers an abstraction layer andemphasizes on the desired outcome of the network service, instead of prescribing how it
is configured [4]. There are various issues that need to be addressed to realize thistechnology, including the means to appropriately express the various network-related
intent, the means to disambiguate them, and the means to concretize the abstract

11

intent so that they can be deployed in practice. Among these issues, translation is the
key to intent-based networking, which derives concrete network configurations fromabstract intent. Figure 1 shows an overview of intent translation. The intent in thisresearch consists of functional and non-functional requirements for the network and/or
network function to be constructed. The intent translator is based on such intent
information and complements it by concretizing the details necessary for the function
to work.

topology and config.

IntentFunctional req.
Deploy a service API

Intenttranslation

Non-Functional req.
• Throughput• TAT(Turn Around Time)etc..

Fig. 1 Concept of automated network intent translation.
Two typical approaches to realize intent translation can be considered: deductive andinductive. In the deductive approach, the technique described in [5], the intent isrefined step by step by applying predefined patterns, and a reasonable proposal of
network configuration that satisfies the intent is searched among the possible
proposals that can be generated. Flexibility is generally an issue with such a technique.
That is, the solution is limited to specific patterns defined in advance. Although avariety of solutions can be generated by combining the patterns, it is necessary to
manually align the rules to select a reasonable proposal from among them. In contrast,
inductive approaches, such as the Large Language Model (LLM) that has emerged inrecent years, can be utilized. Using LLM, it is expected that some answers can beobtained for any intent. However, LLMs are known to often give wrong answers andare not particularly good at thinking that involves logic, such as network design [6].

12Flexibility
Inductive
approachMixedapproach

Deductive
approachFaithfulness

Fig. 2 Position of this study in comparison with existing studies.
Thus, a deductive approach has faithfulness but lacks flexibility, while an inductive
approach has extremely high flexibility but has problems with faithfulness. In contrast,
our method appropriately combines these two approaches to establish a method that
achieves both sufficient flexibility and faithfulness. The relationship between eachapproach is shown in Fig. 2.
3.

Intent Translation with Logic-oriented Generative AI
The left side of Fig. 3 shows our proposed concept of intent translation with logic-

oriented generative AI. The method is based on a deductive engine, whose search isguided by a GNN-based AI, which we refer to as the design AI. The deductive engine
repeatedly refines the intent in stepwise manner. At each step, the design AI evaluates
the multiple proposals generated and selects the most promising proposal as the next
proposal to be refined. The learning of the design AI can be performed by areinforcement learning algorithm. An overview is shown on the right side of Fig. 3. It
learns the promise of a configuration proposal by generating expected returns based on
the values obtained by evaluating the results of the design trials. As the learning
proceeds, we can observe an increase in the success probability of the trials. The
learning process is terminated when the improvement in the learning successprobability comes to a head.

13

Step-wise refinement
based on logical-engine
guided by design AI

Step-wise refinement

Reinforcement learningfor design AI

Score

Fig. 3 concept of automated intent translation and its learning.
Design AI allows the actual search space to be narrowed down to allow flexible
discovery of promising solutions from a vast potential space. In other words, the rules
for searching for solutions, which were previously defined manually, are replaced bylearning models.

Processing flow

one step of refinement

Topology

TAT

TAT

Fig. 4 Example of one step refinement of network intent and evaluation model.
The validity of the design results of this technique is guaranteed by the constraints.
Figure 4 shows how, together with a network topology, the processing flow to performthe functions of the network is embodied in one step. For example, a Turn Around
Time (TAT) non-functional requirement is evaluated by summing the time for eachprocess based on the processing flow and verifying that it falls within the time specified
as intent.
However, such a verification can only be performed once the design has been fully
concretized. This fact has been a factor prolonging the search time, but the use ofdesign AI can solve this problem. In the search process, it is important to make theright choice in the early stages as much as possible. This is because if a wrong decision14

is made in the initial stage, many trial and errors will have to be made again. In otherwords, the initial decision has a larger search space for later stages. However, as Fig. 4
shows, TAT cannot be accurately determined until the last step. The incomplete
processing flow shown in the upper right corner of Fig. 4 does not include some of theprocesses, and TAT cannot be calculated correctly. In other words, it is difficult toefficiently search a huge search space using logic alone. Instead, the design AI
estimates the final TAT value from an early stage of the design process. This allows thesearch to be properly guided. On the other hand, constraints are essential to validate
the obtained design results and to calculate accurate rewards during training.
4.

Conclusion
In this paper, we introduced a technology to realize intent translation, which is a key

element of intent-based networks. In particular, we described a logic-oriented
generative AI that uses AI/ML technology to enhance the logical search engine in thedesign of network configurations to achieve both flexibility and faithfulness. In the
future, we will continue to refine the technology and make it practical, as well as
develop methods for accelerating learning and automating model development.
REFERENCE[1] TM Forum. Autonomous Networks: Empowering Digital Transformation For Smart
Societies and Industries. TMForum White Paper, 2020.
[2] ETSI, “Intent driven management services for mobile networks”, TS 128 312
V17.0.1 (3GPP TS 28.312 version 17.0.1 Release 17), Jul. 2022.
[3] A. Clemm, L. Ciavaglia, L. Granville, and J. Tantsura, “Intent-Based Networking
Concepts and Definitions”, ITU, Geneva, Switzerland, Feb. 2021.
[4] A. Leivadeas and M. Falkner, "A Survey on Intent-Based Networking," in IEEE
Communications Surveys & Tutorials, vol. 25, no. 1, pp. 625-655, Firstquarter 2023,
doi: 10.1109/COMST.2022.3215919.
[5] N. Nazarzadeoghaz, F. Khendek, and M. Toeroe, “Automated design of network
services from network service requirements”, in Proc. 23rd Conf. Innov. Clouds
Internet Netw. Workshops (ICIN), 2020, pp. 63–70.
[6] Q. Lyu, S. Havaldar, A. Stein, L. Zhang, D. Rao, E. Wong, M. Apidianaki, and C.
Callison-Burch, “Faithful chain-ofthought reasoning”, 2023. arXiv:2301.13379.
https://doi.org/10.48550/arXiv.2301.13379

15

Scalable AI/ML for Radio Cellular AccessAndres Arjona, Nokia
Hideaki Takahashi, Nokia

Abstract— Wireless networks are expected to move towards self-sustaining networks
in 5G-Advanced and in 6G, where Artificial Intelligence (AI) and Machine Learning(ML) play a critical role in maintaining high performance in dynamically changing
environment. AI/ML solutions that operate separately at the device or network side, or
jointly on both will emerge. Similarly, lifecycle management procedures will be needed
to enable interoperable automation in the radio, providing a framework with thenecessary tools for deploying and operating ML solutions in radio at scale.
1.

Introduction
We are at the beginning of a revolution in cellular networks as Artificial Intelligence(AI) and Machine Learning (ML) for the air interface become integral to cellular
networks. Although AI/ML is already part of 5G systems, it is currently mostly applied
to network automation and proprietary Self Organizing Networks (SON) solutions.
With the advent of 5G-Advanced, and further with 6G, we will see an advanced
implementation of AI/ML in the RAN and radio interface. The potential benefits of
AI/ML in the network will be significant. They will boost the performance of the radio
interface, reduce power consumption, greatly improve the end user experience, andhelp find better performing network parametrization faster. Further, these solutions
must be both economically and technically feasible to scale.
In this paper, we present discussion on the importance of standardizing lifecycle
management procedures relevant to AI/ML, followed by an example of an AI/ML based
reinforcement learning solution for uplink power control in cellular networks.
2.

Lifecycle Management for AI/ML
AI/ML solutions for the air interface [3] can be one-sided, where a given feature

operates at either the network or device side (e.g., beam prediction, positioning), or
two-sided, where the solution operates jointly in both simultaneously (e.g., device
channel feedback compression). In this latter example, the ML algorithm is applied atboth the device and network side for compression and decompression of the channel
state information.

16

Fig. 1 One-sided and two-sided AI/ML solutions in the mobile network air interface [3]Standardization efforts are essential to ensure that different vendors’ ML
implementations and algorithms for networks and devices can work together in avariety of scenarios. Thus, a holistic framework needs to be developed in 3GPP for 5GAdvanced, addressing both kinds of AI/ML solutions (one-sided and two-sided)
supporting control-plane signaling between the network and the device for correct andcontrollable operation. This framework shall be applicable to any use case in the airinterface, and also be the foundation for the AI-native air interface in 6G.
Specifically, Lifecycle Management (LCM) procedures to enable interoperable
automation mechanisms in the radio are needed. Including procedures for datacollection, development and testing, deployment, and operation and monitoring of ML
solutions. This framework will provide operators, devices, and network vendors therequired tools for operating ML solutions for radio at scale with guaranteedinteroperability.
Data needs to be collected for training, inference and performance monitoring of the
ML solutions. Hence, the framework must ensure that operators have control about
how, what, when, and for which use cases data is collected responsibly, and incompliance with local data and privacy regulations. However, a challenge for the ML
training data, is regarding scalability and access to the data needed in a controlled andefficient manner. To this end, the following principles should be followed for training
data collection procedures:•

Ensure user security and privacy•

Make data accessible by the subscribed parties•

Operator needs to be aware of and control data collection•

Minimize additional air-interface traffic•

Design for extensibility and future evolution

17

3.

Deep Reinforcement Learning for Uplink Power Control
One important trend in ongoing 6G research is the paradigm shift toward self-sustainable networks. To this purpose AI and ML technologies can become key
components in maintaining network performance.
Reinforcement Learning (RL) is one field in machine learning for decision making
that can be applied to cellular networks. Use of RL methods can enable use cases inwireless communications and radio resource management which are otherwise difficult
due to the complex nature of the radio environment. In RL, the objective is to have anagent have freedom to learn a solution, where learning of the decisions is carried out
via an arbitrary function that maximizes a “reward”. Throughout this process theagent learns from the reward feedback signal, which reinforces the desired actions andpenalizes the undesired ones. The agent interacts with the environment by taking anaction based on the observed environment state.
The research work in [1], shows RL applied to uplink power control. Outer-Loop
Power Control (OLPC) in 5G networks relies on tuning two primary parameters, thenormalized transmit power density P0, and the path-loss (PL) compensation factor αpl.
Optimization of these parameters is known to be of great importance to reach highuplink performance. One approach is to optimize uplink power control via an RL agent
for each cell, controlling both P0, and αpl parameters within a single neural network
rather than focusing on P0 alone. However, mitigation actions are needed to cope withbehavior resulting from multi-agent RL, such as high-power consumption from
uncoordinated competition among gNBs in the network trying to maximize their own
performance. To mitigate these issues, cooperative time synchronized reward
mechanisms and sharing of state information between nearby RL agents can be
implemented. Hence, achieving a common goal across multiple gNBs.
The solution in [1] is based on Double Deep Q Network (DDQN), where soft updates
take place at every training occasion. In this solution, the neural network’s output
layer is divided in two dimensions, one dedicated for P0, and the other for αpl indices(See Fig. 2). 3GPP defines 114 values for P0 and 8 values for αpl resulting in 912
possible combinations.

18

Input (one value per cell)

1

...

2

1
1

2

3

NP0

2

4

...
...

NP0
+Nα -1

NP0
+1

50

NP0
+Nα

...

NC -1

NC

51

2nd output dimension (α values)

1st output dimension (P 0 values)

Fig. 2 Multi-Action Neural Network with Two Output Dimensions [1]OLPC parametrization is the problem of finding a balance between the signal tointerference plus noise ratio (SINR) and the number of resource blocks needed pertransmission. If the gNB agents are only aware of their own parametrization andperformance, such uncoordinated approach leads to a competition where the agentsincrease their transmission power to compensate the interference created by theirneighboring agents. Hence, the agents should be provided with information that allows
learning of power settings between gNBs, and that state information is shared between
neighbors at each training step. Likewise, the reward is the sum throughput perutilized resource blocks over the closest neighbors including the agent’s own cell.
The simulation result in [1] (See Fig. 3) shows that maximizing the neighborhood
reward alone may result in unfair user and cell throughput, as power allocations canbecome widespread. Thus, an alternative is to carry out averaging of the ML-suggested
actions, which yields a fairer and more uniform power allocation between cells.
Similarly, co-operation is shown to be essential in multi-agent power control, as the cooperation range affects significantly the results. If the co-operation range is too high, it
leads to noisy rewards which impairs learning, while without co-operation gains
collapse and DDQN is unable to learn the full effects of its actions.
Additionally, when evaluated with the exhaustively searched best configurationcommon across all simulation realizations (referred as golden baseline), simulation
results show that it is possible to achieve ~10% gain in cell throughputs in average,
with the gain being rather fairly distributed over all UEs within the simulation,
showing further benefit over traditional parametrization approaches.

19

Fig. 3 Simulation results: (left) Average Cell Throughout Gain, where N=2 refers tolearning both P0 and αPL output dimensions; (right) Windowed User Throughput
Distribution of with different offered loads [1]4.

Conclusion
AI/ML-based solutions have the potential to further extend the boundaries of

performance of the air interface. However, to deploy AI/ML solutions at scale,standardization of lifecycle management framework is needed. Hence, paving the way
with work in 5G-Advanced for AI-native 6G, where AI/ML is considered from the start
as a key design principle of the system.
Similarly, 6G development must specify enablers for more dynamic reconfigurationof system information parameters. Likewise, more dynamic power control as well asother machine learning applications, such reinforcement learning, bring performance
beyond that of common parameters set over the network. Further, it could be expectedthat such machine learning algorithms will turn to be essential parts of 6G making theparadigm shift towards self-sustained networks, where multiple dependent parameters
and inter-connected features must be tuned simultaneously on the fly.
REFERENCE[1] P.Kela, and T.Veijalainen, “Cooperative Action Branching Deep Reinforcement

Learning for Uplink Power Control”, in 2023 Joint European Conference on Networks
and Communications & 6G Summit (EuCNC/6G Summit), 2023.
[2] A.Maeder, “AI/ML unleashes the full potential of 5G-Advanced”–(Nokia)
https://www.nokia.com/blog/aiml-unleashes-the-full-potential-of-5g-advanced[3] A.Maeder, and I.Kovacs, “Scaling up AI/ML for cellular radio access”–(Nokia)
Scaling AI/ML for cellular radio access in 5G-Advanced (nokia.com)

20

AI/ML-based Radio Propagation Prediction Technology
Tetsuro Imai, Tokyo Denki University
Koshiro Kitao, NTT DOCOMO. INC.
Satoshi Suyama, NTT DOCOMO. INC.

Abstract—Recently, advancement of AI/ML has been remarkable, and many appliedresearch studies are attracting attention now. This is also true in the field of radio
propagation. This paper introduces its application to radio propagation prediction,
which is currently under intensive study.
1.

Introduction
In recent years, artificial intelligence (AI) / machine learning (ML) has maderemarkable progress, and many applied research studies have been reported. Here,they are mainly based on deep learning. The deep learning is one of the methods of ML
for neural networks with many layers (or DNN: deep neural network). Deep learning
has succeeded the dramatic performance improvement of image recognition, natural
language processing etc., while utilizing of abundant computer resources and big data.
The main reason for its success is that the deep learning can automatically extract
features of contents.
In mobile communications, accurate prediction of radio propagation characteristics isneeded for optimum cell design, various prediction models have been proposed so far[1]. These are categorized into two types. One is physical-based model which is basedon electromagnetic theory, and another is statistical (or data-driven) model which isbased on measurement data. Here, ray tracing (RT) is one of the physical-based models
and has become popular tool for radio propagation analysis in recent years. In RT,
various propagation characteristics such as loss, time of arrival, angle of arrival and so
on can be predicted by tracing rays between transmitter (Tx) to receiver (Rx) while
taking interaction (reflection, diffraction, transmission) into account. However,increasing the number of interactions considered to improve the prediction accuracy
increases the computation time. So, when the target characteristic is only propagation
loss, the statistical model, e.g. Okumura-Hata model [2] is preferred.
In statistical modeling, multi-regression analysis has been applied to model the data[3]. The multi-regression analysis is a very powerful tool, but it is needed to manually
determine input parameters (especially environmental parameters related to building,street, etc.) and functional form beforehand. This is very difficult because there are a
lot of candidates. So, the prediction models with neural network (NN) have been
proposed in [4], [5]. By using these models, functional form is automatically generated,21

and it is reported that prediction accuracy for propagation loss is improved. However,
the models are based on conventional fully connected neural network (FNN), optimal
input parameters must be investigated, manually.
As mentioned above, the deep learning can automatically extract features ofcontents. Especially, deep convolutional neural network (DCNN) are very useful to
extract features from image. This means that optimal parameters for propagation loss
prediction can be automatically obtained from map data with information such asbuilding spatial distribution. So, DCNN-based model has been proposed for
propagation loss prediction [6] and is currently being vigorously studied [7]-[12]. This
paper presents our latest results in [12].
2.

DCNN-based Radio Propagation Prediction Model

2.1

DCNN Configuration

DCNN of our proposed model is constructed by two parts: feature extraction part andprediction part, as show in Fig. 1.
The feature extraction part is for extraction of features of contents as key parameters
for propagation loss prediction, and it is constructed by DCNN which has 13
convolutional layers: Conv_1 – Conv_13, and five max. pooling layers: Pool_1 – Pool_5.
First, three maps (the size of each map: 256-by-256) are input. In Conv_1&2 layers,
convolutional processing with 32 filters (the size of each filter: 3-by-3) is done and thenthe 32 maps (the size of each map: 256-by-256) are obtained. In next Pool_1 layer, max.
pooling processing is done for 32 maps. Here, pooling size is 2-by-2, so the size of output
map is reduced to 128-by-128. After the similar convolutional and pooling processing
are repeated, 256 maps (the size of each map: 8-by-8) are output from Pool_5 layer.
Here, the number of samples is 16384 (=8×8×256) and these are input to Dense_1 layer
after conversion process to 1 D data in Flatten_1. The prediction part is constructed by
FNN with two fully connected layers: Dense_1 and Dense_2. After the processing inDense_1&2, propagation loss is predicted as output. Note that activation function isdefined as: 𝑓(𝑥) = 𝑥 in Dense_2 layer; otherwise, Rectified Linear Unit function, i.e.
𝑓(𝑥) = max(0, 𝑥).

Fig. 1 DCNN configuration

22

2.2

Input Map Data

In our model, the spatial information of rectangular area centered on mobile station(MS) position is input to DCNN as map data. The size of rectangular is 256 m -by- 256
m, and the area is sampled with 1 m mesh, so, the sample size is 256-by-256. In
addition, the rectangular is defined so that the base station (BS) always exist in acertain direction. Specifically, as shown in Fig. 2, the rectangular region is defined sothat BS is oriented positively on the xm axis in the local coordinates of the map withMS as the origin. By this definition, the spatial information about “BS direction” areindirectly consideredfor DCNN learning, even if the BS position are not directly

input to the DCNN as parameter.

Fig. 2. Definition of rectangular region
Input maps are three as follows.
⚫

BS distance map: Map with distance from BS to each mesh as an element.

⚫

MS distance map: Map with distance from MS to each mesh as an element.

⚫

Building map: Map with building height information in each mesh.

In the building map, the height is normalized by the height of Fresnel-zone center
when assuming one time scattering. This advantage is that BS antenna height and MS
antenna height are indirectly considered as input parameters. Figure 3 shows theexamples of input map data.

(a) BS distance map(b) MS distance map(c) Building map

Fig. 3. Examples of input map data

23

3.

Performance of DCNN-based Model

3.1

Measurement Data

Propagation loss data measured in Kokura area are used for performance evaluation.
Here, the data can be obtained for free from AP propagation database [13]. Figure 4
and Table 1 show the measurement area and conditions, respectively.

Fig. 4 Measurement area (Kyushu Kokura area, Japan): White lines represent
measurement courses.
Table 1 Measurement conditions

In this paper, the data of 5 courses (#6, #19, #24, #27, #32) are used for validation,
the remaining data of 29 courses are for DCNN training. Here, data of course #5 is not
used because sufficient input map data could be obtained. The total number of samples(or MS points) is 81 for validation and 713 for training.
3.2

Evaluation Results

Figure 5 shows the prediction results for validation data. Horizontal axis represents
distance from BS and vertical axis represents propagation loss. We find thatmeasurement and prediction are agree well. Here, RMS error is 3.23 dB.

24

Fig. 5 Prediction results.
The extracted features after training DCNN can be visualized by using Grad-CAM(Gradient-weighted Class Activation Mapping) [14], which one of XAI (Explainable AI)
algorithms. Therefore, Grad-CAM were performed for three points as shown in Fig. 6.
Figure 7 shows the analysis results with Grad-CAM. In Fig. 7, the larger the gradientvalue, the higher the contribution for the propagation loss prediction. From the results,
DCNN-based model is thought to use the "distribution of low-rise buildings and spaces
without buildings" in the vicinity of MS as the basis for determining the propagation
loss prediction.

100m

BS

100

60

40

B

A

C

Height [m]

80

20
0

(a) Positional relationship with BSABC(b) Maps in local coordinate system
Fig. 6 Reception points for evaluation of extracted features from map data.

25

(a) Point A(b) Point B

Gradient

1.0
0.8
0.6
0.4
0.2
0.0
(c) Point C

Fig. 7 Analysis results with Grad-CAM when using multiple maps.
Finally, Fig. 8 shows propagation loss distribution predicted by trained DCNN when
BS are installed in different location. Note that the other propagation conditions aresame as that in table I. From this figure, we can see that even if the distance from the
BS is the same, the propagation loss increases in areas with dense buildings.

Fig. 8 Propagation loss distribution predicted by trained DCNN.
4.

Conclusion
In this paper, we introduced DCNN-based model for radio propagation loss

prediction. This model predicts the propagation loss from map data with informationsuch as building spatial distribution and its prediction accuracy is higher thanconventional model based on multi-regression analysis. In our study, RMS error of
about 3 dB is obtained. And also, we showed that the basis for determining theprediction in the DCNN-based model can be confirmed by Grad-CAM.

26

REFERENCE[1] T. K. Sarkar, Z. Ji, K. Kim, A. Medour, and M. Salazar-Palma, “A Survey of Various
Propagation Models for Mobile Communication,” IEEE AP Magazine, Vol. 45, No. 3,
pp, 51-82, June 2003.
[2] M. Hata, “Empirical formula for propagation loss in land mobile radio services,”
IEEE Trans. VT, vol. 29, no. 3, pp. 317-325, Aug. 1980.
[3] K. Kitao, and S. Ichitsubo, “Path loss prediction formula in urban area for the
fourth-generation mobile communication systems,” IEICE Trans. Commun., vol. E91B, no. 6, pp. 1999-2009, June 2008.
[4] E. Östlin, H. Zepernick, H. Suzuki, “Macrocell Path-Loss Prediction Using Artificial
Neural Networks,” IEEE Trans. VT, vol. 59, no. 6, pp. 2735-2747, July 2010.
[5] M. Ayadi, A. Ben Zineb, and S. Tabbane, “A UHF Path Loss Model Using Learning
Machine for Heterogeneous Networks,” IEEE Trans. AP, vol. 65, no. 7, pp. 3675-3683,
July 2017.
[6] T. Imai, K. Kitao, and M. Inomata, "Radio Propagation Prediction Model Using
Convolutional Neural Networks by Deep Learning," EuCAP2019, April 2019.
[7] T. Hayashi, T. Nagao, and S. Ito, “A study on the variety and size of input data forradio propagation prediction using a deep neural network,” EuCAP2020, March 2020.
[8] N. Kuno, W. Yamada, M. Inomata, M. Sasaki, Y. Asai, and Y. Takatori, “Evaluationof Characteristics for NN and CNN in Path Loss Prediction,” ISAP2020, Jan. 2021.
[9] X. Zhang, X. Shu, B. Zhang, J. Ren, L. Zhou, and X. Chen, "Cellular Network Radio
Propagation Modeling with Deep Convolutional Neural Networks," in Proc. 26th ACM
SIGKDD Int. Conf. Knowl. Discovery Data Mining, pp. 2378-2386, Aug. 2020.
[10] T. Nagao, T. Hayashi, “A Study on Urban Structure Map Extraction for Radio
Propagation Prediction using XGBoost,” EuCAP2021, March 2021.
[11] K, Inoue, K. Ichige, T. Nagao, and T. Hayashi, “Learning-Based Prediction Method
for Radio Wave Propagation Using Images of Building Maps,” IEEE AWPL, vol. 21, no.
1, pp. 124-128, Jan. 2022.
[12] K. Kozera, T. Imai, K. Kitao, and S. Suyama, “Performance Evaluation of DCNNBased Model for Radio Propagation Loss Prediction - Analysis on Prediction
Mechanism with Grad-CAM -,” IEICE Trans. Commun., vol. J106-B, no.9, pp. 618-627,
Sep. 2023.
[13] AP Propagation Database: Online data repository created and supported byTechnical committee on Antennas and Propagation, IEICE.
https://www.ieice.org/cs/ap/language/en/misc-eng/denpan-db/[14] R. R. Selvaraju, et al.,” Grad-CAM: Visual Explanations from Deep Networks via
Gradient-based Localization,” International Journal of Computer Vision, Dec. 2019.

27

6G Network AI Architecture for Everyone-Centric Customized Services
Huang Huanhuan, Huawei Technologies
Peng Chenghui, Huawei Technologies
Yang Yang, The Hong Kong University of Science and Technology
Koshimizu Takashi, Huawei Technologies Japan

Abstract— This article proposes a network Artificial Intelligence (AI) architecturewith integrated network resources and pervasive AI capabilities for supporting
customized services with guaranteed QoEs (Quality of Experiences). Extensive
simulations show that the proposed network AI architecture can consistently offer ahigher User Satisfaction Ratio (USR) performance than the cloud AI and edge AI
architectures with respect to different task scheduling algorithms, random service
requirements, and dynamic network conditions.
1.

Three AI Architectures

1.1

Cloud AIIn the era of 5G, the cloud AI architecture has been widely adopted to provide
centralized computing services. The conventional “cloud-pipe-terminal” structure
decouples the data sensing functions at user terminals, the communication functions in
mobile networks (a.k.a. the pipe), and the computing functions or the AI-enabled
analytical services on the cloud [1]. This is simply a combination of the existing
infrastructures of Data Technology (DT), Communication Technology (CT), andInformation Technology (IT). It is very challenging to coordinate these separate
functions in multiple facilities for effectively providing an agile, smooth, and stable
service with guaranteed QoE.
1.2

Edge AIIn order to solve the problem of low speed, long delay, poor privacy, and high carbon
emissions in centralized AI applications on the cloud, the edge AI architecture extends
the computing capability from the cloud to the locations physically closer to end users.
The edge AI architecture is much more effective in supporting computing-intensive,delay-constrained, security-assured, and privacy-sensitive applications, such as
interactive Virtual Reality/Augmented Reality (VR/AR) games, but the costs for
deploying edge clouds (also called cloudlets) widely in the neighborhood are usually
very high.
As shown in Fig. 2-1 (a), central, local, and edge clouds are connected by high-speed,
expensive bearer networks, which are just the traffic pipes with huge bandwidth. Local

28

and edge clouds are deployed as affiliated Over-The-Top (OTT) services to support
computing-intensive applications. They are usually co-located with the existing
network elements, but not embedded in mobile networks. Thus, cross-domain resource
coordination and service orchestration between these local/edge clouds and end users
require round-trip data transmissions through the mobile network. The actual service
procedure is very complicated, time-consuming, and expensive, and may generate aseries of management and technical problems such as redundant deployment costs,
circuitous data paths, and frequent desynchronized cooperation. It is very difficult forthe cloud AI and edge AI architectures to guarantee E2E (End to End) QoE forsophisticated cross-domain services in dynamic application scenarios and mobile
environments.

Fig. 1 Three AI Architectures and the System Model.
1.3

Network AI

To address those challenging problems, this article proposes the network AI
architecture with multi-tier multi-function Nodes (mNodes) to integrate and coordinate
cross-domain Sensing, Storage, Communication, Computing, Control, and AI (S2C3A)
resources for processing local/regional user data, executing distributed AI algorithms,
and providing customized services for everyone as closely as possible. This architecture
shifts the classic design paradigm that assumes mobile networks only as the pipe fordata transmissions. Based on the hierarchy of mNodes, heterogeneous network
resources and separate functions are effectively integrated to support cross-domain,
wide-area, and delay-sensitive applications.
In Fig. 1 (a), the proposed network AI architecture consists of three key units andconstructs a comprehensive, distributed, and scalable AI as a Service (AIaaS) platform
in 6G. First, the network infrastructure is composed of dispersive mNodes in multi-tier29

mobile networks. An mNode not only coordinates local resources as a Service Provider
does for E2E service auction [2], but also integrate the basic S2C3A resources andmultiple functions to support QoE-guaranteed, everyone-centric customized services.
Besides general-purpose computing units, it is envisaged that more and more AI
processors will be widely integrated and shared by the mNodes to provide the 6G
native AI service platform. Second, each Network AI Logic and Control (NALC) unit istask-oriented and manages the multi-tier mNodes in a specific local/regional area
through effective signaling schemes. In 6G mobile networks, a NALC coordinates theintegrated S2C3A resources and functions for serving every task in real-time and nearreal-time applications, i.e., E2E delay ranges from milliseconds to tens of milliseconds.
The customized service procedure and personal QoE of every task are constantly
monitored and optimized by a corresponding NALC. Third, a Network AI Management
and Orchestration (NAMO) unit manages the AIaaS platform with multiple NALCs tosupport wide-area applications by cross-domain resource coordination, service
orchestration, and E2E QoE guaranteeing protocols. The proposed network AI
architecture can either serve various tasks independently, or complement with thecloud AI and edge AI architectures to satisfy sophisticated user requirements withchallenging SRZ targets.
2.

System Model and Simulation Results

2.1

System Model

To study a typical 6G system with dispersive computing resources and pervasive
intelligence, Fig. 2-1 (b) shows a general system model for different AI architectures.
Let us consider a series of tasks, each having a customized Service Requirement Zone
(SRZ), arriving at the system with a predefined rate. These tasks are generated
randomly either by end users enjoying mobile internet services or by various devices
and things embedded in industrial IoT applications. We consider a three-tier network
AI architecture with three types of mNodes, which are represented by blue rectangularboxes. Above them sits a cloud, which has the highest data rate and the strongest
computing power. This system model can be easily simplified to represent the cloud AI
and edge AI architectures by setting the number of mNodes to zero from the first andsecond tier, respectively.
For an arbitrary task, the corresponding service provisioning procedure isdetermined by the specific task scheduling algorithm. Upon the arrival of task, its SRZ
is first checked by a nearby 1𝑠𝑡 -tier mNode at the edge, which analyzes the possibilityof satisfying that SRZ with the network resources available in the vicinity. If local
resources are sufficient, the task will be immediately served by this mNode. If not, amore powerful 2𝑛𝑑 -tier mNode will be initiated to lead the effort of identifying feasible

30

network resources in a bigger neighborhood. If regional resources are still not
sufficient, an even stronger 3𝑟𝑑 -tier mNode will be called upon to perform multi-domain
resource coordination over a much wider area.
2.2

Simulation Results

The simulation study is conducted in this article to compare the performance of three
AI architectures, where two task scheduling algorithms are considered in performance
evaluation. The Fair Equal Scheduling (FES) algorithm assigns all the tasks in arandom manner, with half going to the edge and half to the cloud for services. TheCloser-The-Better (TCTB) algorithm follows the Pareto principle, or the 80/20 rule, sothat 80% and 20% of all the tasks go to the edge and the cloud, respectively.

Fig. 2 USR versus Task Density and Computing Requirement.
Fig. 2 illustrates the User Satisfaction Ratio (USR) performance of the three AI
architectures under dynamic task densities and computing requirements, where the
USR is calculated as the ratio between the number of satisfied tasks and the total
number of served tasks. In Fig. 2 (a), the task density has a linear impact on the
decline of the USR curves under different AI architectures. And the network AI
architecture achieves higher USR than the edge AI architecture, and much higher USR
than the cloud AI architecture. In Fig. 2 (b), the USR curve of the cloud AI architecture
has two knee points at about task computing requirement U=48 teraFLOPS and U=66
teraFLOPS. The transition region between them has a steep slope, which implies thatthe energy consumptions for executing all the tasks in the cloud increase very rapidly
when the average computing requirement increases. Under both TCTB and FES

31

algorithms, the green and blue curves of the edge AI and network AI architectures aremuch less sensitive to this change, which is due to the efficient services by mNodes in
the neighborhood.
In Fig. 3 (a), when task size increases, the USR curve of the cloud AI architecture
degrades dramatically because long-distance transmissions of bigger tasks become
more time-consuming and energy-intensive, thus adversely impacting the USR. On thecontrary, the USR curves of the edge AI and network AI architectures are much lesssensitive to task size changes, thanks to the computing resources deployed at the edge
and in the network. Fig. 3 (b) demonstrates the influence of network data rates on the
USR performance. These curves are like the mirror flips of those in Fig. 3 (a), because
higher network data rates and smaller task sizes both imply lower transmission
delays. Therefore, increasing network data rates and reducing task size have almost
equivalent impact on the USR performance.

Fig. 3 USR versus Task Size and Network Data Rate.
3.

Conclusion
The cloud, edge, and network AI architectures were studied and compared in thisarticle under dynamic task densities, task sizes, computing requirements, network
data rates, and two task scheduling algorithms. By deploying multi-tier mNodes, theproposed network AI architecture with integrated S2C3A resources can effectively
support customized services for a variety of user tasks, thus achieving the highest USR
under random service requirements and dynamic network conditions. In contrast, thecentralized cloud AI architecture has difficulties in meeting stringent delay and energy
consumption bounds, thus not suitable for delay-sensitive broadband applications.

32

REFERENCE[1] N. Chen, Y. Yang, T. Zhang, M. T. Zhou, X. L. Luo, and J. Zao,“Fog as a Service
Technology,” IEEE Communications Magazine, Vol. 56, No. 11, pp. 95-101, Nov. 2018.
[2] X. Chen, Y. Deng, G. Zhu, D. Wang, and Y. Fang, “From Resource Auction toService Auction: An Auction Paradigm Shift in Wireless Networks,” IEEE WirelessCommunications, early access, May 2022.

33

AI-based Application-aware RAN Optimization
Eiji Takahashi, NEC
Takeo Onishi, NEC
Yoshiaki Nishikawa, NEC

Abstract— It has become increasingly important for industries to promote digitaltransformation (DX) by utilizing Beyond 5G, Industrial Internet of Things (IIoT), andArtificial Intelligence (AI) to realize a highly productive and prosperous society. In
addition to conventional policies of improving the average quality of experience (QoE)
at each mobile coverage area, there is an increasing need to strengthen policies thatprecisely adhere to quality of service (QoS) requirements per communication session
and in real-time to enable the stable use of applications at high-performance levels,
e.g., work speed or productivity. This article introduces an AI-based application-aware
radio access network (RAN) optimization technology that can support such policies
based on Open RAN architecture.
1.

Introduction
Because of the labor shortage and the decrease in skilled workers due to thedeclining birthrate and aging population, there is an increasing need to replace
humans with machines in several tasks to solve social issues. Accordingly, there is aneed for automation, remote monitoring/control, and labor-saving by promoting digitaltransformation through the utilization of Beyond 5G, IIoT, and AI to realize a highly
productive and prosperous society [1]. In the IIoT area, there are many use cases that
require the mobility and ease of equipment installation, and where wirelesscommunication is essential. And high IIoT application performance (working speed,
productivity, etc.) must be ensured, often resulting in stricter requirements for QoS.
Thus, in addition to the policy to improve the average QoE of users at each area,
there is a need to strengthen policies that precisely protect IIoT application
performance (work speed, productivity, etc.) per communication session. For thispurpose, it is necessary to intelligently and autonomously control the RAN according to
the conditions of application, network, and site, so that IIoT applications can be usedstably at high performance. This article introduces an AI-based application-aware RAN
optimization technology that can support such policies based on the Open RAN
architecture [2], [3].

34

2.

Application-aware RAN Optimization
Here, the industrial use case to ensure the uninterrupted transport of materials at

the factory or warehouse floor by means of Automated Guided Vehicles (AGVs) is
considered. Communication services for those remote-control applications need to fulfill
stringent requirements, especially in terms of latency, communication service
availability and determinism. In those applications, two-way communication consistingof robot status monitoring and control instructions must be completed at a constantcycle, and the system safely stopped with fail-safe if the latency exceeds a threshold.
Safety is maintained by fail-safes, but if fail-safes occur frequently, the facility
utilization rate and productivity will decrease.
The application-aware RAN optimization method consists of AI that analyzes
communication requirements and radio quality fluctuations on a per-user terminal
basis, such as robots and vehicles, and AI that dynamically controls RAN parameters
on a per-user terminal basis based on the results of that analysis. This AI learns frompast operational records of robots and vehicles, and optimally controls RAN parameterssuch as modulation and coding scheme (target block error rate), radio resource
allocation (resource block ratio), and maximum allowable delay (delay budget) while
predicting the probability of exceeding communication latency requirements.
In a typical 5G network, RAN parameters are fixed and set for the entire network.
However, this technology dynamically controls them on a per-user terminal basis toimprove application productivity. Based on the architecture shown in Fig. 1, theproposed method can perform the following tasks on a per user equipment (UE) basis
in near-real-time: 1) estimating application QoS requirements based on information
supplied by the application server, 2) predicting fluctuations in wireless quality byusing radio quality information supplied by the central unit (CU) and distributed unit
(DU), and 3) proactively optimizing CU/DU parameters [4]. While running machinelearning, the system verifies that the accuracy is not compromised. If a risk is detected,
it switches to a stable logic-based engine. This technology ensures the stability of RAN
control by switching engines.

35

Fig. 1 Architecture3.

Evaluation
By using mobility, radio propagation, and network simulators, the timeliness andavailability of communication services are evaluated in the context of mean time
between failures, which is one of the important key performance indicators (KPIs)
defined by 3GPP for this type of traffic. The proposed method optimizes RAN
parameters, including modulation and coding scheme (target block error rate), radio
resource allocation (resource block ratio), and maximum allowable delay (delay
budget), on a per-UE basis in near real-time. The proposed method is compared withthe method of using typical settings for these RAN parameters in conventional
methods. Table 1 showcases the simulation conditions, while Fig. 2 illustrates thesimulation results in an environment where the QoS requirements and radio quality
vary based on field conditions (e.g., movement of UEs, distance between UEs, etc.).
Table 1 Simulation Conditions
Number of gNB and cell
gNB: 1cell: 1Frequency
4.8GHz (n79)Bandwidth
100MHzSCS
30kHz
DuplexTDD
DL, UL ratio
DL:UL = 1:1
Transmission power
23dBm
Floor area
100m x 100m
Floor layout
layout assuming a factory
Number of robots
18 in maximum
Robot running speed
3 m/s in maximum
Traffic per robot
DL: 150Kbps in maximum
UL: 1 Mbps in maximum

36

100.0Relative frequency of
QoS requirement unsatisfaction
per packet

100.0
80.0
60.0

40.0
20.0
1.7
0.0
Proposed Method

Conventional Method

Fig. 2 Simulation Results
The simulation results of implementing this technology to a system that remotely
controls multiple autonomous robots operating in factories or warehouses haveconfirmed that the proposed method reliably satisfies the QoS requirements, incomparison to the conventional method. In other words, the number of robot stoppages
can be reduced by 98% or more compared to cases where this technology is not used.
4.

Conclusion
Mobile network specifications will become more sophisticated toward the Beyond 5Gera, however, intelligent network optimization during operation will be crucial in
responding to the changing conditions of applications, networks, and sites. To
guarantee strict QoS requirements in the vertical domain and to support
diversification of application requirements and wireless quality variation, an
application-aware RAN optimization technology was proposed. The simulation resultsof applying this technology to a system that remotely controls multiple autonomous
robots operating in factories or warehouses confirmed that the number of robot
stoppages can be reduced by 98% or more compared to cases where this technology is
not used.
Conventionally, IIoT devices are equipped with intelligent functions that are specific
to its vendor or model, and the IIoT controller software is also tied to a specific vendor
and model. When Beyond 5G realizes a reliable wireless communication environment
with low latency, intelligent and high-load data processing will be possible on the cloudor edge server. This makes it easier for the IIoT controller installed in the cloud or edge
server to coordinately control IIoT devices of multiple vendors, and for multiple models
to optimize the entire system. Furthermore, achieving simplification, lightweight
implementation, and generalization of IIoT devices will likely drive the spread of IIoT

37

solutions, and as a result, accelerate the developments in IIoT applications and AI
systems.
Acknowledgements
This article is based on results obtained from "Research and Development Project of
the Enhanced Infrastructures for Post-5G Information and Communication Systems"(JPNP20017), commissioned by the New Energy and Industrial Technology
Development Organization (NEDO).
REFERENCE[1] 3GPP, “Service requirements for cyber-physical control applications in verticaldomains,” TS 22.104, V17.4.0, Oct 2020.
[2] NEC, “NEC develops RAN autonomous optimization technology that dynamicallycontrols 5G networks based on user terminal status ~Remote control of robots andvehicles with high productivity~,” Feb. 16, 2024.
https://www.nec.com/en/press/202402/global_20240216_01.html[3] NEC, “More freedom in DX and advanced application development, Autonomous
optimization of 5G networks with AI,” Feb. 16, 2024.
https://www.nec.com/en/global/rd/technologies/202315/index.html[4] ORAN Working Group 2, "AI/ML workflow description and requirements,"
Technical Report v1.1, 2020.

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