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

作　　者：Madiraju Sirisha P.Abdul Khayum 

机构地区：[1]Department of ECE,Jawaharlal Nehru Technological University,Ananthapuramu,Telagana,India [2]Department of ECE,G.Pulla Reddy Engineering College,Kurnool,A.P.,India

出　　处：《Intelligent Automation & Soft Computing》2023年第2期1627-1641,共15页智能自动化与软计算（英文）

摘　　要：In the digital era,the Narrowband Internet of Things(Nb-IoT)influ-ences 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 char-acteristics 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 require-ment 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 uti-lized the entire resource(frequency band)for tackling the unknown dynamics pro-hibitive.The proposed algorithm instantly assigns the network resources per user requirements and enhances selecting the best quality of service metric optimiza-tion.Therefore,it has potential benefits of high scalability,low latency,energy efficiency,and spectrum utility.

关 键 词：DRL NOMA-RSMA nb-IoT mMTC QOS 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]