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Using a Software-Defined Air Interface Algorithm to Improve Service Quality 被引量:1
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作者 Madiraju Sirisha P.Abdul Khayum 《Intelligent Automation & Soft Computing》 SCIE 2023年第2期1627-1641,共15页
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 th... 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. 展开更多
关键词 DRL noma-rsma nb-IoT mMTC QOS
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