Name
Abstract | ||
|---|---|---|
Software defined networking (SDN) has the potential to meet the requirements of the next generation traffic and service requirements. It is especially feasible and flexible when combining with small cell networks, which emerges into a software defined small cell networking (SDSCN) framework. SDSCN stands a chance to play a fundamental role in developing future 5G networks. It is particularly a challenging task to deploy dense small cell networks in the presence of dynamic traffic patterns and severe co-channel interference. Based on the SDSCN framework, in this paper, we propose a traffic clustering method to obtain all traffic patterns in a given area and an energy-efficient scheme to deploy and switch on/off small cell base stations (s-BSs) according to the prevailing traffic pattern. The simulation results indicate that our scheme can meet dynamic traffic demands with optimized deployment of small cells and enhance the energy efficiency of the system without compromising on the spectrum efficiency and quality-of-service (QoS) requirements. In addition, our scheme can achieve very close performance compared with the leading optimization solver CPLEX and find solutions in much less computational times than CPLEX. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/DASC-PICom-DataCom-CyberSciTec.2016.18 | 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech) |
Keywords | Field | DocType |
software defined networking,small cell,cell planning,dynamic traffic | Base station,Airfield traffic pattern,Software deployment,Computer science,Efficient energy use,Quality of service,Computer network,Small cell,Solver,Software-defined networking | Conference |
ISBN | Citations | PageRank |
978-1-5090-4066-7 | 0 | 0.34 |
References | Authors | |
13 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Zhou | 1 | 143 | 11.80 |
| Jiao Zhang | 2 | 127 | 9.88 |
| Boon-Chong Seet | 3 | 393 | 40.45 |
| Lei Zuo | 4 | 0 | 0.34 |
| Xiping Hu | 5 | 719 | 56.30 |
| Jiaxun Li | 6 | 30 | 4.12 |
| Wang Shan | 7 | 3 | 2.74 |
| Jibo Wei | 8 | 419 | 44.40 |