C. Liu, S. Samarakoon, M. Bennis and H. V. Poor, "Fronthaul-Aware Software-Defined Wireless Networks: Resource Allocation and User Scheduling," in IEEE Transactions on Wireless Communications, vol. 17, no. 1, pp. 533-547, Jan. 2018. doi: 10.1109/TWC.2017.2768358
Fronthaul-aware software-defined wireless networks : resource allocation and user scheduling
|Author:||Liu, Chen-Feng1; Samarakoon, Sumudu1; Bennis, Mehdi1;|
1Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland
2Department of Electrical Engineering, Princeton University, Princeton, NJ 08544, USA
|Online Access:||PDF Full Text (PDF, 0.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019060518445
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2019-06-05
Software-defined networking (SDN) provides an agile and programmable way to optimize radio access networks via a control-data plane separation. Nevertheless, reaping the benefits of wireless SDN hinges on making optimal use of the limited wireless fronthaul capacity. In this paper, the problem of fronthaul-aware resource allocation and user scheduling is studied. To this end, a two-timescale fronthaul-aware SDN control mechanism is proposed in which the controller maximizes the time-averaged network throughput by enforcing a coarse correlated equilibrium in the long timescale. Subsequently, leveraging the controller’s recommendations, each base station schedules its users using Lyapunov stochastic optimization in the short timescale, i.e., at each time slot. Simulation results show that significant network throughput enhancements and up to 40% latency reduction are achieved with the aid of the SDN controller. Moreover, the gains are more pronounced for denser network deployments.
IEEE transactions on wireless communications
|Pages:||533 - 547|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by TEKES under Grant 2364/31/2014, in part by the Academy of Finland project CARMA, in part by the Nokia Foundation, and in part by the U.S. National Science Foundation under Grant CNS-1456793.
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