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Emerging Technologies for 6G Communication Networks: Machine Learning Approaches.

Annisa Anggun Puspitasari1, To Truong An1, Mohammed H Alsharif2

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Summary
This summary is machine-generated.

Machine learning (ML) and its derivatives offer solutions for optimizing emerging technologies in sixth-generation (6G) wireless networks. This study surveys ML, deep learning (DL), and reinforcement learning (RL) algorithms for 6G advancements.

Keywords:
6G visions and requirementsdeep learning (DL)emerging technologiesmachine learning (ML)reinforcement learning (RL)sixth generation (6G) communicationwireless communications

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Area of Science:

  • Telecommunications Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Sixth-generation (6G) wireless networks promise ultra-reliable low latency communication (URLLC), high data rates, and integrated sensing, building on fifth-generation (5G) success.
  • Emerging technologies like intelligent reflecting surface (IRS) and unmanned aerial vehicles (UAVs) present challenges in optimizing for 6G's demanding requirements.
  • Conventional mathematical approaches struggle with the complexity of optimizing these new technologies for 6G.

Purpose of the Study:

  • To provide a comprehensive overview of machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms for 6G.
  • To address the research gap concerning the application of these AI algorithms in the context of 6G.
  • To examine how ML algorithms can optimize emerging technologies for 6G network requirements.

Main Methods:

  • Literature review and survey of existing research.
  • Analysis of machine learning, deep learning, and reinforcement learning algorithms.
  • Exploration of their application in conjunction with emerging 6G technologies like IRS, UAVs, and NOMA.

Main Results:

  • ML algorithms and their derivatives are identified as a viable solution for optimizing complex 6G system functionalities.
  • The study highlights the potential of AI in addressing challenges posed by emerging technologies for 6G.
  • Integration of ML with emerging technologies is crucial for achieving 6G network visions.

Conclusions:

  • Machine learning, deep learning, and reinforcement learning are essential for overcoming the challenges in 6G wireless communication.
  • The synergy between AI algorithms and emerging technologies will drive the development and success of 6G networks.
  • This research underscores the critical role of AI in realizing the ambitious goals of 6G.