In this chapter, the problem of power-aware wireless resource virtualization with device-to-device (D2D) communication underlaying the LTE network was formulated. The problem is a mixed integer non-linear programming (MINLP) problem. Due to the NP-hardness of such problems, it was divided into four linear programming subprobems that were solved to optimality. The resource allocation and sharing problems were solved in [135] while the power allocation subproblems were solved here. Furthermore, two heuristic algorithms that solve the power subproblems were developed . Results showed that the eNodeB can save up to 42% of energy while D2D pairs can save up to 75% while employing this proposed power-aware scheme. Moreover, the heuristic algorithms achieved similar results to the LP solution, proving their efficiency.
Chapter 5
Dynamic Spectrum Management Through
Resource Virtualization with M2M
Communications
5.1
Introduction
The advancement and penetration of cellular technology has led to the increased licensing of wireless spectrum [135]. This has led to an increase in the number of con- sumers for new services such as satellite digital audio broadcasting and wireless Internet access which has caused a dramatic growth in spectrum access demand [135]. It is pro- jected that the number of mobile-connected devices will reach 11.6 billion devices by 2021 with the monthly global mobile data traffic demand reaching 49 exabytes [7]. However, it is becoming more difficult to provide spectrum for new services or expanding existing ones with most of the spectrum already being assigned [135]. Yet, studies performed in the USA show that the problem is often a spectrum access problem rather than the lack of available spectrum, meaning there is unexploited capacity in the spectrum [135]. Moreover, service providers (SPs) are looking for creative solutions to satisfy the grow- ing data services’ demand rate while simultaneously increasing their average revenue per user [135]. This is especially critical for 5G networks where the service requirements are extremely stringent [188].
Wireless resource virtualization (WRV) has been proposed as one potential solu- tion to meet the increasing rate demand in 5G networks [135, 172, 173]. WRV can be defined as the slicing of wireless resources and sharing the physical infrastructure among co-existing networks in a dynamic manner in order to efficiently utilize the available re- sources [135, 172]. WRV can be extremely advantageous to the different SPs. Firstly, it
helps improve the resource utilization due to the SPs sharing them. This in turn reduces the number of idle resources. Secondly, WRV can reduce both the capital expenditures (CAPEX) and operating expenditures (OPEX) by almost 80% and 27% respectively [135, 172]. Additionally, WRV can support higher peak rates due to carrier resource aggregation and radio resource sharing between different SPs. Finally, multi-SP multi- plexing gain is introduced as a result of the increased number of users in the cells [135]. Adaptive resource allocation techniques can further augment the benefits of WRV, espe- cially in orthogonal frequency division multiple access-based (OFDMA) systems such as in LTE-A downlink [135].
Another proposed solution for 5G networks is the adoption of a multiple radio access technology (multi-RAT) heterogeneous networks (HetNets) architecture. This ar- chitecture aims to boost capacity and improve the QoS by leveraging spectrum access across multiple radio technologies [190]. It seeks to aggregate the various radio technolo-
gies into one common converged network that is seamless to the end user. The aim is
to develop resource allocation techniques that can utilize the available resources in the different RATs in an efficient manner. Multi-RATs also open the door to improved per- formance gains through the efficient diversity dimensions utilization such as the spectral, temporal, and frequency dimensions [190]. This includes using multiple technologies such as device-to-device (D2D) communication, LTE/LTE-A, and machine-to-machine com- munication (M2M) technologies (such as Bluetooth, Zigbee,...) among others [191, 192]. However, the management of the available radio resources becomes a challenge in such a scenario. This is because inefficient management of the available resources can nega- tively affect other parameters such as utilization, interference, fairness, complexity, and QoS. Hence, it is crucial to posit appropriate spectrum and radio resource management (RRM) techniques that can efficiently utilize the available resources.
The proposed architecture in this chapter is based on both these concepts. The problem of wireless resource virtualization with M2M communications underlaying LTE- A cellular network is presented and described. To the best of our knowledge, no previous work addresses the topic of M2M communications in the context of resource virtual- ization. The objective is to maximize the overall system throughput by leveraging the benefits of dynamic spectrum sharing between cellular service providers as well as be- tween cellular and M2M communications in a multi-RAT HetNet environment. This is
in an attempt to provide a more efficient management and utilization of the spectrum. In this chapter:
• The technologies used and challenges faced in multi-RAT architectures are briefly
discussed. In particular, the advantages and challenges of efficient spectrum man- agement are highlighted.
• A combined wireless resource virtualization and M2M communication underlaying
LTE-A cellular network architecture is proposed.
• The problem of wireless resource virtualization machine-to-machine communica-
tion underlaying LTE-A cellular network is formulated as an integer non-linear programming problem.
• The performance of the proposed architecture is evaluated using two different algo-
rithms, a decomposition-based algorithm and a greedy-based heuristic algorithm. This chapter is organized as follows: Section 5.2 briefly describes multi-RAT ar- chitecture as well as the common technologies used and challenges it faces. Section 5.3 discusses some of the related work done in the literature. Section 5.4 presents the pro- posed architecture, its system model, and the channel models considered in this work. Moreover, a mathematical description of the problem is given including the objective and considered constraints. Section 5.5 briefly describes the problem and explains how it can be formulated as an integer non-linear programming (INLP) problem. Section 5.6 presents how a decomposition-based algorithm can be used to evaluate the performance of the proposed architecture. Section 5.7 summarizes the greedy-based heuristic algo- rithm used for performance evaluation. Section 5.8 presents the simulation parameters and results. Finally, Section 5.9 concludes the chapter.