Resource Allocation with Carrier Aggregation in Cellular Networks

Carrier aggregation is one of the most distinct features of 4G systems including LTE-Advanced. Given the fact that LTE requires wide carrier bandwidths to utilize such as 10 and 20 MHz, CA needs to be taken into consideration when designing the system to overcome the spectrum scarcity challenges. With the CA being defined in [5], two or more component carriers (CCs) of the same or different bandwidths can be aggregated to achieve wider transmission bandwidths between the evolve node B (eNodeB) and the UE. This feature allows LTE-Advanced to meet the International Mobile Telecommunications (IMT) requirements for the fourth-generation standards defined by the International Telecommunications Union (ITU) [6]. An overview of CA framework and cases is presented in [3]. Many operators are willing to add the CA feature to their plans across a mixture of macro cells and small cells. This will provide capacity and performance benefits in areas where small cell coverage is available while enabling network operators to provide robust mobility management on their macro cell networks.

The user satisfaction with the provided service can be expressed using utility functions that represent the degree of satisfaction of the user function of the rate allocated by the cellular network [1–9]. We assume that the applications utility functions U(r) are strictly concave or sigmoidal-like functions [10–16].

In this chapter, we present a resource allocation with carrier aggregation optimization problem to allocate the eNodeB’s carrier resources optimally among users in its coverage area while taking into consideration the rates allocated to each user from other carriers for cellular system [3–5]. We propose two multi-stage resource allocation with carrier aggregation approaches. The first approach uses distributed (decentralized) multi-stage algorithms to allocate users, under the coverage area of a primary carrier and a secondary carrier, the resources from both carriers. The second approach uses centralized multi-stage algorithms to allocate users the resources optimally from all in band carriers and give each user the ability to select its primary and secondary carriers based on their offered prices in order to provide a minimum price for the allocated resources.

In this chapter, we focus on the problem of radio resource allocation with user discrimination for different scenarios in cellular networks. First, we present a resource allocation with user discrimination approach for spectrum sharing between public safety and commercial users. It is important to have a common technical standard for commercial and public safety users as it provides advantages for both. The public safety systems market is much smaller than the commercial cellular market which makes it unable to attract the level of investment that goes in to commercial cellular networks and this makes a common technical standards for both the best solution. The public safety community gains access to the technical advantages provided by the commercial cellular networks whereas the commercial cellular community gains enhancement in their systems and makes it more attractive to consumers. The National Public Safety Telecommunications Council (NPSTC) and other organizations recognized the desirability of having an inter operable national standard for a next generation public safety network with broadband capabilities. The USA has reserved spectrum in the 700 MHz band for an LTE based public safety network. The current public safety standards support medium speed data which drives the need of new technology to add true mobile broadband capabilities and makes LTE the baseline technology for next generation broadband public safety networks.

Then, we provide a resource allocation with user discrimination optimization framework in cellular networks for different types of users running multiple applications simultaneously. Mobile users are now running multiple applications simultaneously on their smart phones. Operators are moving from single-service to multi-service and new services such as multimedia telephony and mobile-TV are now provided. In addition, different users subscribing for the same service may receive different treatment from the network providers (Ekstrom, IEEE Commun. Mag. 47, 76–83, 2009; Ekstrom et al., IEEE Commun. Mag. 44, 38–45, 2006; Research, Mobile VoIP subscribers will near 410 million by 2015, VoLTE still a long way off, Infonetics Research, California, 2010; Solutions and Networks, Enhance mobile networks to deliver 1000 times more capacity by 2020, Nokia Solutions and Networks, 2013; Intelligence, Smartphone users spending more ‘face time’ on apps than voice calls or web browsing, GSMA Intelligence, 2011; Networks, Device Analyzer: Understanding Smartphone Usage, Computer Laboratory, University of Cambridge, Cambridge, 2011) because of the subscriber differentiation provided by the service providers. In addition, we present an efficient resource allocation with user discrimination framework for 5G Wireless Systems to allocate multiple carriers resources among users with elastic and inelastic traffic. As 5G systems’ expected capabilities have started to take shape, CA is expected to be supported by 5G. Therefore, CA needs to be taken into consideration when designing 5G systems. Beside CA capability, 5G wireless network promises to handle diverse QoS requirements of multiple applications since different applications require different application’s performance (Shen, IEEE Commun. Mag. 50, 122–130, 2012; Frequency spectrum wall chart, Commerce Dept., National Telecommunications and Information Administration, Office of Spectrum Management, 2016; Shenker, IEEE J. Sel. Areas Commun. 13, 1176–1188, 1995). Furthermore, certain types of users may require to be given priority when allocating the network resources (i.e., public safety users) which needs to be taken into consideration when designing the resource allocation framework.

It is better to use the second paragraph of the chapter as an abstract as follows: In this chapter, we introduce an application-aware spectrum sharing approach for sharing the Federal under-utilized 3.5 GHz spectrum with commercial users. In our model, users are running elastic or inelastic traffic and each application running on the UE is assigned a utility function based on its type. Furthermore, each of the small cells’ users has a minimum required target utility for its application. In order for users located under the coverage area of the small cells’ eNodeBs, with the 3.5 GHz band resources, to meet their minimum required quality of experience (QoE), the network operator makes a decision regarding the need for sharing the macro cell’s resources to obtain additional resources. Our objective is to provide each user with a rate that satisfies its application’s minimum required utility through spectrum sharing approach and improve the overall QoE in the network. We present an application-aware spectrum sharing multi-stage algorithm that is based on resource allocation with carrier aggregation to allocate macro cell permanent resources and small cells’ leased resources to UEs based on a utility proportional fairness policy, and allocate each user’s application an aggregated rate that can at minimum achieve the application’s minimum required utility

In this chapter, we consider an LTE-Advanced cellular system sharing the 3550-3650MHz band with aMIMO radar [13]. The LTE-Advanced cellular system has NBS base stations. In order to mitigate radar interference, a spectrum sharing algorithm is proposed. The algorithm selects the best interference channel for radar’s signal projection to mitigate radar interference to the ith BS. We consider a MIMO collocated radar mounted on a ship. Collocated radars have improved spatial resolution over widely spaced radars [14]. The LTE cellular system operates in its regular licensed band and shares the 3:5 GHz band with a MIMO radar in order to increase its capacity such that the two systems do not cause interference to each other. We focus on finding an optimal solution for the resource allocation with carrier aggregation problem to allocate the LTE-Advanced BS/eNodeB and the available MIMO radar resources optimally among users subscribing for a service in the cellular cell coverage area. Each user is assigned a utility function based on the application running on its UE. Real-time applications are represented by sigmoidal-like utility functions whereas delay-tolerant applications are represented by logarithmic utility functions. Real-time applications are given the priority when allocating resources. A resource allocation with carrier aggregation algorithm is proposed in this chapter to allocate the LTE-Advanced eNodeB and the MIMO radar resources optimally among users. The proposed algorithm is performed in two stages, the LTE-Advanced eNodeB resources are first allocated to users subscribing for a service and then the available MIMO radar resources are allocated to the same users. The algorithm employs a proportional fairness approach in its two stages to guarantee that no user is allocated zero resources and gets dropped.

In this chapter, we focus on solving the problem of utility PF resource block scheduling with CA for multi-carrier cellular networks. The resource scheduling approach presented in (Erpek et al., IEEE International Conference on Computing, Networking and Communications (ICNC) Workshop CCS, 2015; Hou and Chen, 2012 IEEE International Conference on Communications (ICC), 2012, pp. 53485353) does not consider the case of multi-carrier resources available at the eNodeB. It only solves the problem of RB scheduling in the case of single carrier. We introduce an approach for resource block scheduling with carrier aggregation in LTE-Advanced cellular networks. In our model, users are running elastic or inelastic traffic. We use logarithmic and sigmoidal-like utility functions to represent the applications running on the user equipment. Our objective is to assign resource blocks from multiple carriers based on a proportional fairness scheduling policy. In our approach, users are partitioned into different groups based on the carriers coverage area. In each group of users, the UEs are assigned RBs from all in band carriers. We use a utility proportional fairness approach in the utility percentage of the application running on the UE. Each user is guaranteed a minimum quality of service with a priority criterion that is based on the type of application running on the UE.

In this chapter, we summarize the contributions provided in this book and discuss some possible research directions in the future to improve and expand the proposed methods presented in this book.