**Author details**

*6.2.1 Intelligent data processing*

*6.2.2 Intelligent network optimization*

the environment observations [42, 45].

grated computing heterogeneous platform.

RETIOT (POCI-01-0145-FEDER-016432).

signals [40].

**7. Conclusion**

**Acknowledgements**

**20**

Based on the growing increase in the diversity of F-RAN applications, the envis-

There are a number of ML techniques such as unsupervised, supervised, and RL algorithms that can be employed for efficient network optimization. For instance, as supervised learning focuses on mapping inputs to outputs in accordance with the training samples, the DNN-based supervised learning is an attractive scheme for

beamforming design and power control of fog nodes. On the other hand,

unsupervised learning is based on inferring the underlying data structure without any label, so it is appropriate for empirical analysis such as computation offloading, clustering, and resource allocation, in the F-RAN. Besides, in the RL, to maximize predicted cumulative return, sequential actions are taken by actor/agent based on

In this chapter, we have presented a comprehensive overview of the evolution of computing paradigms and have highlighted their associated features. Moreover, different models that focus on effective resource allocation across an integrated computing platform have been presented. Besides, a comprehensive discussion on efficient resource management and optimization of the 6G fog computing platform to meet strict on-device constraints, reliability, end-to-end latency, bit-rate, and security requirements have been presented. In this context, we have presented AI as a resourceful technique for the achievement of high-level automation in the inte-

This work is supported by the European Regional Development Fund (FEDER), and Internationalization Operational Programme (COMPETE 2020) of the Portugal 2020 (P2020) framework, under the projects DSPMetroNet (POCI-01-0145- FEDER-029405) and UIDB/50008/2020-UIDP/50008/2020 (DigCORE). It is also supported by the Project 5G (POCI-01-0247-FEDER-024539), SOCA (CENTRO-01-0145-FEDER-000010), ORCIP (CENTRO-01-0145-FEDER-022141), and

aged multimedia data to be supported will be heterogeneous, huge, and highdimensional. Therefore, direct raw data transmission to the cloud and fog node will bring about high communication overhead. Besides, direct utilization of raw data for network optimization can cause high-computing overhead and low-efficiency issues. Moreover, there has been considerable advancement in the DNNs that facilitate data processing. For instance, convolutional operations have been exploited by convolutional neural networks (CNNs) for spatial feature extraction from input

*Moving Broadband Mobile Communications Forward - Intelligent Technologies for 5G…*

Isiaka A. Alimi<sup>1</sup> \*, Romil K. Patel1,2, Aziza Zaouga1 , Nelson J. Muga<sup>1</sup> , Qin Xin<sup>3</sup> , Armando N. Pinto1,2 and Paulo P. Monteiro1,2

1 Instituto de Telecomunicações and University of Aveiro, Portugal

2 Department of Electronics, Telecommunications and Informatics, University of Aveiro, Portugal

3 Department of Science and Technology, University of the Faroe Islands, Tórshavn, Faroe Islands

\*Address all correspondence to: iaalimi@ua.pt

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
