Abstract: In order to improve the manageability and adaptability of future 5G wireless networks, the software orchestration mechanism, named software defined networking (SDN) with control and user plane (C/U-plane) decoupling, has become one of the most promising key techniques. Based on these features, the hybrid satellite terrestrial network is expected to support flexible and customized resource scheduling for both massive machinetype-communication (MTC) and high-quality multimedia requests while achieving broader global coverage, larger capacity and lower power consumption. In this paper, an end-to-end hybrid satellite terrestrial network is proposed and the performance metrics, e. g., coverage probability, spectral and energy efficiency (SE and EE), are analysed in both sparse networks and ultra-dense networks. The fundamental relationship between SE and EE is investigated, considering the overhead costs, fronthaul of the gateway (GW), density of small cells (SCs) and multiple quality-ofservice (QoS) requirements. Numerical results show that compared with current LTE networks, the hybrid system with C/U split can achieve approximately 40% and 80% EE improvement in sparse and ultra-dense networks respectively, and greatly enhance the coverage. Various resource management schemes, bandwidth allocation methods, and on-off approaches are compared, and the applications of the satellite in future 5G networks with software defined features are proposed. Index Terms: Coverage probability, 5G, hybrid satellite terrestrial networks, on-off strategy, software defined networks, sparse and ultra-dense networks, spectral and energy efficiency.
An end-to-end satellite-terrestrial network has been presented in REF , by providing a detailed theoretical analysis of the coverage probability and of the spectral and energy efficiencies for different node densities.
Energy efficient hybrid satellite terrestrial 5G networks with software defined features
Journal of Communications and Networks
Journal of Communications and Networks
Computer Science