# Large Language Models meet Network Slicing Management and Orchestration

Abdulhalim Dandoush1,2, Viswanath Kumarskandpriya1, Mueen Uddin2, Usman
Khalil3  1Esme Research Lab, SA ESME, Ivry-Sur-Seine, France  2 University of
Doha for Science and Technology (UDST), Doha, Qatar  3 University Brunei
Darussalam, Brunei Darrussalam  Corresponding author: Abdulhalim Dandoush,
<EMAIL_ADDRESS>
###### Abstract

Network slicing, a cornerstone technology for future networks, enables the
creation of customized virtual networks on a shared physical infrastructure.
This fosters innovation and agility by providing dedicated resources tailored
to specific applications. However, current orchestration and management
approaches face limitations in handling the complexity of new service demands
within multi-administrative domain environments. This paper proposes a future
vision for network slicing powered by Large Language Models (LLMs) and multi-
agent systems, offering a framework that can be integrated with existing
Management and Orchestration (MANO) frameworks. This framework leverages LLMs
to translate user intent into technical requirements, map network functions to
infrastructure, and manage the entire slice lifecycle, while multi-agent
systems facilitate collaboration across different administrative domains. We
also discuss the challenges associated with implementing this framework and
potential solutions to mitigate them.

##  I Introduction

The ever-growing demand for diverse network services with varying Quality-of-
Service (QoS) requirements has pushed the boundaries of traditional network
architectures. Network slicing emerges as a transformative technology
addressing this challenge [1]. It allows the creation of isolated virtual
networks (VNs) on top of a shared physical infrastructure. These slices can be
customized with dedicated resources (connectivity, storage, computing) to
cater to the specific needs of a particular application or service, such as
low-latency services for autonomous vehicles or high-bandwidth services for
virtual reality experiences [2, 3]. Network slicing offers a multitude of
advantages:

  * •

Flexibility and Agility: Network slices can be provisioned, scaled, and
reconfigured to meet dynamic service demands. This allows service providers to
quickly adapt to changing market conditions and offer innovative services to
their customers.

  * •

Enhanced Cost-efficiency: Resources are efficiently utilized by dedicating
them to specific applications, minimizing wastage. Network operators can
optimize their infrastructure investments by allocating resources only where
they are truly needed [4].

  * •

Multi-domain and Multi-tenant Support: It empowers the creation of isolated
network environments, catering to a variety of users and services across
different administrative domains. This enables network operators to offer
secure and reliable network slices to multiple tenants, such as enterprises
and content providers[1].

In addition, network slicing has the potential to revolutionize various
sectors, including:

  * •

Mobile Network Operators (MNOs): Offering differentiated services with
customized SLAs to diverse customer segments [5]. MNOs can leverage network
slicing to create high-performance slices for mission-critical applications
like industrial automation or remote surgery, while also providing basic
connectivity slices for less demanding users.

  * •

Content Providers: Delivering high-quality streaming experiences with
guaranteed bandwidth and low latency. Content providers can create dedicated
network slices to ensure smooth and uninterrupted delivery of video and audio
content to their subscribers [6].

  * •

Enterprises: Establishing secure and reliable private networks for mission-
critical applications. Enterprises can leverage network slicing to create
private slices for their internal applications, ensuring isolation, security,
and predictable performance [7].

Limitations of Current Orchestration and Management While network slicing
holds immense promise, current orchestration and management approaches
primarily rely on SDN based solutions aligned with the specifications of
centralized Network Functions Virtualisation (NFV); Management and
Orchestration of ETSI[8, 9] or with a Multi-Level Delegation Architecture as
described in [7]. These approaches face challenges in handling the complexity
of new service demands with diverse QoS requirements. For instance, imagine a
scenario where a manufacturing company requires a network slice for its
factory automation system. This slice would necessitate ultra-reliable low-
latency communication for real-time control of robotic equipment. A management
system governed by a central authority (e.g., a network operator) might
struggle to efficiently manage such a slice, especially if the factory spans
multiple geographical locations with a slice to span different network
operators (mutli-administrative domains). In fact, each domain might have its
own management system and protocols, making it difficult to achieve seamless
slice creation, management, and resource allocation across these boundaries.
Moreover, the user will be unable to express the exact and correct requirement
and needs of its slice, but rather it will make a high level slice request
expressed in a human manner far from a concrete technical request as assumed
in the current references [9, 10, 11, 1, 4].

Motivation: The Need for collaborative Intelligent Management The dynamic
nature of network slices, coupled with the heterogeneity of services and
multi-domain environments, necessitates not only an intelligent but also a
collaborative management solution. A multi-agent system of Large Language
Models (LLMs) can play a pivotal role in addressing challenges and issues in
the telecom sector [12]. In particular, we believe that they are able to
interpret user intents expressed in natural language can facilitate seamless
communication between orchestrators across different domains in the slicing
context. They are able also to expose an abstracted view of the network
topology of a given region/autonomous system to the collaborators (e.g., the
other operators or slice providers). The agents distributed across the access,
transport, core and cloud networks can collaborate based on the exposed
abstracted pieces of information between them in order to standardize and
translate the user intents into specific technical requirements, enabling
efficient creation or modification of slices that span multiple administrative
domains. Furthermore, LLMs contribute to adaptable resource allocation by
dynamically analyzing patterns, determining optimal computing, memory,
storage, and bandwidth resources for efficient network functioning. In
addition, the agents can help in mapping the chain of network functions
required for the service requesting the slice to the shared infrastructure
across the different parts of the network domains. LLMs also automate complex
network configurations and instruction generation, ensuring accuracy and
adherence to best practices. In real-time monitoring and analysis, LLMs can
report results to network controllers, allowing for proactive measures to
address issues promptly and ensure uninterrupted network performance. To the
best of our knowledge, this is the first work in the literature to explore the
potential of LLMs for Self-Driving Network Slices powered by LLMs.

The rest of this paper is organized as follows: Section II introduce the
basics of Large Language Models. In Section IV the multi-agent LLMs based
approach is described from both the user and operator perspectives. The
challenges of applying LLMs for network management and orchestration is
discussed in Section V and future directions are presented. Section VI
concludes our work.

##  II Large Language Models Overview

In this section we will introduce briefly the Large Language Models (LLMs) and
mention some recent use cases.

LLMs are a type of artificial neural network architecture known as
transformers, first introduced by Vaswani et al. in 2017 [13]. Transformers
deviate from traditional recurrent neural networks (RNNs) by relying solely on
an attention mechanism to capture relationships between elements within the
input data. The architecture of a transformer consists of encoder and decoder
blocks stacked in series. The encoder block processes the input data (text,
code) through multiple layers, with each layer utilizing a so-called “self-
attention” mechanism. This mechanism allows the model to attend to different
parts of the input sequence simultaneously, understanding how each element
relates to the others. This is crucial for capturing long-range dependencies
within the data [13]. In such a system, the decoder block utilizes the encoded
information from the encoder to generate an output. It also employs a self-
attention mechanism to ensure the generated output is consistent with the
context established in the previous steps. Additionally, the decoder uses an
encoder-decoder attention mechanism to attend to relevant parts of the encoded
input during generation. During the training of these artificial neural
networks, LLMs are exposed to massive datasets of text and code. These
datasets can encompass e-books, on-line articles, code repositories, and
online conversations. Through a process called backpropagation, the LLM
adjusts the weights and connections within its neural network to minimize the
difference between its predictions and the actual data. Over time, the LLM
develops the ability to recognize patterns, understand complex relationships,
and generate human-quality outputs [14]. his empowers them to generate human-
quality text, translate languages, create diverse creative content, and answer
your questions in an informative way. The LLMs started to be applied in
different fields from text and image generation to network log analysis and
security threat detection. For example, a recent study in [15] explores the
potential of LLMs to generate task-specific code from natural language queries
to enhancing network management. In another example, Rawat et al., [16]
analyse the different use of LLMs including virtual assistants to provide
real-time enhanced customer support.

##  III Basic E2E Network Slicing Architecture

Before presenting our approach of integrating LLMs into Network Slicing
framework, a general view of the E2E Network Slicing as identified by ETSI NGP
workgroup [17](one of the Standards Developing Organizations (SDOs) working on
setting the standards of the new generation networks) is introduced in this
section. Figure 1 illustrates the reference network slicing architecture as
comprehensively studied in [1]. This architecture is a simple way to represent
slicing from the vision of the different SDOs like 3GPP and ETSI. This will be
used hereafter to highlight how LLMs can empower this promising era. In this
architecture three actors are collaborating:

  * •

Tenants: Consumers of NS services, which can be NS-as-a-Service (NSaaS).

  * •

NS Providers (NSPs): Providers of access to NS instances.

  * •

NS Agents (NSAs): Entities with complete view and control over their network
domain.

Figure 1: SDO’s views on Network Slicing - GSMA, 3GPP and ETSI

The ETSI Network Slicing (NS) architecture encompasses several pivotal
operations, each contributing to the seamless orchestration and deployment of
end-to-end network slices. These operations play a crucial role in fulfilling
tenant requirements, optimizing resource utilization, and ensuring dynamic
adaptability to changing network conditions.

_Service Profile Preparation:_ The initial step involves a tenant preparing a
_service profile_ that articulates the desired NS. This profile includes a
_service graph_ and additional attributes such as service type, profile
identifier, and subscribing entity. The service graph delineates nodes in
terms of compute, storage, and service instance types, along with slice
constraints represented as edges such as the link capacity.

_Resource Database Aggregation:_ Within the NS Provider (NSP), the _resource
database_ (DB) aggregates topological information from various domains in the
underlying infrastructure. The resources DB provides a comprehensive view of
the abstracted physical infrastructure from multiple Network Slice Agents
(NSAs).

_NS Instance Creation:_ The NSP creates the _NS Instance (NSI)_ based on the
received service profile from the tenant. The NSI represents the NSP’s view of
the NS and involves computations and mappings of the service profile to
abstracted infrastructure elements stored in the resources DB.

_NS Delegation and Segment Deployment:_ Upon successful computation and
mapping, the NSP delegates _segments_ to NSAs. A segment represents the set of
paths and nodes (e.g., a load balancer function or a firewalling virtual
function) allocated to a specific NSA, contributing to the deployment of the
overall logical end-to-end NS. The _NS delegation function_ ensures efficient
interconnection of these segments.

_Tenant-Initiated Resource Augmentation:_ Tenants can dynamically scale their
allocated resources during runtime using the _subnet augment function_. This
operation enables tenants to submit _augment requests_ , such as modifying
latency constraints, triggering operations for system stability and resource
adjustments.

The 3GPP vision of slicing is shown in Figure 1 which can be seen as
complementary to the ETSI architecture [1]). Based on the customer’s SLA,
three main 3GPP functions are involved to manage a network slice. The Customer
Service Management Function (CSMF) gathers service requirements from the
customer (e.g., the user of a remote surgery service), converts them into NS
requirements (a technical Service profile), and forwards them to the NS
Management Function (NSMF), which oversees Communication Services provided by
the Network Operator in a given administrative domain.

The NSMF handles the creation and management of NS Instances (NSIs) based on
translated service profile. The NS subnet management is provided then by the
NS Subnet Management Function (NSSMF) at a lower level (the SDN controller
level in the ETSI architecture).

NSs and NS Subnets are supplied by the operators owning the infrastructure,
where the NSMF and NSSMF are located. These key operations collectively form
the foundation of Network Slicing architecture, offering a comprehensive
framework for technology-agnostic network slicing.

###  III-A Limitations and Challenges in Applying Network Slicing Framework

Applying the ETSI/3GPP Network Slicing (NS) framework in scenarios involving
multi-administrative domains, such as network segments managed by different
operators, and highly heterogeneous services and slice profiles presents a set
of challenges and limitations. This section discusses key considerations in
addressing these complexities.

  * •

Interoperability and Standardization: Achieving seamless interoperability
across diverse administrative domains requires robust standardization efforts.

  * •

Security and Trust: Managing security across multiple administrative domains
with distinct policies is complex. Especially, when dealing with a
heterogeneous mix of services crossing an access network (e.g., 5G/6G), an IP
backbone network, and a cloud computing resources where the application is
deployed.

  * •

Resource Coordination and Allocation: Coordinating resources across diverse
administrative domains with different policies is challenging due to
heterogeneous service requirements and lake of complete view for affecting
overall optimization.

  * •

Policy and Governance: Establishing a unified policy framework that aligns
with various governance models is challenging.

  * •

Dynamic Service Orchestration: Orchestrating highly heterogeneous services in
real-time requires dynamic control mechanisms. In fact, complexity in
orchestrating services may result in delays and challenges in meeting dynamic
service demands.

  * •

Slice Lifecycle Management: Diversity in slice profiles may lead to
difficulties in managing and adapting to changes in services and resource
requirements.

  * •

Regulatory Compliance: Ensuring compliance with diverse regulatory frameworks
across domains requires continuous monitoring.

  * •

Service Level Agreements (SLAs): Defining and adhering to SLAs that satisfy
diverse service and profile requirements is complex.

Addressing these challenges necessitates collaborative efforts among
standardization bodies, operators, and service providers to establish common
practices, interfaces, and protocols that facilitate the seamless deployment
of network slices in a multi-administrative domain and heterogeneous services
environment.

##  IV Proposed LLM-based Slicing Management approach

In this section, we describe our proposed approach for utilizing Multi-Agent
Systems (LLMs) as a transformative solution for network slicing management.
Figure 2 describes how the set of LLM agents can be mapped to management
frameworks introduced by SDOs such as ETSI and 3GPP. Each agent within the
system represents a distinct administrative domain, or alternatively, a
Software-Defined Networking (SDN) controller responsible for specific segment
subnet configurations and data/log/statistic collection from the underlying
infrastructure. Notably, an agent can also function as an SDN orchestrator,
synthesizing a high-level abstraction of network, storage, and computing
resources obtained from the agents working at the SDN controllers level in
each domain. This orchestrator agents plays a pivotal role in exposing and
communicating an abstracted view of the topology and the available capacity to
other agents (i.e., on the other administrative domains). Through this
cooperative and intelligent multi-agent system, we envision the collaborative
management of end-to-end network slicing that overcomes the challenges and
limitations posed by the deployment of the SDOs Network Slicing frameworks in
multi-administrative domains with highly heterogeneous services as discussed
in Section III-A.

Figure 2: LLM Agents scope mapped to 3GPP and ETSI related management
functions responsible for network slice deployments

The general position of the LLMs multi-agent in E2E Network Slicing Management
and Orchestration is illustrated in Figure 3. From assisting the user in
making his high level intent based slicing request (e.g., Generic Slice
Template GST in GSMA reference) to the deployment and monitoring of the
different slice functions located at the access network, edge, core and cloud
where the applications are hosted.

LLM Assisted functions can seamlessly collaborate and coordinate with each
other thereby offering enriched user experience to both categories of users:
(i) Network Operators and (ii) Network Consumers. In following subsections, we
will explain the improved user experience flows for each category of users.

Figure 3: High level view of LLMs Assisted Network Slice Management

###  IV-A From Customer request to auto deployment

####  IV-A1 Service Consumer point of view

Large Language Models (LLMs) play a crucial role in simplifying the
interaction between end users or applications and the network orchestration
system. When a user initiates a network slice request, she/he often
communicates her/his needs in natural language, specifying high-level
requirements such as good connectivity, low cost or premium service, high or
law “connection speed”, and reliability. A LLM model as shown in Figure 4 can
translate this high level request to a more concrete service profile,
identifying more technical requirements such as latency, throughput, and
reliability according to the slice template request standardized by GSMA/3GPP.
In other words, LLMs excel in translating these user requests into specific
technical requirements that can be understood and executed by the network
orchestrator(s). Moreover, if the required constraints cannot be met due to
capacity limitation on some portion of the slice, the agent can negotiate
(e.g., by sending a specific prompt) with the corresponding agent located at
the access network level to negotiate a relaxed version of the requirements
that can be allocated and initiated if the user can approve them. Leveraging
their natural language understanding capabilities, LLMs ensure precise
communication between users and the network slice provider related agents.
Figure 4 shows a hypothetical scenario where an end user is requesting one or
two slices for Telemedicine service. In fact, based on the scenario, a
telemedicine service can consume two slices, one with high throughput for the
remote visio and video streaming and another with an ultra low latency
requirement used to send commands remotely to a medical devices (e.g., active
catheter or robots). The user simply states their intent of consuming a
Telemedicine service along with the requirement for high quality video calling
and security, considering the privacy and safety concerns related to the
health data. In fact one can imagine a service catalogue with innovative
services as explained in [7] to simplify the creation of the corresponding
slice or joining an existing one. For every service in the catalogue, a chain
of the required functions is already known and predefined. However this list
cannot cover all the possible requests a user can make, and can limit
innovation. That is why a chatbot agent can assist the user at the early stage
to form any request regardless the predefined catalogue idea. The agent then
can prompt the corresponding agent at the NS provider to suggest a new chain
of functions based on its knowledge and continuous learning of different
patterns. The user usually has zero knowledge how the underlying network
infrastructure is being built and what network slices are available or need to
be created in-order to deliver the Telemedicine or another innovative service.
Here a LLM agent that co-reside on the user equipment (a PC or a medical
device) can quickly provide on-device NLP experience. The LLM agent can
synthesize and understand the user’s intent and efficiently translate that to
a 3GPP service profile in terms of standard service attributes that can be
exchanged with a standards complaint Communication Service Management Function
(CSMF) and Network Slice Management Function (NSMF) respectively.

Here in this case, multiple LLM agents can work at different administrative
domain to execute a control task. For example LLM agents at NSMF can
coordinate with LLM agents as Network Slice Subnet Management functions such
as Core, Access and Transport subdomains to effectively configure underlying
network infrastructure as per the network intents specified in the Slice
profile. In fact, intelligent mapping of one or more network slice profiles to
a service profile can be efficiently performed a utility LLM agent that is
sitting between NSMF and NSSMFs respectively.

Introducing LLM assisted capability in service fulfillment will not only
improve automation and time to efficiency, but also will vastly improve end
user experience. Figure 5 depicts a hypothetical natural language conversation
between an end user and the on-device LLM agent. The conversation starts with
the user stating their intent to consume a telemedicine service. The LLM agent
walk-through all the steps needed to successfully sign-up the user to the
telemedicine service while abstracting away all the complexities in network
service provisioning flow.

LLM agent first extracts the keywords from the user’s intent (such as
telemedicine service, high quality video calls, security, sensitive medical
data) and deduces that for this service to be provided, a special network
slice has to be utilized. LLM Agent also deduces that a 5G NR access or a
fiber optical broadband access is very crucial for the ultra low latency
service requirement.

Figure 4: LLM Assisted End User Network Slice Intent Translation Figure 5: A
hypothetical interaction between an end user and an intelligent LLM,
negotiating over a telemedicine service

####  IV-A2 Network Operator point of view

The potential for LLMs to assist Network Operators in planning and deployment
of network slices is enormous. In this section, we explore some of the
possible use-cases. As in the case of network service consumer, a network
operator user can express their intent of deploying one or more network slices
along with specific optimization goals for each of the network slice. This
intent along with information about the underlying network infrastructure
(such as access, transport, core and datacenter network pieces of information)
can be used by series of LLM agents to coordinate a network modelling
exercise.

Figure 6: Slice LLM Agents

Figure 6 describes the composition of multi-agent framework for LLM assisted
network slice design and deployment, which can be used by a network operator.
Each agent is created with a defined role, goal, inputs to consume and outputs
to produce. Also each agent is equipped with a set of tools to carry out their
respective tasks. The On-Device Agent is the higher-level user facing agent
that resides within the user equipment to facilitate the user interaction with
the Network Slice Provider (NSP) or the Network Operator (NOP). This agent has
access to Slice modelling Agent and Slice Deployment Agent as part of tools
declaration within the agent’s definition. Each tool has its own inputs and
outputs respectively. LLM learns what input to pass and what output to collect
based on the respective tool’s definition. A complete sample listing of
network slice LLM agent definition is provided in the appendix A section as a
Yaml 111YAML is a human-friendly data serialization language for all
programming languages. template.

Figure 7: Sequence of interaction among Slice agents to design and deploy a
network slice against a specific optimization constraint

Figure 7 explains how these LLM agents can work collaboratively for a network
operator use-case. Consider a scenario where a network operator would like to
design and deploy a network slice instance within an underlying network
infrastructure with limited resources such that, the average slice utilization
ratio is greater than 80 percent (for optimizing power and cost). Without LLM
assistance, this process would involve considerable effort as a network
planning activity involving a long journey. However this entire experience can
be rapidly automated with LLMs in picture.

In this typical use-case, the network operator simply expresses its intent to
the On-Device agent which is capable of using Slice modelling and Slice
Deployment Agents. First it deduces the tasks to be followed. In this case, it
learns first the slice modelling has to be done to generate some
recommendations on different slice topologies that would fit the optimization
constraint of maintaining overall slice utilization ratio of greater than 80
percent. In-order to perform this task, it chooses the Slice modelling Agent
based on the description. Since this tool requires some input, On-Device agent
generates that input based on its knowledge and call the Slice modelling Agent
to perform Task 1 as shown in Figure 7. Slice modelling Agent in-turn uses
another tool like slicenet [18] to perform the necessary experiments and
modelling of the network to fit the optimization criteria. Based on the
insights, Slice modelling agent further generates some recommendations on a
different Network Slice Instance (NSI) topologies that could fit the
optimization goal and pass this as the output to On-Device agent (refer to
step #7 in sequence diagram depicted in Figure 7. This is further presented
back to the NOP user to confirm its choice (as shown in step#8 in the figure).
Here the user has option to either accept one of the choice provided by the
LLM or provide reinforcement learning from human/AI (RLHF) feedback to fine
tune the modelling task as needed. If the NOP user proceeds to select a
choice, On-Device agent proceeds to Task 2. In this task, On-Device agent
works with Slice Deployment and Resource Management Agents respectively. These
lower level agents are capable of generating standards based deployment
descriptors such as SliceProfile[19], Network Service Descriptor [20], Network
Function Descriptor [21] and also interact with Network Slice Management
Systems, NFV-Os to deploy the network slice instances. Post deployment, the
information about the created NSI(s) along with other site specific
information is relayed back to the On-Device agent thereby concluding Task 2.

Last, concerning the deployment of Task2, by LLMs assisting in the generation
of specific requirements, such as computing, memory, storage, and bandwidth
resources accross each administrative domain, the NSP ensures that the
orchestrators at different domains are equipped with accurate and standardized
information templates, promoting consistency and interoperability (e.g., using
TOSCA Yaml as shown in Appendix A). Moreover, LLMs support the mapping of user
requirements to network functions and services. This involves analyzing the
placement of functions, which is a critical step in optimizing resource
allocation and then converting the map into actionable configurations,
promoting a harmonized approach to network slicing management. Also, one of
the critical challenges in network slicing is the optimal allocation of
resources to different slices. LLMs, with their ability to analyze and
understand complex patterns and requirements, can determine dynamically the
appropriate computing, memory, storage, and bandwidth resources (e.g., auto-
scaling up or down) at a given time based on the collected statistics from the
southbound interfaces such as P4 or OpenFlow, resulting in a more efficient
network functioning. This adaptability is essential for accommodating the
varying needs of different applications and services and increases NSP profit
while keep respecting the SLA of the custemers.

##  V Challenges and Future Directions

###  V-A Current Challenges

Integrating Large Language Models (LLMs) into end-to-end network slice
management presents several challenges and requires careful consideration:

  1. 1.

Data Set Availability: Acquiring relevant and diverse datasets for training
and fine-tuning LLMs within a network management context remains a challenge.
Simulation techniques can partially address this issue by generating synthetic
data, but ensuring the authenticity and representativeness of such data is
crucial [18].

  2. 2.

Computing Power and Hosting: The computational demands of LLMs, especially
large models, can strain existing infrastructure. Advances in developing
lighter versions of LLMs such as Phi-1 [22], optimized for deployment on edge
devices or cloud environments, are essential for scalability and cost-
effectiveness.

  3. 3.

Network Security Backdoors: LLMs, like any complex software system, can
potentially have vulnerabilities that malicious actors may exploit as
backdoors [23]. Utilizing LLMs themselves for continuous analysis and
mitigation of such security risks is a proactive approach and an active
research area.

  4. 4.

Trust and Validation: Ensuring the trustworthiness and reliability of LLM-
assisted processes is paramount. Legal and regulatory compliance, such as
avoiding SLA violations and network neutrality violation, requires rigorous
validation and adherence to standards mainly with the difficulty of fine-
tuning the LLM models with the network data due to the unexciting of large
deployed slicing technologies. Testbeds and Simulation techniques play a
crucial role in testing and validating LLM integration while addressing trust-
related concerns. Collaboration with communication regulatory authority and
operators is vital for the validation phase before to go to commercialization.

  5. 5.

Interoperability and Standards: Integrating LLM-assisted functions across
heterogeneous network environments requires adherence to interoperability
standards. Developing common protocols and interfaces for communication (e.g.,
northbound interfaces), prompts and data exchange among LLMs and network
components is essential for seamless integration.

  6. 6.

Privacy and Data Protection: LLMs often operate on sensitive data, raising
concerns about privacy and data protection [24]. Implementing robust
encryption methods, data anonymization techniques, and access control
mechanisms can mitigate privacy risks associated with LLM usage.

###  V-B Future Directions and Solutions

To address these challenges and leverage the potential of LLMs for end-to-end
network slice management, several future directions and solutions can be
explored:

  1. 1.

Collaborative Learning: Implementing collaborative learning frameworks where
multiple LLMs collaborate and share knowledge can enhance their collective
intelligence and decision-making capabilities [12].

  2. 2.

Federated Learning: Utilizing federated learning techniques to train LLMs
across distributed network environments while preserving data privacy and
security [25].

  3. 3.

Explainable AI: Enhancing the explainability of LLMs to provide transparent
insights into their decision-making processes, fostering trust and
understanding among stakeholders [26].

  4. 4.

Continuous Monitoring and Auditing: Implementing robust monitoring and
auditing mechanisms to detect and mitigate potential biases, errors, or
security vulnerabilities in LLM-assisted processes.

  5. 5.

Ethical Considerations: Incorporating ethical frameworks and guidelines into
the development and deployment of LLMs to ensure responsible and fair use of
AI technologies in network management.

  6. 6.

User-Centric Design: Designing LLM-assisted interfaces and workflows with a
focus on user experience and usability, enabling intuitive interaction and
effective utilization of LLM capabilities.

  7. 7.

Industrial, academic and governmental Collaboration: Promoting collaboration
and knowledge-sharing among industry stakeholders, academia, and regulatory
bodies to address common challenges and foster innovation in LLM-enabled
network management.

By addressing these challenges and exploring innovative solutions, the
integration of LLM multi-agent systems into end-to-end network slice
management can unlock significant benefits in terms of efficiency, automation,
and optimization of telecommunications networks.

##  VI Conclusion

In the era of multi-domain network slicing, the integration of large language
models (LLMs) and advanced AI technologies redefines network management
paradigms. By embracing intelligent, context-aware orchestration,
organizations can build responsive, secure, and efficient network
infrastructures spanning diverse administrative domains. This research
illuminates the path forward, where AI-driven multi-domain network slicing
becomes the cornerstone of next-generation networks, ensuring unparalleled
connectivity and user satisfaction. In this context, we proposed a framework
that maps agents to the ETSI and 3GPP vision and recent management works of
E2E slicing. This framework explains how LLMs can collaborate from assisting
the user in initiating the request to deploying, continually monitoring
resource consumption, and assisting in auto-scaling and allocation
optimization.

However, it’s essential to acknowledge the challenges associated with
integrating LLMs for end-to-end network slicing, including data set
availability, computing power, network security backdoors, trust and
validation, interoperability and privacy. Addressing these challenges will
pave the way for future research and development efforts in this domain. It is
important to explore new collaborative learning techniques, improving
explainability of AI decisions, and promoting industry and regulation
authority collaboration to establish standards and best practices.

## References

  * [1] M. Chahbar, G. Diaz, A. Dandoush, C. Cerin, and K. Ghoumid, “A comprehensive survey on the e2e 5g network slicing model,” _IEEE Transactions on Network and Service Management_ , vol. 18, no. 1, pp. 49–62, 2021.
  * [2] A. Kammoun, N. Tabbane, G. Diaz, N. Achir, and A. Dandoush, “Proactive network slices management algorithm based on fuzzy logic system and support vector regression model,” in _Advances on Broad-Band Wireless Computing, Communication and Applications - Proceedings of the 14th International Conference on Broad-Band Wireless Computing, Communication and Applications, BWCCA 2019, Antwerp, Belgium, November 7-9, 2019_ , ser. Lecture Notes in Networks and Systems, L. Barolli, P. Hellinckx, and T. Enokido, Eds., vol. 97. Springer, 2019, pp. 386–397. [Online]. Available: https://doi.org/10.1007/978-3-030-33506-9_34
  * [3] S. Wijethilaka and M. Liyanage, “Survey on network slicing for internet of things realization in 5g networks,” _IEEE Communications Surveys & Tutorials_, vol. 23, no. 2, pp. 957–994, 2021.
  * [4] M. Chahbar, G. Diaz, and A. Dandoush, “Inters: Towards inter-slice bandwidth resource sharing,” in _22nd IEEE International Conference on High Performance Switching and Routing, HPSR 2021, Paris, France, June 7-10, 2021_. IEEE, 2021, pp. 1–6. [Online]. Available: https://doi.org/10.1109/HPSR52026.2021.9481833
  * [5] R. Raman, R. Singh, Z. Gupta, S. Verma, A. Rajput, and S. M. Parikh, “Wireless communication with extreme reliability and low latency: Tail, risk and scale,” in _2022 5th International Conference on Contemporary Computing and Informatics (IC3I)_ , 2022, pp. 1699–1703.
  * [6] K. Mehmood, K. Kralevska, and D. Palma, “Intent-driven autonomous network and service management in future cellular networks: A structured literature review,” _Computer Networks_ , vol. 220, p. 109477, 2023.
  * [7] A. Dandoush, A. Kammoun, g. diaz, n. achir, and N. Tabbane, “Enabling new generation services with multi-level delegation architecture of slicing technology,” Apr. 2023. [Online]. Available: http://dx.doi.org/10.36227/techrxiv.22581145.v1
  * [8] _Network Functions Virtualisation (NFV); Ecosystem; Report on SDN Usage in NFV Architectural Framework_ , ETSI GS NFV-EVE 005, Dec. 2015, v. 1.1.1.
  * [9] _Network Functions Virtualisation (NFV); Management and Orchestration_ , GS NFV-MAN 001, Dec. 2014, v. 1.1.1.
  * [10] A. Devlic, A. Hamidian, D. Liang, M. Eriksson, A. Consoli, and J. Lundstedt, “Nesmo: Network slicing management and orchestration framework,” in _2017 IEEE International Conference on Communications Workshops (ICC Workshops)_ , May 2017, pp. 1202–1208.
  * [11] M. Chahbar, G. Diaz, and A. Dandoush, “Towards a unified network slicing model,” in _2019 15th International Conference on Network and Service Management (CNSM)_ , 2019, pp. 1–5.
  * [12] L. Bariah, Q. Zhao, H. Zou, Y. Tian, F. Bader, and M. Debbah, “Large language models for telecom: The next big thing?” 2023.
  * [13] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” _CoRR_ , vol. abs/1706.03762, 2017. [Online]. Available: http://arxiv.org/abs/1706.03762
  * [14] J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: pre-training of deep bidirectional transformers for language understanding,” in _Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers)_ , J. Burstein, C. Doran, and T. Solorio, Eds. Association for Computational Linguistics, 2019, pp. 4171–4186. [Online]. Available: https://doi.org/10.18653/v1/n19-1423
  * [15] S. K. Mani, Y. Zhou, K. Hsieh, S. Segarra, T. Eberl, E. Azulai, I. Frizler, R. Chandra, and S. Kandula, “Enhancing network management using code generated by large language models,” in _Proceedings of the 22nd ACM Workshop on Hot Topics in Networks_ , ser. HotNets ’23. New York, NY, USA: Association for Computing Machinery, 2023, p. 196–204. [Online]. Available: https://doi.org/10.1145/3626111.3628183
  * [16] B. Rawat, A. S. Bist, U. Rahardja, Q. Aini, and Y. P. Ayu Sanjaya, “Recent deep learning based nlp techniques for chatbot development: An exhaustive survey,” in _2022 10th International Conference on Cyber and IT Service Management (CITSM)_ , 2022, pp. 1–4.
  * [17] _Next Generation Protocols (NGP); E2E network slicing Reference Framework and Information Model_ , ETSI GR NGP 011, 2018.
  * [18] V. KumarSkandPriya, A. Dandoush, and G. Diaz, “Slicenet: a simple and scalable flow-level simulator for network slice provisioning and management,” in _2023 International Symposium on Networks, Computers and Communications (ISNCC)_ , 2023, pp. 1–6.
  * [19] 3GPP, “Management and orchestration of 5g networks; network resource model (nrm); stage 2 and stage 3.” 3GPP, Tech. Rep. 3GPP TS 28.541 version 16.6.0 Release 16, 2023.
  * [20] ETSI, “Network functions virtualisation (nfv) release 3; protocols and data models; network service descriptor file structure specification,” ETSI, Tech. Rep. V3.5.1 (2021-06), 2021.
  * [21] ——, “Network functions virtualisation (nfv) release 4; protocols and data models; nfv descriptors based on tosca specification,” ETSI, Tech. Rep. ETSI GS NFV-SOL 001 V4.3.1 (2022-08), 2022.
  * [22] S. Gunasekar, Y. Zhang, J. Aneja, C. Cesar, T. Mendes, A. D. Giorno, S. Gopi, M. Javaheripi, P. Kauffmann, G. de Rosa, O. Saarikivi, A. Salim, S. Shah, H. Singh Behl, X. Wang, S. Bubeck, R. Eldan, A. T. Kalai, Y. T. Lee, and Y. Li, “Textbooks are all you need,” June 2023. [Online]. Available: https://www.microsoft.com/en-us/research/publication/textbooks-are-all-you-need/
  * [23] H. Yang, K. Xiang, M. Ge, H. Li, R. Lu, and S. Yu, “A comprehensive overview of backdoor attacks in large language models within communication networks,” _IEEE Network_ , pp. 1–1, 2024.
  * [24] Y. Wu, F. Roesner, T. Kohno, N. Zhang, and U. Iqbal, “Secgpt: An execution isolation architecture for llm-based systems,” 2024.
  * [25] R. Ye, W. Wang, J. Chai, D. Li, Z. Li, Y. Xu, Y. Du, Y. Wang, and S. Chen, “Openfedllm: Training large language models on decentralized private data via federated learning,” 2024.
  * [26] X. Wu, H. Zhao, Y. Zhu, Y. Shi, F. Yang, T. Liu, X. Zhai, W. Yao, J. Li, M. Du, and N. Liu, “Usable xai: 10 strategies towards exploiting explainability in the llm era,” 2024.

##  Appendix A Sample LLM Agents Definition with agent role, description and
associated tools used by each agent.

⬇

1llm-assisted-slice-agents:

2 - name : ’On-Device Agent’,

3 goal : ’Design and Deploy a network slice infrastructure within the
constraints given by the network operator’,

4 prompt : ’You are a helpful on-device assistant. You are allowed use the
agents mentioned under tools section to accomplish the goal. You should break
down the main goal into different tasks and for each task, use the appropriate
agent to accomplish that task. For each task, provide the necessary inputs. At
the end of each task, collect the output and process it if necessary and chain
that output to subsequent tasks if needed.’

5 tools :

6 - name : ’modeller’,

7 agent : ’Slice modelling Agent’,

8 description : ’Use this tool to perform simulation of different network
scenarios and collect the recommendations based on experiment insights’,

9 input : ’network slice modelling requirements’,

10 output : ’slice topology recommendations’

11 - name : ’deployer’,

12 agent : ’Slice Deployment Agent’,

13 description : ’Use this tool to perform deployment of a specific network
topology and collect the network access information’,

14 input : ’network slice deployment information’,

15 output : ’deployed network slice information’

16 - name : ’Slice Modelling Agent’,

17 goal : ’Model a network slice according to the given constraint by
performing experiments on various network topologies to arrive at
recommendations’,

18 prompt : ’You are a network slice simulation expert. You are allowed use
the agents mentioned under tools section to accomplish the goal. You should
break down the main goal into different tasks and for each task, use the
appropriate agent to accomplish that task. For each task, provide the
necessary inputs. At the end of each task, collect the output, \

19 process it if necessary and chain that output to subsequent tasks if
needed.’

20 tools :

21 - name : ’slicenet’,

22 description : ’Use this tool to perform simulation of different network
scenarios and collect the recommendations based on experiment insights’,

23 input : ’*.yaml files representing slicenet experiments’,

24 output : ’slice topology recommendations and Slice Profiles for each
recommendation’

25 - name : ’slicenet-preparer’,

26 description : ’Use this tool to generate slicent yaml files describing the
experiment goal, network constraints and network infrastructure information’,

27 input : ’network slice modelling requirements’,

28 output : ’*.yaml files representing slicenet experiments’

29 - name : ’Slice Deployment Agent’,

30 goal : ’Deploy a network slice topology using a given network
infrastructure by using standards based network management frameworks’,

31 prompt : ’You are a network slice management expert. You are allowed use
the agents mentioned under tools section to accomplish the goal. You should
break down the main goal into different tasks and for each task, use the
appropriate agent to accomplish that task. For each task, provide the
necessary inputs. At the end of each task, collect the output, process it if
necessary and chain that output to subsequent tasks if needed.’

32 tools :

33 - name : ’nsi-deployer’,

34 description : ’Use this tool to deploy or discover NSI using NSMF or
similar orchestrators and collect network slice instance information including
Slice ID and other deployment information’,

35 input : ’Slice deployment descriptors’,

36 output : ’Network Slice Instance Information’

37 - name : ’nsi-preparer’,

38 description : ’Use this tool to generate network slice & subnet templates
that describes the network slice instance topology based on slice profile’,

39 input : ’network slice profile based on GSMA NEST’,

40 output : ’TOSCA YAML files in ETSI SOL 007 NSD and ETSI SOL 001 VNFD or
similar deployment descriptors’

41 - name : ’Resource Management Agent’,

42 goal : ’Manage Virtual or Physical network infrastructure resources and
serve resource allocation request at the VIM layer using standards based
network management frameworks’

43 prompt : ’You are a network infrastructure expert. You are allowed use the
agents mentioned under tools section to accomplish the goal. You should break
down the main goal into different tasks and for each task, use the appropriate
agent to accomplish that task. For each task, provide the necessary inputs. At
the end of each task, collect the output, process it if necessary and chain
that output to subsequent tasks if needed.’

44 tools :

45 - name : ’resource-deployer’,

46 description : ’Use this tool to deploy network function and associated
dependencies and collect network slice instance information including Slice ID
and other deployment information’,

47 input : ’NF deployment descriptors’,

48 output : ’Network Function Instance Information’

49 - name : ’resource-manager’,

50 description : ’Use this tool to perform LCM and Resource management of
network functions’,

51 input : ’network function instance information & resource management
actions {start, stop, scale, restart, delete} ’,

52 output : ’NF Monitoring information’

Listing 1: LLM Assisted Slice Agents Definitions in Yaml template


# Extreme wave excitation from localized phase-shift perturbations

Y. He1<EMAIL_ADDRESS>A. Witt2<EMAIL_ADDRESS>S. Trillo3 A.
Chabchoub4,5,1 N. Hoffmann2,6<EMAIL_ADDRESS>1 Centre for Wind,
Waves and Water, School of Civil Engineering, The University of Sydney,
Sydney, NSW 2006, Australia  2 Dynamics Group, Hamburg University of
Technology, Hamburg, Germany  3 Department of Engineering, University of
Ferrara, via Saragat 1, 44122 Ferrara, Italy  $4$ Hakubi Center for Advanced
Research, Kyoto University, Yoshida-Honmachi, Kyoto 606-8501, Japan  5
Disaster Prevention Research Institute, Kyoto University, Kyoto 611-0011,
Japan  6 Department of Mechanical Engineering, Imperial College London,
London, United Kingdom

###### Abstract

The modulation instability is a focusing mechanism responsible for the
formation of strong wave localizations not only on the water surface, but also
in a variety of nonlinear dispersive media. Such dynamics is initiated from
the injection of side-bands, which translate into an amplitude modulation of
the wave field. The nonlinear stage of unstable wave evolution can be
described by exact solutions of the nonlinear Schrödinger equation (NLSE). In
that case, the amplitude modulation of such coherent extreme wave structures
is connected to a particular phase-shift seed in the carrier wave. In this
letter, we show that phase-shifts localization applied to the background,
excluding any amplitude modulation excitation, can indeed trigger extreme
events. Such rogue waves can be for instance generated by considering the
parametrization of fundamental breathers and thus, by seeding only the local
phase-shift information to the regular carrier wave. Our wave tank experiments
show an excellent agreement with the expected NLSE hydrodynamics and confirm
that even though delayed in their evolution, breather-type extreme waves can
be generated from a purely regular wave train. Such novel focusing mechanism
awaits experimental confirmation in other nonlinear media, such optics,
plasma, and Bose-Einstein condensates.

The formation of extreme wave events, so-called rogue waves, can be understood
and modelled by the nonlinear Schrödinger equation (NLSE) or its higher-order
forms [1, 2, 3, 4]. One way to control such rogue waves in a laboratory
environment is the use of fundamental breather solutions in form of time-
periodic, doubly-localized, and space-periodic forms. These are also known as
Akhmediev (AB) [5], Peregrine (PB) [6], and Kuznetsov breather (KB) [7],
respectively. Such solutions are based on long wave perturbation of a regular
wave train with a precise dedicated phase-shift of the carrier at a specific
location and instant. While, modulational instability (MI) is usually seen as
a mechanism that leads, through the amplification of sidebands, to a strong
amplitude modulation, these phase-shifts always accompany such MI-induced
amplitude reshaping. Indeed, the initial temporal phase profile, or
equivalently, the input phase relationship in Fourier space (i.e. the relative
phase between the MI side-bands and the central carrier) have deep impact on
the type of amplitude modulation that develops upon propagation [8, 9], as
recently observed experimentally [10, 11, 12].

The aim of this paper is to show that the initial phase-shift profile contains
the leading information that allows the breathers to undergo the peak growth
and focusing process even when the input amplitude modulation is suppressed. A
previous experimental study has revealed that locally the same type of maximal
breather compression can be achieved by just starting from the amplitude
modulation only and ignoring the phase-shift in the boundary condition [13].
In this experimental study, we provide evidence for the opposite phenomenon,
namely that extreme wave events can be triggered and excited by a localized
phase-shift perturbation (PSP) in a regular (i.e., constant amplitude) wave
train.

In our study, we use specific phase-shifts proper to the three fundamental
breathers, as mentioned above, and show that the extreme localization achieved
in the wave flume resembles those of pure breathers, though is spatially
delayed with respect to the exact solution. In fact, the smaller the phase-
shift applied to the carrier the better the agreement with the respective pure
breather solution. All observations are in reasonable agreement with the NLSE
prediction despite the very strong wave focusing reached.

The NLSE is the simplest framework to describe the wave propagation of
nonlinear dispersive waves [14]. For the deep-water wave problem the slowly-
varying wave envelope $\Psi$ around the wave number $k$ satisfies [15, 2]

$\displaystyle
i\left(\Psi_{x}+\dfrac{2k}{\omega}\Psi_{t}\right)-\dfrac{k^{2}}{\omega}\Psi_{tt}-k^{3}\left|\Psi\right|^{2}\Psi=0,$
(1)

where $\omega=\sqrt{gk}$ is the dispersion relation, $g$ being the
gravitational acceleration.

Figure 1:  (a,b,c) Akhmediev breather, contrasting spatio-temporal evolutions
arising from (a) the full analytical Solution and (b) the PSP; (c) temporal
profile of exact solution compared to that generated from the PSP at the
location of maximal compression. (d,e,f) Peregrine breather, contrasting
spatio-temporal evolutions arising from (d) the full solution and (e) the PSP;
(f) comparison of transverse profiles at maximal compression. (g,h,i)
Kuznetsov breather, contrasting spatio-temporal evolutions arising from (g)
the full solution and (h) the PSP; (i) comparison of transverse profiles at
maximal compression. Here the adopted carrier wave steepness $ak=0.1$ and
carrier amplitude $a=0.01$ m. All waves are excited at $x=-30$, i.e. 30 meters
ahead of the first focus point ($x=0$) of the exact solution.

As a result of integrability a variety of steady and pulsating solution have
been derived [16, 17]. We will not recall the parametrization of three
fundamental breathers on a finite-amplitude carrier as these have been
discussed in many past publications [18, 19, 20, 21]. The family of ABs
describe the nonlinear stage of MI and are periodic in time when considering
the framework (1), the PB is the limit case of zero modulation frequency, i.e.
infinite modulation period, and the KBs the space periodic solution, which
starts its evolution from a solitonic perturbation on the carrier. As such,
the KB never converges to a uniform amplitude. All these solutions have been
observed in a variety of nonlinear dispersive media and, for more details, we
refer the reader to [18, 19, 22, 20, 21]. In water waves the boundary
conditions at beginning of the flume consist of time-series of surface
displacement. The latter are driving the wave maker while its mechanical
motion is linearly proportional to the surface elevation signal as determined
by theory. To drive the wavemaker and to observe NLSE solitons or breathers,
it is sufficient to use the expression of surface elevation defined to first-
order of approximation [22]

$\displaystyle\eta(x,t)=\textnormal{Re}\left(\Psi\left(x,t\right)\exp\left[i\left(kx-\omega
t\right)\right]\right).$ (2)

As mentioned in [13], each of these exact solutions $\Psi(x,t)$ can be written
as amplitude modulation function $A(x,t)$ and a respective phase-shift
$\phi(x,t)$, both evolving in time and space. Mathematically speaking,
$\Psi(x,t)=A(x,t)\exp[i\phi(x,t)]$. Also in the latter work, it has been shown
that wave breather-like focusing can develop when starting from boundary
conditions at beginning of the flume, involving the amplitude modulation only
without consideration of the breather-specific local phase-shift. As next, we
propose a novel extreme wave focusing mechanism, which originates in a regular
and amplitude-modulation-free wave train, with a PSP, i.e. a localized phase-
shift. While a similar mechanism was originally investigated theoretically in
[23] for abrupt phase jumps, in this paper, we rather consider the PSP
obtained by impressing the localized phase profile of the pure breather
solution at a given distance from the focus point. Examples of evolution,
obtained from the NLSE (1) are shown in Fig. 1(a,b,c) for the AB, in Fig.
1(d,e,f) for the PB, and in Fig. 1(g,h,i) for the KB. In all examples $x=0$
stands for the first focus point, and the PSP is excited at $x=-30$. As shown,
in all three different breather cases considered, a strong wave envelope
compression is expected to occur in the condensate from a PSP-triggering only.
We emphasize that the smaller the from the solution defined amplitude
modulation, and as such the related phase-shift of the exact solution for the
smaller $x$ values, the better the agreement with the extreme waves starting
from the phase-shift perturbation at the maximum focus point. Note that
similarly to the mechanism studied in [13], the location of maxima deviates
and is retarded compared to the motion of exact solution. Moreover, we can
clearly notice that deviations are rather substantial when considering the KM
case. The reason for this is that the solution never converges to the
background and thus triggering the boundary conditions from a regular wave
train with $\phi_{KM}(x^{*},t)$ will more substantially deviate compared to
the AB- or PB-type dynamics. In any event, the PSP-induced dynamics for both
the PB and the KM cases show a post-focusing dynamics dominated by fissions
into pairs of quasi-KM breathers with opposite velocities [13], whose
observation, however, require tanks with length far exceeding that of the
present experiment.

In the following we report on our experimental investigation. We adopt the
same set-up as described in [24]. Moreover, we have to be cautious about the
fact that carrier parameters have to be chosen in such a way to allow for the
observation of the first focus (compression) point within the tank length for
either the full exact breather or the corresponding respective PSP of the
regular carrier wave.

Figure 2: AB evolution for $ak=0.12$ and $a=0.01$ with expected amplitude
amplification of 2.41 (the case of maximal growth rate. (a) Propagation of
wave envelope as measured in the flume. (b) NLSE simulations with same
boundary conditions as in (a). (c) Experimental observation of the AB-PSP in
the regular background case. (d) NLSE simulations with same boundary
conditions as in (c). (e) Analytical and phase-shifted AB wave elevation
boundary conditions at $x=-16$ m as implemented into the experiments and
simulations.

As a starting point, our experimental investigation is initiated by
investigating the AB dynamics triggered by both exact and PSP configurations
as discussed above and illustrated in Fig. 1(a,b,c). The results are shown in
Fig. 2. All collected measurements of the exact time-periodic breather show a
reasonable agreement with NLSE theory. This applies for both the case when
considering the pure AB solution of the NLSE and the regular wave train in
which the AB-type phase-shift have been locally seeded. Remarkably, a gradual
focusing clearly emerge from the phase-shift seeding in agreement with the
numerical NLSE expectations.

Figure 3: PB evolution for $ak=0.1$ and $a=0.01$ with expected theoretical
amplitude amplification of 3. (a) Propagation of wave envelope as measured in
the flume. (b) NLSE simulations with same boundary conditions as in (a). (c)
Experimental observation of the PB-PSS in the regular background case. (d)
NLSE simulations with same boundary conditions as in (c). (e) Analytical and
phase-shifted PB wave elevation boundary conditions at $x=-12$ m as
implemented into the experiments and simulations.

The same excellent agreement in the wave focusing is also observed for case of
PB, which is illustrated in Fig. 3. In this case one particular feature of
focusing from the PSP in the background of constant amplitude becomes clear.
The retardation and delay in the focusing from the single unstable Peregrine-
packet is noticeable. A similar feature has been observed when ignoring the
breather-specific phase-shift and considering only the amplitude modulation
[13]. These observations also expose the limitations of our experimental set-
up. Since we start from a small phase-shift value in the carrier, the
retardation of maximal wave focusing of the waves would require a long fetch
to observe interesting post-focusing dynamics (recurrence, triangular patterns
etc.). This is also the case for the KM breather. We chose the case of
amplitude focusing factor of 3.3 and as the previous breather cases, the two
type of wave envelope dynamics are shown in Fig. 4.

Figure 4: KM evolution for $ak=0.1$ and $a=0.01$ with expected amplitude
amplification of 3.3. (a) Propagation of wave envelope as measured in the
flume. (b) NLSE simulations with same boundary conditions as in (a). (c)
Experimental observation of the KM-PSS in the regular background case. (d)
NLSE simulations with same boundary conditions as in (c). (e) Analytical and
phase-shifted KM wave elevation boundary conditions at $x=-12$ m as
implemented into the experiments and simulations.

We annotate that we employ the same colobar scale in all Figures to make
significant wave focusing more distinct. Overall, all cases show a reasonable
good agreement with the weakly nonlinear NLSE framework approximated from the
Euler equations to third-order in steepness. That said, a typical wave
asymmetry is clearly noticeable in the experimental data. The latter can be
explained and modelled by the modified NLSE accounting for fourth-order
effects in steepness in form of higher-order dispersion and mean flow [25, 26,
27]. Note that when injecting the PSP an adjustment of the linear wave maker’s
frequency-related transfer function is required to ensure the regularity of
the wave train.

In summary, we have reported an experimental study confirming a proof of
concept that rogue waves can appear from a localized PSP in a regular wave
train. Such PSP can be for instance constructed from breather solutions of the
nonlinear Schrödinger equation and then, seeded in a condensate. Our wave tank
measurements underline that nonlinear focusing can be exhibited by PSP
confirming yet another focusing mechanism beyond the wave modulation or
superposition principle. We anticipate future experimental studies exploring
such focusing dynamics in various wave systems governed by dispersion and
nonlinearity [28, 29, 30] and also the development of theoretical techniques
to predict such dynamics within the NLSE framework and beyond [31, 32].

## References

  * Akhmediev and Ankiewicz [1997] N. N. Akhmediev and A. Ankiewicz,  _Solitons: nonlinear pulses and beams_ (Chapman & Hall, 1997).
  * Osborne [2010] A. Osborne,  _Nonlinear Ocean Waves and the Inverse Scattering Transform_ (Academic Press, 2010).
  * Onorato  _et al._ [2013] M. Onorato, S. Residori, U. Bortolozzo, A. Montina, and F. Arecchi, Rogue waves and their generating mechanisms in different physical contexts, Physics Reports 528, 47 (2013).
  * Dudley  _et al._ [2014] J. M. Dudley, F. Dias, M. Erkintalo, and G. Genty, Instabilities, breathers and rogue waves in optics, Nature Photonics 8, 755 (2014).
  * Akhmediev  _et al._ [1985] N. Akhmediev, V. Eleonskii, and N. Kulagin, Generation of periodic trains of picosecond pulses in an optical fiber: exact solutions, Sov. Phys. JETP 62, 894 (1985).
  * Peregrine [1983] D. H. Peregrine, Water waves, nonlinear Schrödinger equations and their solutions, J. Austral. Math. Soc. Ser. B 25, 16 (1983).
  * Kuznetsov [1977] E. A. Kuznetsov, Solitons in a parametrically unstable plasma, in  _Akademiia Nauk SSSR Doklady_ , Vol. 236 (1977) pp. 575–577.
  * Trillo and Wabnitz [1991] S. Trillo and S. Wabnitz, Dynamics of the nonlinear modulational instability in optical fibers, Optics letters 16, 986 (1991).
  * Armaroli  _et al._ [2020] A. Armaroli, A. Gomel, A. Chabchoub, M. Brunetti, and J. Kasparian, Stabilization of uni-directional water wave trains over an uneven bottom, Nonlinear Dynamics 101, 1131 (2020).
  * Kimmoun  _et al._ [2016] O. Kimmoun, H. C. Hsu, H. Branger, M. S. Li, Y. Y. Chen, C. Kharif, M. Onorato, E. J. R. Kelleher, B. Kibler, N. Akhmediev, and A. Chabchoub, Modulation instability and phase-shifted fermi-pasta-ulam recurrence, Sci. Rep. 6, 28516 (2016).
  * Mussot  _et al._ [2018] A. Mussot, C. Naveau, M. Conforti, A. Kudlinski, F. Copie, P. Szriftgiser, and S. Trillo, Fibre multi-wave mixing combs reveal the broken symmetry of fermi–pasta–ulam recurrence, Nature photonics 12, 303 (2018).
  * Pierangeli  _et al._ [2018] D. Pierangeli, M. Flammini, L. Zhang, G. Marcucci, A. Agranat, P. Grinevich, P. Santini, C. Conti, and E. DelRe, Observation of fermi-pasta-ulam-tsingou recurrence and its exact dynamics, Physical Review X 8, 041017 (2018).
  * Chabchoub  _et al._ [2020] A. Chabchoub, T. Waseda, M. Klein, S. Trillo, and M. Onorato, Phase-suppressed hydrodynamics of solitons on constant-background plane wave, Physical Review Fluids 5, 114801 (2020).
  * Ablowitz [2011] M. J. Ablowitz,  _Nonlinear dispersive waves: asymptotic analysis and solitons_ , Vol. 47 (Cambridge University Press, 2011).
  * Zakharov [1968] V. E. Zakharov, Stability of periodic waves of finite amplitude on the surface of a deep fluid, Journal of Applied Mechanics and Technical Physics 9, 190 (1968).
  * Shabat and Zakharov [1972] A. Shabat and V. Zakharov, Exact theory of two-dimensional self-focusing and one-dimensional self-modulation of waves in nonlinear media, Sov. Phys. JETP 34, 62 (1972).
  * Akhmediev [2021] N. Akhmediev, Waves that appear from nowhere: complex rogue wave structures and their elementary particles, Frontiers in Physics 8, 612318 (2021).
  * Dudley  _et al._ [2010] J. M. Dudley, G. Genty, F. Dias, B. Kibler, and N. Akhmediev, Modulation instability, akhmediev breathers, and rogue waves in nonlinear fiber optics, in  _Fiber Lasers VII: Technology, Systems, and Applications_ , Vol. 7580 (SPIE, 2010) pp. 615–620.
  * Kibler  _et al._ [2010] B. Kibler, J. Fatome, C. Finot, G. Millot, F. Dias, G. Genty, N. Akhmediev, and J. M. Dudley, The peregrine soliton in nonlinear fibre optics, Nature Physics 6, 790 (2010).
  * Kibler  _et al._ [2012] B. Kibler, J. Fatome, C. Finot, G. Millot, G. Genty, B. Wetzel, N. Akhmediev, F. Dias, and J. M. Dudley, Observation of kuznetsov-ma soliton dynamics in optical fibre, Scientific reports 2, 1 (2012).
  * Chabchoub and Grimshaw [2016] A. Chabchoub and R. H. Grimshaw, The hydrodynamic nonlinear Schrödinger equation: Space and time, Fluids 1, 23 (2016).
  * Chabchoub  _et al._ [2011] A. Chabchoub, N. Hoffmann, and N. Akhmediev, Rogue wave observation in a water wave tank, Physical Review Letters 106, 204502 (2011).
  * Sheveleva and Finot [2020] A. Sheveleva and C. Finot, Temporal fresnel diffraction induced by phase jumps in linear and nonlinear optical fibres, Results in Physics 19, 103344 (2020).
  * He  _et al._ [2021] Y. He, P. Suret, and A. Chabchoub, Phase evolution of the time-and space-like peregrine breather in a laboratory, Fluids 6, 308 (2021).
  * Trulsen and Stansberg [2001] K. Trulsen and C. T. Stansberg, Spatial evolution of water surface waves: Numerical simulation and experiment of bichromatic waves, in  _The Eleventh International Offshore and Polar Engineering Conference_ (OnePetro, 2001).
  * Shemer and Alperovich [2013] L. Shemer and L. Alperovich, Peregrine breather revisited, Physics of Fluids 25, 051701 (2013).
  * Slunyaev  _et al._ [2013] A. Slunyaev, E. Pelinovsky, A. Sergeeva, A. Chabchoub, N. Hoffmann, M. Onorato, and N. Akhmediev, Super rogue waves in simulations based on weakly nonlinear and fully nonlinear hydrodynamic equations, Physical Review E 88, 012909 (2013).
  * Kourakis  _et al._ [2013] I. Kourakis, M. Mc Kerr, and A. Ur-Rahman, Semiclassical relativistic fluid theory for electrostatic envelope modes in dense electron–positron–ion plasmas: Modulational instability and rogue waves, Journal of Plasma Physics 79, 1089 (2013).
  * Luo  _et al._ [2020] D. Luo, Y. Jin, J. H. Nguyen, B. A. Malomed, O. V. Marchukov, V. A. Yurovsky, V. Dunjko, M. Olshanii, and R. Hulet, Creation and characterization of matter-wave breathers, Physical Review Letters 125, 183902 (2020).
  * Frisquet  _et al._ [2013] B. Frisquet, B. Kibler, and G. Millot, Collision of akhmediev breathers in nonlinear fiber optics, Physical Review X 3, 041032 (2013).
  * Suret  _et al._ [2020] P. Suret, A. Tikan, F. Bonnefoy, F. Copie, G. Ducrozet, A. Gelash, G. Prabhudesai, G. Michel, A. Cazaubiel, E. Falcon, G. El, and R. Stéphane, Nonlinear spectral synthesis of soliton gas in deep-water surface gravity waves, Physical Review Letters 125, 264101 (2020).
  * Mullyadzhanov and Gelash [2021] R. Mullyadzhanov and A. Gelash, Solitons in a box-shaped wave field with noise: Perturbation theory and statistics, Physical Review Letters 126, 234101 (2021).