Adaptive Carrier Aggregation with Differentiating Cloud Services for Maximizing Radio Resource Efficiency and Reward Toward 5G Cellular Network

  • Ben-Jye ChangEmail author
  • Shih-Ting Feng
  • Kai-Peng Jhuang


Toward 5G cellular network, to achieve an extremely high data rate and Ultra Reliable Low Latency Communication by using a limited radio frequency spectrum bands becomes a big challenge. 3GPP LTE-A/LTE-A Pro thus specifies the technologies of Carrier Aggregation (CA) and Network Function Virtualization (NFV) to increase the frequency spectrum efficiency and to dynamically allocate the virtualized network component for different classes of requests, respectively. CA can aggregate multiple contiguous or non-contiguous Component Carriers (CCs) and to improve frequency spectrum utilization and signal quality. NFV can dynamically allocate network (virtualized) resource for different classes of services, e.g., human-driven Cloud Computing, machine-driven Internet of Vehicles (IoVs) (e.g., Autonomous Self Driving Vehicle) and sensing-based Internet of Things (IoTs), etc. However, different SINRs of different frequency spectrum bands suffer from the exiting radio nature of CCs. The CA effect and system capacity are thus limited obviously. Additionally, in CC accesses via IoV/IoT, human-driven and machine-driven types communications exhibit different arrival requests and traffic characteristics. Various QoS requirements and traffic distributions of Human and Machine transmissions are certainly different. This paper thus proposes the Cross-Layer scheduling with CC Aggregation (CLCA) toward 5G. CLCA consists of three mechanisms: (1) Markov Decision Process-based cost reward Packet Selection (MDP-PS), (2) Adaptive Packet Scheduling (APS) and (3) Adaptive Component Carrier scheduling (ACC). Numerical results demonstrate that the proposed CLCA approach outperform the compared approaches in system capacity, network reward and packet failure rate.


Toward 5G LTE-A Pro Carrier Aggregation (CA) Carrier Component IoV IoT Network Function Virtualization (NFV) Radio resource allocation 



This research was supported in part by the Ministry of Science and Technology of Taiwan, ROC, under Grant MOST-105-2221-E-224-031-MY2.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and Information EngineeringNational Yunlin University of Science and TechnologyYulinTaiwan, ROC

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