DAI Framework in D2D 5G communication. The DAI framework is realised in an efficient, distributed, autonomous and flexible way, and it focuses on the local environment rather than the global environment. It is implemented using a BDI extended agent (BDIx agent) that consists of Beliefs (knows the environment)\footnote{Beliefs can also comprise part of other forms of AI/ML techniques (e.g. Fuzzy Logic, Deep Learning Neural Networks etc.)}, Desires (what it has as objective) and Intentions (current running objectives realised from Desires that are executed through selected plans). Desires can become Intentions using priority values and well-defined IF-THEN rules in the Fuzzy Logic Plan Library according to the Requirements based on values from Sensors, violation of pre-specified thresholds and raise of Events. More specifically, changes in values of Beliefs or incidents that trigger an event act as reinforcement learning and enable the reactive behaviour of an agent that changes Desires, Intentions and plans to be executed.

1. A novel Distributed AI
framework with ML for
5G/6G communication
1
Iacovos Ioannou*, Christophoros Christophorou,
Vasos Vassiliou, Andreas Pitsillides**
*work stemming from Iacovos PhD Thesis
University of Johannesburg, March, 2022
**Dept. of Computer Science, University of Cyprus &
University of Johannesburg (Visiting Professor)

2. The existing mobile network challenges:
i) huge bandwidth demands
ii) infrastructure and technologies cannot
fulfill data rate and connections needs
iii) new technologies complicate 5G/6G
management (e.g. Softwarisation Augmented Reality
/Virtual Reality /Autonomous Vehicles)
2
Need to meet 5G requirements:
 high data rates
 low latency
 low energy consumption
 high scalability
 improved connectivity and
reliability
 improved security
And the more demanding 6G
requirements:
 higher data rates and lower
latency
 mass connectivity
 flexibility in diverse services,
 flexibility in managing and
controlling diverse networking
equipment, including softwarised
network elements and even flat
infrastructure (no BSs)
Problem Statement and Motivation
propose a Distributed AI Framework capable to tackle these diverse
needs and requirements

3. i) a fully decentralized control with virtual
resources
ii) a distributed control and independent
and autonomous systems (e.g. no dependence
on BS)
iii) to be adaptable and flexible in any
situation (support dynamic environments)
iv) AI/ML everywhere (from centralized computing
facilities to every terminal in the network)
4
NEW 5G/6G Requirements demand

4. Enabling technologies for
emerging 6G/WLANS
5
Pervasive
Artificial
Intelligence /ML
one of the key enabling technologies for 5G/6G
AI/ML is widely used to solve problems in diverse
applications of computer science

5. Objective
o develop a Distributed AI (DAI)
framework that will act as a glue with
other AI/ML techniques utilized by Mobile
Devices to
o satisfy 5G/6G requirements
o support Self-Organizing Network
o be Adaptable, Autonomous, Dynamic
and Flexible
Ioannou, V. Vassiliou, C. Christophorou and A. Pitsillides, "Distributed Artificial Intelligence Solution for D2D Communication in 5G
Networks," in IEEE Systems Journal, vol. 14, no. 3, pp. 4232-4241, Sept. 2020,.
I. Ioannou, PhD Thesis, A novel Distributed Artificial Intelligence framework with Machine Learning for 5G/6G communication, Sept
2021, Computer Science, University of Cyprus
6
BDI agents-based DAI are a promising technology
worth investigating to realise above

6. BDI agents-based DAI
• BDI agents-based DAI as a concept is based
on
– intelligent agents that manage
their knowledge, abilities, capabilities
and intents/plans
to perform actions
with objective to solve a problem
by collaboration or as individual entities
7
Amato, A., & Venticinque, S. (2014). A distributed agent-based decision support for cloud brokering.
Scalable Computing, 15(1), 65–78. https://doi.org/10.12694/scpe.v15i1.966

7. Why BDI (Belief-Desire-Intention) agents?
because of their unique features
– Beliefs: correspond to the informational state of the agent
• can include inference rules + allows advance chaining to guide to new beliefs
– Desires: correspond to the motivational state of the agent
• characterize objectives or situations that agent would like to fulfil
– Intentions (what): correspond to the deliberative state of the agent
• This is what agent chose to perform by executing a plan.
8
BDI agents-based DAI
These unique attributes contribute to the achievement
of desirable DAI Framework performance
Note: Intentions are desires to which the agent has commitment, and a goal is a
desire (converted to intention) that has been adopted for active pursuit by the agent

8. Extended BDI agents (BDIx agents)
BDIx agent
• can utilize in Beliefs any other AI/ML for better
understanding of surrounding environment to agent
• can prioritise the order of execution of the Desires
– Desires may depend on the completion of others to
start/conclude, thus priority values are assigned to control
their execution
– Plan Library must also be used for controlling the execution
of Desires converted to Intentions, and thus restrict agent
deliberation (note: Intentions can change at runtime and anytime)
9
I. Ioannou, PhD Thesis, A novel Distributed Artificial Intelligence framework with Machine
Learning for 5G/6G communication, Sept 2021, Computer Science, University of Cyprus

9. A BDIx-based DAI Framework
10
• can achieve specific task/requirement in 5G/6G through
agent communication
• uses targeted modules within the BDIx agents-based DAI
framework
• modules can be substituted or added as needed (extra AI/ML
models)
• offers intercommunication and collaborative decisions
• by using well-defined messages of BDIx agents in the
framework
• can use many predefined well-structured languages for
BDI agents communication (e.g. FIPA ACL*)
• including “propose”, “notify”, and “inform” types of messages
*http://www.fipa.org/repository/aclspecs.html

10. constituent components, include:
– i) Beliefs
– ii) Desires
– iii) Plans, which are associated to the Desires
– iv) Threshold values
– v) Events
– vi) Plan library: handles priority of Desires to
become Intentions
• can be added or removed at run time as long as they are
not used by the BDIx agent during the pursuing of Intentions (even if used in
Intentions agent can still reset its states with an update)
11
BDIx-based DAI Framework

11. The implemented BDIx Agent
Architecture
12
I. Ioannou, PhD Thesis, A novel Distributed Artificial Intelligence framework with Machine Learning for
5G/6G communication, Sept 2021, Computer Science, University of Cyprus

12. BDIx-based DAI Framework
• Many appealing features, including:
– Modularity, distributed AI, Multitasking
Execution, Autonomicity, Dynamicity,
Collaborative Environment, Logging of User
Actions, Flexibility, DAI, Security,
Environmental Representation, Light Execution,
Deliberation (details appear here)
• Architectural Characteristics of BDI Agents
– Persistence coefficients, Priority values,
Flexibility, Responsiveness, Reactivity (details
appear here)
13

13. BDIx-based DAI framework
14
i) fast network control with less messaging
exchange (hence a reduced signaling overhead with fast decision
making)
ii) support of self-healing mechanisms and to
collaboratively act as a self-organising network
iii) can capitalise on existing mechanisms/
implementations (e.g., ANNs, optimized functions)
offers:

14. D2D as an example of utilization of the
DAI Framework
To better illustrate the concepts of the DAI
framework a D2D setup is considered
In this setup, each D2D device, aims to tackle
the D2D challenges by focusing on the local
environment of D2D communication
15

15. D2D as an example of realisation of the DAI
Framework
• Operates in licensed (inband
D2D) and unlicensed (outband
D2D) spectrum
• Transparent to cellular
network
• allows proximate
devices to bypass BS
and establish direct
links between them
(share their connection, act as
relay stations, or directly
communicate and exchange
information)
I. Ioannou, V. Vassiliou, C. Christophorou and A. Pitsillides, "Distributed Artificial Intelligence Solution for D2D
Communication in 5G Networks," in IEEE Systems Journal, vol. 14, no. 3, pp. 4232-4241, Sept. 2020,
D2D communication to play a major role in the realization of 5G/6G
16

16. Challenges in D2D
• Many challenges, including:
– Device discovery
– Mode selection
– Interference management
– Power control
– Security
– Radio resource allocation
– Cell densification and offloading
– QoS / Path Selection
– mmWave
– Handover of D2D device
17
I. F. Akyildiz, S. Nie, S. C. Lin, and M. Chandrasekaran, “5G roadmap: 10
key enabling technologies,” Comput. Networks, vol. 106, pp. 17–48, 2016

17. Implementation Specifics for Meeting
the D2D Requirements
• D2D challenges implemented with Plans*
associated with the related Desires
• In addition, some D2D challenges
–handled when specific network Events
are raised, and/or
–some Beliefs change due to Sensor
readings or raised Events
18
*The PhD thesis outlines a plan for each of the D2D challenges
I. Ioannou, PhD Thesis, A novel Distributed Artificial Intelligence framework with Machine Learning for
5G/6G communication, Sept 2021, Computer Science, University of Cyprus

18. recall, BDIx agent characterised by:
–components
• Beliefs
• Desires
• Intentions and Goals
–and its behaviour
• Perception
• Plan
19
Implementation Specifics for Meeting
the D2D Requirements

19. 20
To handle D2D communications with BDI Agents
BDIx agent reasoning should be created.
Illustrative sets of BDIs:
– Beliefs:
• Channel, transportation mode (Relay/Cluster/Relay hop), technology
used (Wi-Fi/Bluetooth/ mmWaves), number of interfaces, location,
neighbor agents, maximum radius, next hop (D2D relay or BS channel),
Pool of assigned frequencies and if it is a D2D relay
– Desires:
• Find best Transmission Mode (Data Rate is acceptable) that
achieves the best achievable Signal Quality, Data Rate and WDR,
Data Rate is acceptable, Signal quality is acceptable, WDR is
acceptable, Maximum Sum Rate is achieved
– Intention:
• when Desire “Find best Transmission Mode that achieves the best
achievable Signal Quality, Data Rate and WDR” has priority 100%
is converted to Intention and it is moved in Goals. In the Goals
the Intention will run the DAIS as in Plan.
Distributed AI Solution (DAIS) for D2D
communication in 5G networks

20. DAIS Plan For Transmission Mode Selection: new device entering
21
I. Ioannou, V. Vassiliou, C. Christophorou and A. Pitsillides, "Distributed Artificial Intelligence Solution for D2D
Communication in 5G Networks," in IEEE Systems Journal, vol. 14, no. 3, pp. 4232-4241, Sept. 2020
Example plan
Step 1. Desire "Device Discovery" becomes an
Intention
Step 2. Once fulfilled, priority value of related Desire set
to 0% while rest of Desires increased
Step 3. Desire "Data Rate is acceptable" becomes an
Intention. Related Desire is associated with "DAIS" Plan
which goes through the following steps:
(a) Compute WDR of proximity devices and
select highest
(b) Set Transmission mode as "D2D Client" .
Initiate connection to D2DSHR with
highest WDR.
(c) Request connection to D2DSHR
(d) D2DSHR responds to the request
(e) D2D Client connects to the D2DSHR
Step 4. Once DAIS plan finalized and Intention
achieved, priority value of related Desire set to 0%.
Then another Desire selected, if any, based on priority
values set by Fuzzy Logic rules, to become an Intention

21. • Algorithm
– target maximization of sum rate based on WDR
• jointly solves QOS, Power, Densification and Offloading
challenges
– targets clustering, backhauling formation and
Transmission mode selection (D2D Relay/D2D MultiHop
Relay/D2D Client)
• Benefits
– Energy savings achieved
– Sum Data Rate increased
– QoS/QoE achieved through clustering of neighbor nodes
22
DAIS (Distributed AI Solution) for D2D
communication in 5G networks
I. Ioannou, V. Vassiliou, C. Christophorou and A. Pitsillides, "Distributed Artificial Intelligence Solution for D2D
Communication in 5G Networks," in IEEE Systems Journal, vol. 14, no. 3, pp. 4232-4241, Sept. 2020

22. Evaluation setup and scenarios
• simulation of 10 ≤ N ≤ 1000 D2D Devices
• placed in cell range of 1km radius from BS
using Poisson Point Process distribution
• simulation environment implemented in Java
using specific libraries from Matlab 2020a
"5G/LTE Toolbox” in conjunction with JADE
library
• comparatively evaluated against several
other approaches
23
I. Ioannou, V. Vassiliou, C. Christophorou and A. Pitsillides, "Distributed Artificial Intelligence Solution for D2D
Communication in 5G Networks," in IEEE Systems Journal, vol. 14, no. 3, pp. 4232-4241, Sept. 2020

23. Existing Approaches In D2D that handle
Transmission Mode Selection
Existing approaches:
• separate the UEs into categories of D2D
Devices and regular UEs
• do NOT utilize all transmission modes in a
distributed manner
Our approach:
• considers all UEs as candidates to become
a D2D Device
• utilizes all transmission modes (D2D Relay,
D2D multi-hop and D2D cluster) in a
distributed manner
our approach uses:
• the coordinates of each D2D Device and the distance as metric to examine the achievement of the
link constrains
• the Weighted Data Rate (WDR) for the decision of mode selection in a distributed manner
Weighted Data Rate
(WDR): The Data Rate of the
weakest link in a path towards
BS. Each D2D Device has its
WDR
24
I. Ioannou, V. Vassiliou, C. Christophorou and A. Pitsillides, "Distributed Artificial Intelligence Solution for D2D
Communication in 5G Networks," in IEEE Systems Journal, vol. 14, no. 3, pp. 4232-4241, Sept. 2020

24. gains by using BDIx agents
25
I. Ioannou, V. Vassiliou, C. Christophorou and A. Pitsillides, "Distributed Artificial Intelligence Solution for D2D
Communication in 5G Networks," in IEEE Systems Journal, vol. 14, no. 3, pp. 4232-4241, Sept. 2020

25. Comparative performance evaluation
26
I. Ioannou, V. Vassiliou, C. Christophorou and A. Pitsillides, "Distributed Artificial Intelligence Solution for
D2D Communication in 5G Networks," in IEEE Systems Journal, vol. 14, no. 3, pp. 4232-4241, Sept. 2020

26. Conclusion
• introduced novel DAI framework utilizing ML
through BDIx multi-agents to address the
demanding management and control
challenges in 5G/6G communication networks
• from the D2D implementation example, shown
that the framework
– executes control of the communication
– achieves fast decision in control
– is dynamic and flexible
– achieves a comparatively better solution (in terms of SE and PC)
– reduces messaging exchanges
27

27. Current plans: DAI Framework within the
5G softwarised architecture and security
28
In our architecture we do not have small
cells. Small cells are replaced by D2D-
Relays.
Using Scyther tool protocol shown to be
secure and trustworthy.
Also DAIS Framework can be implemented
in a secure way with this protocol.

28. Future Work
• extensive evaluation using both simulation and a
(small scale) test-bed
• other Plans and Intentions for tackling 5G/6G challenges
• a game theoretic perspective of the BDIx
agents can also be investigated, to form a multi-agent
system in a non-cooperation environment
• framework can be enriched with new
technologies, e.g.
– D2D caching, as well as software-driven Functional
Metasurfaces and BlockChain technology
29

29. Thank you!
Any
questions?
30

30. ADDITIONAL AND LINKED IN
SLIDES
31

31. BDIx-based DAI Framework Main
Features
• Modularity: An authorized user can add or remove Desires at run time and change
the relations between Beliefs, Plan Library and Desires easily.
• Multitasking Execution: Multiple problems can be solved concurrently by the BDIx
agent with the parallel execution of multiple Intentions.
• Collaborative Environment: Through communication with the use of e.g., FIPA ACL
and LTE ProSe, the agents can coordinate and form a collaborative environment
through which they can negotiate the acceptance of a proposal by other agents and
commit to do their proposed task by considering their Beliefs and Desires.
• Logging of User Actions: The BDIx agents can Log user action in order to improve
the QoE.
• Autonomicity: The BDIx agent can act independently in order to solve a problem
and decides for the control of communication without any dependency on
information other than the local information provided by Device Discovery
(Proximity Services).
• Dynamicity: The BDIx agent supports reinforcement learning with the use of sensors
and metrics that measure the environment and updates the Beliefs according to the
representation of the environment in order to react to changing conditions of
operation.
32

32. BDIx-based DAI Framework Main
Features (Cont..)
• Flexibility: is has the ability to adapt to possible, future changes in its requirements
and react fast in a change of a situation with the use of APIs.
• DAI: It is a Distributed Artificial Intelligence (DAI) Control with the use of BDIx
agents.
• Security: Each BDIx agent can utilise well known security techniques (e.g., RSA
encryption, SSL protocol, digital signatures ) assigned in each device as tools to
increase security.
• Environmental Representation: The BDix agents can achieve an accurate
representation of the surrounding environment in the Beliefs with the use of
sensors, variables, simple data structures and with the utilization of high
complicated data structures (i.e., Neural Networks).
• Light Execution: The BDIx agent uses reduced CPU and memory resources for
executing tasks in order to run efficiently on Smartphones and Internet of Things
(IoT) hardware.
• Deliberation: The BDI agents can have an increasing freedom for selecting Desires to
become Intentions. However, in our framework the freedom is slightly restricted by
the Fuzzy Logic rules of the Plan Library of the agent.
33

33. Architectural Characteristics of the BDI
Agents
Architectural Characteristics of the BDI Agents:
– Persistence coefficients: higher persistence to continue current
actions independently and with lower persistence to be adaptable
and reactive but with inconsistent and computational costly
behaviors.
– Priority values: is the characteristic of the agent to determine the
correct intention to be used from a corresponding Desire in case of
a Believe change or the raise of an event.
– Flexibility: is the ability of the agent to easily define and adapt its
Beliefs, Desires and Intentions in real time.
– Responsiveness: is the agent's behavior and responsiveness to
events raised, sensor measured values, and changes in its Beliefs.
– Reactivity: a reactive agent can define a cognitive model and
through this model specify its target challenges along with the
plans that will achieve their implementation.
34
these characteristics are adjusted or extended to achieve
the 5G/6G requirements.

34. Summary of BDI and BDIx agents
differences
35
BDI Agent BDIx Agent
Utilises other AI/ML approaches at Beliefs N Y
Uses Fuzzy logic with priorities values on Beliefs N Y
Filters Sensor Values and Raised Events N Y
Provides REST API to Telcom Operators N Y
Has LEGO Based Components N Y
Provides Concurrent Execution of Multiple Intentions N Y
Provides ACID mechanism for Beliefs N Y
Has an Architecture for the implementation
Simpler
Architecture Y
Has a Flowchart of execution that support the above
Simpler
Flowchart Y
Enforces through the BDIx Interpreter the whole
implementation of the DAI Framework
No Supported
yet Y
Provides additional Features based on the 5G/6G requirements
Specific
Features Y
Adapts the Characteristics to be aligned with the requirements Y

35. Complexity of our approach
• The complexity of our approach is only depended
on the implementation of the Fuzzy Logic
controller and the complexity of the plan that is
currently executed because all the other
components are just Queues and Lists that are
part of an Object (called BDIx agent).
• Fuzzy Logic Engine and its infrequent call we
expect DAI computational load to be rather low
(could be in the order of O(n)), especially if Fuzzy
Logic is implemented as a table with parameters.
36
https://www.sciencedirect.com/science/article/abs/pii/S01650114
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