Optimal resource scheduling for energy-efficient next generation wireless networks

Abstract

<p>Cellular networks can provide highly available and reliable communication links to the Internet of Things (IoT) applications, letting the connected Things paradigm gain much more momentum than ever. Also, the rich information collected from the Things with sensing capabilities can guide the network operator to an unforeseen direction, allowing the underlying cellular networks to be further optimized. In this regard, the cellular networks and IoT are conceived as the key components of the beyond-4G and future 5G networks. Therefore, in this dissertation, we study each of the two components in depth, focusing on how to optimize the networking resources for the quality service and better energy-efficiency. To begin with, we study the heterogeneous cellular network architecture which is a major enhancement to the current 4G network by means of the base station (BS) densification and traffic offloading. In particular, the densely deployed short-range, low-power smallcell base stations (SBSs) can significantly improve the frequency reuse, throughput performance and the energy-efficiency. We then study the heterogeneous C-RAN (cloud radio access network), which is one of the core enablers of the next generation 5G cellular networks. In particular, with the high availability provided by the long-range macro BS (MBS), the heterogeneous C-RAN (H-CRAN) can effectively enhance the overall resource utilization compared to the conventional C-RANs. In each study, we propose an optimal resource scheduling and service provisioning scheme to provide a quality service to users in a resource-efficient manner. In addition, we carry out two studies for the Internet of Things (IoT) networks operating with the IEEE 802.11ah standard. Specifically, we introduce energy-efficient device management algorithms for the battery-operated, resource-constrained IoT sensor devices to prolong their lifetime by optimally scheduling their activation. The enhanced power saving mechanism and the optimal sensing algorithm that we propose in each study can effectively improve both the energy-efficiency of the IoT devices and the lifetime of the entire network.</p>