Streamlining B2B Communication with EAI: Integrating BizTalk, ESB, and Orchestration for Efficient Business Process Management
DOI:
https://doi.org/10.70179/56jxg578Keywords:
Machine learning, 6G, optimization, intelligent wireless, semiconductor innovation, AI-driven networks, wireless communication, deep learning, edge computing, massive MIMO, beamforming, reconfigurable hardware, spectrum efficiency, energy efficiency, network slicing, ultra-low latency, intelligent transceivers, hardware acceleration, silicon technologies, signal processing, cognitive radio, neural networks, adaptive modulation, data-driven design, channel estimation, device scaling, AI- enhanced connectivity, network intelligence, smart antennas, semiconductor scaling, wireless automation.Abstract
Despite the heterogeneity of 6G techniques, machine learning will play a paramount optimization role to spare the customarily
manually optimized process. To optimally exploit the optimization capabilities of ML-driven approaches, development and
transfers of proper network conditions, reduction of training data needs and algorithmic complexity, and accountability and
correctness of the ML-driven approaches are fundamental challenges that need to be addressed. In this chapter, we concisely
survey the state-of-the-art of ML-driven optimization and the related challenges in 6G telecommunications. The general
communication system model is first presented to introduce ML related concepts such as supervised and unsupervised learning.
The most promising ML-driven optimization techniques and formulations are surveyed to offer comprehension of the state-of-
the-art. Both model-driven and data-driven approaches are reviewed with the aim to provide an overall perspective to the reader
on the state-of-the-art. Then, we outline ML challenges such as the need for training data, accountability, and explainability, and
we show how these challenges are especially relevant in a 6G context. We then highlight the ongoing research efforts conducted
to face these challenges tailored for a 6G context. We conclude this chapter by outlining open research problems in the 6G
context. This chapter is targeted to audiences who are interested in ML-driven approaches for telecommunications systems in the
upcoming 6G era.
We focus on the use of machine learning (ML) to devise optimization solutions for future 6G systems. Within the proposed
taxonomy, we then survey the state-of-the-art in ML-driven optimization which is composed of both model-driven (where a
system model is required) and data-driven (for which a model is neither available nor necessary) approaches. Then, we discuss
central ML challenges such as the necessity of supervised data, explainability, and accountability, focused in a 6G context with
perspectives highlighted both from an academic and industry viewpoint. Lastly, we conclude with open problems with the aim of
sparking interest in the academic community.
