Understanding LTE and its Performance

The development of mobile communications has traditionally been viewed as a sequence of successive generations. The first generation of analogue mobile telephony was followed by the second, digital, generation. Then, the third generation was envisaged to enable full multimedia data transmission as well as voice communications. In parallel to these activities related to the evolution of current wireless technologies, there is also an increased research effort on future radio access, referred to as fourth-generation (4G) radio access. Such future radio access is anticipated to take the performance and service provisioning of wireless systems a step further, providing data rates up to 100 Mbps with wide-area coverage and up to 1 Gbps with local-area coverage.

The Third Generation Partnership Project (3GPP) Long-Term Evolution/System Architecture Evolution (LTE/SAE) seeks to take mobile technology to the next level through the realization of higher bandwidths, better spectrum efficiency, wider coverage, and full interworking with other access/backend systems. LTE/SAE proposes to do all this using an all-IP architecture with well-defined interworking with circuit-switched systems. Additionally, the evolved 3GPP system introduced a hybrid mobile network architecture supporting radio access technologies and several mobility mechanisms. We begin this chapter by introducing the LTE network reference model and define its various functional entities and its interconnection possibilities. Next, we discuss the end-to-end protocol layering in a LTE network, network selection and discovery, and IP address allocation. Finally, we describe in more detail the functional architecture and processes associated with security, QoS, and mobility management.

The LTE link layer protocols are optimized for low delay and low overhead and are simpler than their counterparts in UTRAN. The state-of-the-art LTE protocol design is the result of a careful cross-layer approach where the protocols interact with each other efficiently. This chapter provides a thorough overview of this protocol stack, including the sublayers and corresponding interactions in between them, referring always to 3GPP specifications.

Physical layer of the radio interface is typically the most important argument when different cellular systems have been compared against each other. The physical layer structures naturally relate directly to the achievable performance issues when observing a single link between a terminal station and a base station. For the overall system performance the protocols in the other layers, such as handover protocols, also have a great deal of impact. Naturally it is essential to have low Signal-to-Interference Ratio (SIR) requirements for sufficient link performance with various coding and diversity solutions in the physical layer, since the physical layer defines the fundamental capacity limits. In this chapter, a detailed description of LTE physical layer while focusing on OFDAM technology is given.

Quality of Service (QoS) is a broad term used to describe the overall experience a user or application will receive over a network. QoS involves a broad range of technologies, architecture, and protocols. Network operators achieve end-to-end QoS by ensuring that network elements apply consistent treatment to traffic flow as they traverse the network.

LTE promises the support of high throughput, low latency, plug and play, FDD, and TDD in the same platform. This will enable better and richer quality of experience for users and the ability to provide sophisticated services and applications such as VoIP, high-definition video streaming, mobile gaming, and peer-to-peer file exchange. The technology in the backhaul network must efficiently support these bandwidth-intensive services guaranteeing quality and adherence to persevere end-to-end SLAs. The technology must support any service from any point to any point at any scale at the lowest cost per bit. LTE has been designed with different QoS frameworks and means to enable delivery of the evolving Internet applications. QoS specifically for evolving Internet applications is a fundamental requirement to provide satisfactory service delivery to users and also to manage network resources.

The aim of future wireless networks is to provide a universal ubiquitous coverage across different radio technologies through a multi-modal Mobile Node (MN), while offering a rich range of services with variable bandwidth and Quality of Service (QoS) anytime and anywhere. These features require connectivity across multiple networks with different radio technologies, over different geographic areas, with access to different types of services. Such connectivity can be provided by the 4G architecture which envisions highly flexible and adaptive integration of diverse radio technologies to support built-in capabilities for seamless interaction in these environments [1].

Within the worldwide beyond 3G cellular network, mobility is here to stay in communication networks. Understanding the essence of mobility makes the mobile network design significantly different – though more complex as well – from fixed communications and creates a lot of potential for provision of completely new kinds of services to end users. One of the main goals of LTE, or any wireless system for that matter, is to provide fast and seamless handover from one cell (a source cell) to another (a target cell). This is especially true for LTE system because of the distributed nature of the LTE radio access network architecture which consists of just one type of node, the base station, known in LTE as the eNodeB.

Long-term evolution networks promise to change the mobile broadband landscape with peak data rates of over 100 Mbps, high-speed mobility, reduced latency, and the support of a variety of real-time applications. However, simply providing LTE coverage is not enough to fulfill indoor service requirements. Therefore, operators need to complement macro network with femtocell deployments more tailored to residential and workplace use. To understand the importance of femtocellfor LTE, it is important to analyze mobile customers’ behaviors and to determine the nature of this demand and more particularly where it occurs. Traditionally, mobile operators’ mission is to deliver services to mobile users constantly on the move which use their mobile phones mainly for voice services. With the emergence of technologies such as UMTS and the Fixed Mobile Convergence (FMC), mobile services usage are changing and new trends are appearing leveraging indoor importance. In such context, high data rates and coverage are the two main ingredients that each operator should offer to remain competitive. However, operators usually fail to provide high quality of services to home users and 45% of home and 30% of business subscribers experience problems with poor indoor coverage [1]. With macro cellular network, it is very difficult for operators to provide high-quality services and cell coverage to indoor users. Indeed, it is nearly impossible for operators to deploy a huge number of outdoor base stations in areas densely populated in order to improve indoor coverage. The above-mentioned concerns emphasize the need of femtocells as indoor solutions.¹

LTE system presents a very challenging multiuser communication problem: Many User Equipments (UEs) in the same geographic area requiring high on-demand data rates in a finite bandwidth with low latency. Multiple access techniques allow UEs to share the available bandwidth by allotting each UE some fraction of the total system resources [1]. One of these multiple access techniques is Orthogonal Frequency Division Multiple Access (OFDMA) which is adopted by 3GPP release 8 due to its flexibility for accommodating many UEs with widely varying applications, data rates, and QoS requirements. Consequently, this would reveal the need for schemes of scheduling and resource allocation in LTE Networks.

Generally, there is a strong motivation beyond the scheduling and resource allocation to improve system performance by increasing the spectral efficiency of the wireless interface, and hence improve the system capacity. However, random fluctuations in the wireless channel preclude the continuous use of highly bandwidth efficient modulation, hence utilizing the Adaptive Modulation and Coding (AMC) technique [2]. Besides, the strategy used to allocate resources in the downlink direction has further impacts on spectral efficiency improvement. Given that the resources in LTE-based OFDMA are represented by slots– the basic unit of resource allocation in time and frequency domain. Thus, in this chapter we are interested on strategies of slot allocation in the downlink LTE networks which are combined with AMC and multiuser diversity techniques.

Long-Term Evolution (LTE) is a new radio access technology proposed by the third-generation partnership project (3GPP) in order to provide a smooth migration toward fourth-generation (4G) wireless systems. The 3GPP LTE uses orthogonal frequency division multiple access (OFDMA) in the downlink. The OFDMA technology divides the available bandwidth into multiple narrow-band subcarriers and allocates a group of subcarriers to a user based on its requirements, current system load, and system configuration.

The 3GPP LTE radio network architecture consists of only one node between the user and the core network known as eNodeB which is responsible to perform all radio resource management (RRM) functions. Packet scheduling is one of the RRM functions and it is responsible for intelligent selections of users and transmissions of their packets such that the radio resources are efficiently utilized and the users’ quality of service (QoS) requirements are satisfied.

Cross-layer resource allocation is promising for future wireless networks. Mechanism of exploiting channel variations across users should be used in scheduling and Medium Access Control (MAC) designs to improve system capacity, fairness, and QoS guarantees. Due to variable data rates and stochastic transmission inherent in channel-aware networks, the issue of cross-layer is becoming very challenging and interesting.

Since LTE is based on OFDMA, decisions to which time slot, subchannel, and power level for communication are determined by the intelligent MAC layer which seeks to maximize the Signal-to-Interference-Ratio (SINR)for every User Equipment (UE). This allows UEs to operate at the maximum modulation rates obtainable given the radio frequency conditions at the UE location. Accordingly, this allows service providers to maximize the number of active users whether they are fixed, portable, or mobile [1].

LTE supports Orthogonal Frequency Division Multiple Access (OFDMA) communication system where frequency reuse of one is used, i.e. all cells/sectors operate on the same frequency channel to maximize spectral efficiency. However, due to heavy Co-channel Interference (CCI) in frequency reuse one deployment, UEs at the cell edge may suffer degradation in connection quality. With LTE, UEs operate on subchannels, which only occupy a small fraction of the whole channel bandwidth; the cell edge interference problem can be easily addressed by appropriately configuring subchannel usage without resorting to traditional frequency planning.

Resource allocation in multi-cell OFDMA networks has been developed in several works using Fractional Frequency Reuse (FFR). However, only few contributions have explicitly taken into account the nature of application being either real time or non-real time. For example, authors in [1,2] proposed dynamic resource allocation scheme for guaranteeing QoS requirements while maximizing the whole throughput of the system. However, both schemes work only for non-real-time application. Qi and Ali-Yahiya [3,4] introduced the Radio Network Controller (RNC)to control a cluster of Base Station (eNodeBs) in the multi-cell OFDMA system and to allocate resources in a distributed way; however, these schemes allocate resources in the RNC without taking into account the reallocation scheme at each eNodeB for coordinating resource according to the FFR. Authors in [5] proposed a local resource allocation the eNodeBs in a random way without taking into consideration the RNC. Thus the eNodeB has not a global view about the adjacent cells in the system, leading to inefficient resource allocation.

The next generation network will be seen as a new initiative to bring together all heterogeneous wireless and wired systems under the same framework, to provide connectivity anytime and anywhere using any available technology. Network convergence is therefore regarded as the next major challenge in the evolution of telecommunications technologies and the integration of computer and communications. One of the important points in this context is the development of mechanisms that are able to support transparent service continuity across different integrated networks through the use of appropriate interworking architecture, handover decision algorithms, context adaptation strategies, etc. The reason is that wireless networks differ in their key functionalities like Quality of Service (QoS) support and service differentiation, access control, or signaling for Authentication, Authorization, and Accounting (AAA).

With the rapid growth of wireless access networks, the great advances in mobile computing, and the overwhelming success of the Internet, a new communication paradigm has emerged, whereby mobile users require ubiquitous access to their services while roaming, preferably without interruption or degradation of their communication quality. One of the research challenges for next generation (NG) all-IP-based wireless and mobile systems is the design of intelligent mobility management techniques that take advantage of IP-based technologies to achieve global roaming among heterogeneous access technologies [1].