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Quarterly Journal of Emerging Technologies and Innovations

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Published

2020-01-08

Issue

Vol. 5 No. 1 (2020): JETI-JANUARY-MARCH-2020

Section

Articles

How to Cite

Carrasco Ramírez, J. G. (2020). Integrating AI and NISQ Technologies for Enhanced Mobile Network Optimization. Quarterly Journal of Emerging Technologies and Innovations, 5(1), 11–22. Retrieved from https://vectoral.org/index.php/QJETI/article/view/85

Integrating AI and NISQ Technologies for Enhanced Mobile Network Optimization

José Gabriel  Carrasco Ramírez

Abstract

The relentless progression towards fifth generation (5G) and the impending transition towards sixth-generation (6G) mobile networks are characterized by their ability to deliver ultra-high speeds, minimal latency, and an interconnected web of a myriad of devices. Despite the considerable advancements, this evolution has also introduced an array of complex challenges in network management and optimization. Traditional computational methods are increasingly inadequate in addressing these complexities, paving the way for novel solutions that leverage cutting-edge technologies. This paper proposes an innovative framework that integrates the predictive prowess of Artificial Intelligence (AI) with the quantum computational strengths of Noisy Intermediate-Scale Quantum (NISQ) devices. Aimed at optimizing Radio Access Network (RAN) operations, particularly focusing on the critical Root Sequence Index (RSI) assignment problem, this framework heralds a paradigm shift in network optimization approaches. Our proposed AI-NISQ framework is uniquely positioned to tackle the complexities of RAN by employing AI to predict and analyze network performance and configuration data, translating these insights into Quadratic Unconstrained Binary Optimization (QUBO) problems. NISQ devices, exploiting quantum mechanics, are then utilized to find optimized solutions through quantum annealing—a process otherwise infeasible for classical computing when faced with large-scale optimization problems. The synergy of AI and NISQ technologies is expected to not only improve RSI assignment accuracy but also enhance overall network throughput and reduce latency, leading to a more robust and efficient network infrastructure. This paper presents an empirical evaluation to validate the proposed framework's efficacy over traditional optimization methods, setting a new standard for future RAN optimization tasks.

Author Biography

José Gabriel  Carrasco Ramírez

PhD (c) and Msc in International Law and International Studies.

Caribbean International University, Curazao, Lawyer.

Universidad Católica Andrés Bello, Caracas, Venezuela.

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Submission Guidelines for the Journal of Quarterly Journal of Emerging Technologies and Innovations (QJETI) General Guidelines:

  1. Article Types: We accept a variety of article types including Original Research Articles, Review Papers, Short Reports, and Opinion Pieces.

  2. Language: Manuscripts must be written in English, and should be clear, concise, and free of jargon.

  3. Length: There is no strict word limit, but Original Research Articles should generally be between 4,000 and 10,000 words, including references.

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  5. Abstract: An abstract of 250-300 words is required for all submissions, summarizing the key objectives, methods, findings, and implications of the research.

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  • Title Page: Must include the title, author names, affiliations, and contact information for the corresponding author.

  • Introduction: Provide background, state the problem, and outline objectives.

  • Methods: Detail the methods and tools used in your research.

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Published by Vectoral Publishing, the Quarterly Journal of Emerging Technologies and Innovations maintains high standards in peer-review and scientific inquiry. Authors who wish to submit manuscripts should adhere to the guidelines detailed on our website, www.vectoral.org/QJETI. Vectoral Publishing does not bear any responsibility for the accuracy of information or for any outcomes related to the use of the research published. For editorial correspondence and submissions, please reach out to editorial@vectoral.org.