1) AI and connectivity will play crucial roles in next-generation networks that will need to automatically compute, learn, and respond to business needs. This will pose challenges regarding complexity, scalability, security, and reliability that AI can help address. 2) The combination of connected devices, advanced computing, and communication technologies creates highly complex systems that require high levels of intelligent automation. AI can help CSPs design and run 5G networks by handling complex dependencies and making autonomous decisions. 3) Various industries are increasingly linked through AI and connectivity technologies. AI will enhance network autonomy in telecom and enable transformations in sectors like transportation and manufacturing. Safety is a major concern requiring predictive analytics and failure detection across cloud, network,

1. AI & Connectivity: Challenges &
Growth Strategies For The Future
Next-generation of networks are expected to compute, learn to think and respond to
business needs almost automatically and manage the constant growth of data
generated by the ever-growing number of connected, intelligent devices. Artificial
Intelligence (AI) plays a crucial role in automating processes, managing complexity,
scalability, and using information from distributed systems in real time. This article
explores the technical issues the R&D community has to tackle to assist the
communications service provider (CSPs) and other players in the industry fully benefit
from the capabilities of AI.
A combination of millions of connected devices, petascale computing, and advanced
communication technologies that allow for real-time interactions create systems that
are at a complexity far beyond the capabilities of human beings to fully comprehend
and manage. The operation and management of these systems require an extremely
high level of automation that is intelligent.
One benefit of the increasing size and the plethora of interactions leading to the
increase in complexity of the system is that they enable the collection of massive
quantities of data from various components that form the entire system. The
collected data can be integrated into models and utilized to gain profound insights
that can be used to improve and customize the system's behavior.
The system will ultimately be in a position to learn from the results of its behavior.
The use of machine learning and other techniques from the area of AI are the most
effective way to attain the higher levels of automation needed to handle the
complexity and optimize the performance of systems. A portion of this work is
already in progress in initiatives that support AI in standards development
organizations like the 3rd Generation Partnership Project (3GPP) and the Open Radio
Access Network (O-RAN) Alliance.
Network Intelligence For Industries
AI will likely play an important role in automating new-generation systems across a
range of industries. It is especially relevant in enhancing the degree of autonomy for
telecom networks. This, in turn, allows for transformation in other sectors like
transportation and manufacturing.

2. 1) Zero-touch cognitive networks
Around the globe, CSPs are deploying the fifth generation of 3GPP mobile networks
(in the sense of 5G). They are also preparing for the coming age, which will detect,
compute, understand, think and react to business requirements in a completely
autonomous manner, thus transitioning towards cognitive networks that are
completely non-touch. The next generation of networks will be able to handle the
constant explosion of data generated by the ever-growing number of smart,
connected devices and an array of innovative applications at the edge of the network
and in the cloud, in addition to complex topologies for networks. This will pose new
challenges in the fields of complexity, scaling, security and reliability to the network's
operations, design and deployment. The traditional mobile network is developed and
operated by experts in the telecom industry who heavily rely on their vast knowledge
of the topology of the web & mobile of the subscribers and patterns of usage, as
well as the radio propagation models used to create and manage the policies that
govern the network.
The topologies of 5G are becoming more complicated due to the smaller cell sizes
and the advancement of radio technology. The usage patterns have become less
reliable for humans by themselves, and radio propagation models are more difficult
to compute due to the increase in radio spectrum bands and more dense topologies.
This is why AI is crucial in helping CSPs in designing and running 5G networks. To
advance towards developing a zero-touch framework of cognitive networks and to
think and make decisions with a high degree of automation across complex
dependencies and wide areas, AI will have to be a more integral element of the
networks.
Every local site provides a wealth of information on the condition of various
components, the time, series of events, and details about the context. This data can
be utilized to create models of local behavior, and reasoning is needed to process
the data collected across different sites to derive general system-wide insights. In the
ideal scenario, the information and information gathered at one place could be
utilized on the other sites to provide better forecasts. Network infrastructures are
constantly evolving to accommodate the ever-growing demand for real-time
capabilities by software-controlled data pipelines to adjust the amount, velocity and
variety of real-time data and algorithms that can perform real-time decision-making.
The addition of more intelligence to the services and networks and business
processes will enable the transition to a data-driven approach, allowing for a greater
level of automation, performance, and efficiency. With an increase in autonomy, the

3. job of the CSP in managing the network will shift by managing the network using
intentions and overseeing automation, taking control. The intelligent features are
tailored for each service, allowing them to function in a more robust, secure, reliable,
and trustworthy way, bringing mobile networks to a whole new degree of
technological innovation to benefit society and industry.
2) Industry Automation: How are different industries linked
together?
The transformation toward Industry 4.0 is well underway. The information generated
by connected units of production and items used in production provides the
efficiency and flexibility that was impossible before. Anomaly detection and root
cause analysis is used to eliminate obstacles and boost production yield. Predictive
algorithms provide insights to maintenance plans and improve the efficiency of the
process.
5G mobile systems are currently being created based on requirements specifically
designed to the needs of industries. They will also include support for bounded
latency with high reliability. This will aid in the change.
For instance, control logic for production units placed in rugged machines at the
factory floor can be using a 5G connection transferred to an IT infrastructure that is
already in place and becomes an integral component of manufacturing operations
control.
By delegating SW devices, wireless connectivity allows for greater flexibility, improves
efficiency, and eliminates the requirement for the device's SW management. Mobile
connected devices such as AGVs and robots managed by an infrastructure based on
edge clouds could enable complex automated production in real-time, supported
by AI cloud technology. Another instance is the introduction of AR/VR devices that
are wirelessly connected. Specialists can remotely help local maintenance and even
supervise delicate processes using feedback haptic. Furthermore, when you have SW
removed by the gadget, the battery lasts longer, and the user will benefit from the
most powerful AR/VR apps.
Even though AI promises to automatize and connect industries allowing for
disruptive innovations, it comes with a host of problems: data is usually diverse and
heterogeneous, and there are a lot of hard demands on autonomous decision-
making in real-time. In addition, the safety cruciality of various applications in the
field that occur in human-machine collaboration puts high standards for the
requirements and increases the difficulty.

4. 3) AI on Wheels: AI in the transportation and automotive
industries
Innovations in the transportation and automotive industries have led to the creation
of vehicles that feature increased levels of automation. They are designed for
different uses, including the transport of goods and people and the management of
utility and infrastructure. They are also connected with various operational areas,
ranging from restricted controlled regions (for instance, harbors, mines, urban zones,
etc.)) to vast public roads.
Connectivity plays a variety of roles according to time and geographic scale. Sharing
can lead to enhanced situational awareness and agreements-seeking interactions
between vehicles from the perspective of single-vehicle real-time data. Additionally,
(edge)-cloud-based AI instances can analyze real-time data from roads infrastructure
and off-board sensors to monitor and enhance the capabilities of vehicles provided
by its sensors mounted on its board. In addition, the massive amount of sensor data
produced by cars could be centrally utilized, even offline, to constantly improve
operational safety and efficacy.
Data analytics and connectivity are essential for system-level traffic optimization. This
involves various temporal and geographical time frames based on optimization,
ranging from local traffic adjustments to account for unexpected events in the local
area and long-term flow optimizations. These optimizations benefit from the ever-
growing real-time information accessibility within the connected IoT ecosystem,
gaining data across different domains to assess the condition of connectedness,
mobility and infrastructure.
Safety is a major issue for the automotive and transportation industries. While
sensors onboard will continue to play a vital role in ensuring security on public roads,
central connectivity and analytics are becoming a critical component to ensure safe
operations for vehicles in limited areas and road safety for cars and systems on the
public. Artificial intelligence-based prediction of networks, sensors off-board, and
system performance will be an essential component of automated vehicle
operations. Automation is critical to speedy failure detection and mitigation
throughout the system. In essence, the safety of the vehicle design should consider
cloud and network-based functions and techniques built on data will be a part of the
solution, creating an efficient and safe system.
It is well-known that AI is useful to nearly all industries and helps achieve efficiency
by utilizing intelligent and adaptive automation. The above examples illustrate
various aspects that connect AI across large industries.

5. A Solution Oriented Approach
Five major issues must be resolved to ensure that the public accepts AI as a practical
option to the intelligent automation of complex systems that operate close to real-
time. Solutions will require strong domain expertise and profound knowledge of
fundamental connectivity and communication issues.
1) Self-Aware Systems: that take decisions on their own
Zero-touch operations imply a lesser amount of human involvement and, therefore,
an increase in autonomy for the network to handle the ever-growing complexity of
the system's scale and reduced time to make a decision. One of the most important
advantages of zero-touch processes is their auto-adapting, self-learning automation,
in which unforeseeable situations, intentions and demands are addressed with no
human intervention.
The transformation of network operations where decisions are made by humans and
then carried out by machines, or those in which decisions are made entirely by
machines, requires these machines to comprehend the scenario and link it to
background knowledge. Devices must be given greater autonomy by shifting beyond
the imperative (what should be done) towards declarative (what to accomplish)
objective specifications and also provide them the ability to provide relevant
knowledge of the domain. These are referred to as declarative accurate specifications
intentions. Intents may be used to express functions, functional and nonfunctional,
and objectives, limitations and requirements.
Knowledge-intensive systems employ formal models to understand situations and
perform actions based upon autonomously made decisions. These models can be
obtained through various methods: learning from data through the machine-learning
(ML) method, which is provided by experts in the domain or discovered automatically
through an inference process that involves logical reasoning.
Utilizing a traditional approach to data science, which is training a model by
analyzing all available data, is not without its drawbacks. The first is that a
conventional method might not scale and be unattainable. The state vector could be
filled with many options, and combinations of scenarios to train at inferencing times
could be many. Additionally, this method cannot handle unpredictable situations
since there isn't any data for training on. Instead, we must split the problem into
smaller ones and organize a range of more specific methods than employ an agent-
based method. Agents could be conventional models, ML components, and expert-
level rules, and the list goes on. Agent orchestration can be controlled by a

6. continuous optimization loop that tries to connect the gap between the actual and
desired (specified through intent) states based on the system's understanding of the
state of the network.
Hybrid strategies can be beneficial in the next generation of intelligent systems. The
solid modeling of intricate models can be paired with symbolic logic, which offers
knowledge representation, reasoning and explanation capabilities. The knowledge
could include the universal law of physics or the most well-known techniques in the
particular domain.
Intelligent systems should be equipped with the capability to take decisions
independently to meet goals, solve a problem in various ways and have flexibility
when making decisions using a variety of pre-populated and acquired information.
2) Intelligence is distributed and decentralized.
In a vast network, the decisions have to be taken at various levels and locations.
Certain decisions are based upon local data and are governed by tightly controlled
loops of control with low latency. Others are more strategic, affect the entire system,
and use data gathered from various sources. These decisions taken at a global scale
may require immediate responses in urgent situations like power grid malfunctions,
cascading faults of nodes or other such issues. The technology used to automate
these massive and complicated systems should be able to reflect the distributed
nature of their systems and support the topology of management.
Data generated at the edge, either in an edge node of a network or device, may need
to be processed locally. It is not always possible to transfer data to a centrally located
cloud, and there could be laws regarding the location of data and security or privacy
concerns regarding data transfers. The scope of the decisions made in these
instances is restricted to a limited area, so the algorithms and computing capabilities
required are typically light and fast. But local models can be based on insufficient
and biased data that could lead to the loss of efficiency. It is necessary to take
advantage of the magnitude of distribution, create an appropriate abstraction of
local models, and then transfer the insights to other models local to the model.
Learning about global patterns in data through multiple networked devices or nodes
that don't have access to the data is also feasible. Federated learning has helped
pave the way in this direction, and more distributed training patterns like vertical
federated or split-learning have come into play. The new structures allow machine-
learning models to modify their implementations to the demands they will have to
meet concerning data transfer or computation and for the consumption of network

7. resources and memory with excellent performance assurance. However, further
research is required, particularly to accommodate different types of models, model
combinations, and stronger privacy assurances.
A common, decentralized and distributed model is needed to make the most
efficient use of global and local models and data and determine the best way to
disperse reasoning and learning across nodes to meet the extremely high
requirements for latency. The paradigms themselves can be developed with machine
learning and other AI techniques that incorporate characteristics of self-
management, auto-optimization and self-evolution.
3) Trustworthy AI
AI-based autonomous systems are complicated algorithms and models, and, in
addition, they develop over time, incorporating new information and data without
the need for manual intervention. Dependence on information and complex
algorithms and the risk of unanticipated behavior from AI-based systems calls for
new approaches to ensure transparency, understandability, technological robustness
and security in data governance and privacy, and fairness and nondiscrimination,
oversight by humans as well as environmental and social well-being and
accountability. These aspects are vital to the human being to be able to comprehend
and thereby build trust that is calibrated in AI-based systems.
The able explanation AI(XAI) is employed to create transparency for AI-based
systems, explaining to the user what the AI algorithm reached the specific conclusion.
The techniques apply to a variety of AI techniques, such as reinforcement learning
(RL), supervised learning (RL), machine reasoning and others . XAI is recognized as an
essential feature in implementing AI models in systems to protect basic rights for AI
users in AI decision-making. It is crucial in telecommunications, where
standardization organizations like ETSI and IEEE insist on the necessity of XAI to
ensure the security of intelligent communication systems.
The constantly evolving character of AI models demands either novel methods or
enhancements to processes in place to ensure the security and reliability of AI
models in both their training and deployment into the actual world. In addition to
the statistical assurances provided by robustness to adversaries, formal verification
methods could be modified to provide deterministic security guarantees for systems
that rely on AI to be safe. Security is among the main factors in ensuring robustness
in which models and data must be secured from attacks by malicious actors. Data
privacy which is the source as well as the intended users, should be protected. The

8. algorithms themselves cannot release private information. In addition, data must be
vetted to ensure fairness and the expectation of the domain due to the bias it may
introduce into AI decision-making.
As the people who use AI systems are ultimately human, techniques such as those
based upon causality and data-proven have to be designed to give the ability to
make decisions accountable. The AI systems must be developed to learn and
improve the stakeholder requirements constantly they're expected to meet and
move to a higher degree of automated decision-making or even human-level if they
don't have enough confidence in certain choices.
4) Human-machine collaboration
Intelligent machines that are connected and of all kinds are becoming more and
more prevalent throughout our lives and range in size from collaborative robotics
and cobots . For proper collaboration, these machines must comprehend human
intentions and needs in detail. Additionally, all information that is related to these
machines needs to be readily available to enable context-awareness. AI is essential
throughout this process to improve the capabilities and cooperation of both
machines and humans.
Modern advances in natural language processing and computer vision have enabled
computers to make more precise interpretations of humans' input. This is achieved
by considering the non-verbal language, for example, the body language and voice
tone. The accurate detection of emotions is evolving and may help identify more
complex behavior, such as fatigue and distraction. Additionally, advancements in
areas like the understanding of scenes and the extraction of semantic information are
essential to fully understand the surrounding environment. The computer should use
the entire perceptual data to identify the most effective method that will maximize
collaboration. Learning through reinforcement (RL) is the process by which a system
is trained to choose the most appropriate action, in light of the present state and
observations of the surrounding environment is gaining more attention. The research
is gaining attention and strategies like secure AI are being investigated to ensure
safety throughout the RL model's life cycle to stay clear of dangerous circumstances.
Information about RL will be described in the following section.
AI has also provided the ability to understand better the way machines work by using
digital twins. Extended Reality (XR) gadgets are more common in mixed reality
systems to show the intricate information about machines and interact directly with
twins in digital form simultaneously. This helps humans understand the way devices

9. operate and allows them to anticipate their actions. Together with the XR interface,
XAI can justify an arbitrary decision taken by machines.
To make collaboration possible, machines must be able to respond and interact with
human beings promptly. Since AI techniques involved in the collaborative system
may be extremely complex in their computing and devices could have limited
hardware resources, A distributed intelligence solution is required to deliver
immediate responses. This implies that the infrastructure for communication plays an
essential role in the entire process by facilitating high-reliability and low-latency
communications networks.
5) Industrialize the gaming technology: Bring games and
simulations to an industrial scale.
RL is a machine-learning method that develops a decision-making model by using
data in a data-driven approach. An RL agent learns through taking actions in the
environment. It also uses its existing knowledge and examines the environment
through random acts to acquire new information. By analyzing the results of such
activities, like rewards, an agent adjusts its policies to maximize some idea of the
cumulative reward.
One of the main factors for success when using RL in a virtual setting is the players'
training to compete against each other and exploring the variety of actions that can
be taken. In this context, it is necessary to conduct more research to determine how
to make the most of the advantages of RL and other cutting-edge algorithms and
tailor their applications to the specific needs of industrial settings and limitations.
One approach is to design an appropriate and safe exploration method that allows
exploration of an environment-controlled and secure way, for instance, by identifying
regions of state-action or temporal slices that are where certain actions are
permitted and limiting the investigation to these areas. Another alternative is to train
an agent within a virtual environment like a simulator emulator, in which the agent is
free to explore the environment without restrictions. This is a method of transferring
into a real environment, also known as Sim2Real. Two of the most recent techniques
for Sim2Real are domain randomization, in which the parameters of the simulation
are randomly selected to facilitate generalized training. The other is domain
adaptation, in which the model trained by simulation is shifted towards the real-
world domain. Another option is offline RL, where the agent is prepared from a static
database. The data should include the trajectories of state, actions, and rewards
generated by a logging strategy. Because the agent can't communicate with the

10. static dataset, one of the major challenges for training is to ensure that they are not
biased towards the dataset, which typically has limited and partial information.
In industrial systems such as telecom networks, it's not always feasible to investigate
every action when the system is operating because an inconvenient action or state
combination could result in lower performance. Suppose more research is conducted
on these methods, which are not exclusive but, in a way, complementary. In that case,
the problem of exploration is possible to solve, and the potential of RL can be
successfully applied in industrial processes.
Conclusion
Based on our research into intelligent mobile networks shortly and continuous
developments in the field of automation in industry, this article identified five
technical issues that need to be tackled to maximize opportunities offered by AI
technologies. The technical issues are:
1. systems that have the ability to take decisions on their own,
2. Decentralized and distributed intelligence
3. an AI that is reliable,
4. human-machine interaction, and
5. making games and simulations available on an industrial scale
These issues are best solved through strategies built on solid domain expertise,
which includes understanding fundamental connectivity and communication issues.