Open Access

ARTICLE

Using a Software-Defined Air Interface Algorithm to Improve Service Quality

Madiraju Sirisha^1,*, P. Abdul Khayum²

1 Department of ECE, Jawaharlal Nehru Technological University, Ananthapuramu, Telagana, India
2 Department of ECE, G. Pulla Reddy Engineering College, Kurnool, A.P., India

* Corresponding Author: Madiraju Sirisha. Email:

Intelligent Automation & Soft Computing 2023, 35(2), 1627-1641. https://doi.org/10.32604/iasc.2023.025980

Received 11 December 2021; Accepted 23 February 2022; Issue published 19 July 2022

Abstract

In the digital era, the Narrowband Internet of Things (Nb-IoT) influences the massive Machine-Type-Communication (mMTC) features to establish secure routing among the 5G/6G mobile networks. It supports global coverage to the low-cost IoT devices distributed in terrestrial networks. Its key traffic characteristics include robust uplink, moderate data rate/device, extremely high energy efficiency, prolonging device lifetime, and Quality of Service (QoS). This paper proposes a Deep Reinforcement Learning (DRL) combined software-defined air interface algorithm applied on the switching system, satisfying the user requirement and enabling them with the network resources to extend quality of service by choosing the most appropriate quality of service metric. In this framework, Non-Orthogonal Multiple Accesses (NOMA) and Rate-Splitting Multiple Access (RSMA) are combined to accommodate massive (Nb-IoT) devices that can be utilized the entire resource (frequency band) for tackling the unknown dynamics prohibitive. The proposed algorithm instantly assigns the network resources per user requirements and enhances selecting the best quality of service metric optimization. Therefore, it has potential benefits of high scalability, low latency, energy efficiency, and spectrum utility.

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Keywords

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