Advanced Wireless Networks (eBook)

Technology and Business Models

Savo G. Glisic (Autor)

eBook Download: EPUB

2016 | 3. Auflage
John Wiley & Sons (Verlag)
978-1-119-09688-7 (ISBN)

Infos

The third edition of this popular reference covers enabling technologies for building up 5G wireless networks. Due to extensive research and complexity of the incoming solutions for the next generation of wireless networks it is anticipated that the industry will select a subset of these results and leave some advanced technologies to be implemented later,. This new edition presents a carefully chosen combination of the candidate network architectures and the required tools for their analysis.  Due to the complexity of the technology, the discussion on 5G will be extensive and it will be difficult to reach consensus on the new global standard.  The discussion will have to include the vendors, operators, regulators as well as the research and academic community in the field. Having a comprehensive book will help many participants to join actively the discussion and make meaningful contribution to shaping the new standard. 

Savo Glisic, Professor of Telecommunications, University of Oulu, Finland
Head of the Networking Research Group, and Director of Globalcomm Institute for Telecommunications. He was Visiting Scientist at Cranfield Institute of Technology, Cranfield, U.K. (1976-1977) and University of California, San Diego (1986-1987). He has been active in the field of wireless communications for 30 years. His research interest is in the area of network optimization theory, network topology control and graph theory, cognitive networks and game theory, radio resource management, QoS and queuing theory, networks information theory, protocol design, advanced routing and network coding, relaying, cellular, WLAN, ad hoc, sensor, active and bio inspired networks with emphasis on genetic algorithms and stochastic geometry. He runs an extensive doctoral program in the field of wireless networking (www.telecomlab.oulu.fi/kurssit/networks/).

Textauszug

1
Introduction: Generalized Model of Advanced Wireless Networks

In the process of evolving towards 5G networks, wireless networks are becoming more complex in both, the number of different functionalities they provide as well as in the number of users they serve [1]. Future 5G networks are expected to be highly heterogeneous (see Chapter 11) and to integrate cognitive network concepts [2, 3] (Chapter 9), heterogeneous solutions for the offload of cellular network traffic to WLANs [4, 5], multi-hop cellular networks (Chapter 8) including combinations of ad hoc (Chapter 4) and cellular networks [6, 7], and mobile to mobile (m2m) communications [8]. In order to analyze and control these networks, evolving towards complex networks structures, efficient modeling tools are needed.

Complex network theory (Chapter 14) has emerged in recent years as a powerful tool for modeling large topologies observed in current networks [9]. For instance, the World Wide Web behaves like a power-law node degree distributed network, wireless sensor networks like lattice networks, and relations between social acquaintances like small world networks. The concept of small world networks was first introduced by Watts and Strogatz [10] where a small world network is constructed via rewiring a few links in an existing regular network (such as a ring lattice graph). Later on, Newman-Watt [11] suggested a small world network constructed by adding a few new links (shortcuts) without rewiring existing links. The concept of small world can be introduced to wireless networks, typically to reduce the path length, and thus provide better throughput and end to end delay.

Several works have addressed the question of how to construct a wireless network topology in ad hoc and sensor networks (Chapter 5) in such a way that the small world feature is preserved [12–16]. Long range shortcuts can be created by adding wired links [17], directional beamforming [18] or using multiple frequency channels [19] concepts. In Ref. [9] it was demonstrated that small world networks are more robust to perturbations than other network architectures. Therefore, any network with this property would have the advantage of resiliency where the random omission of some vertices does not increase significantly the average path length or decrease the clustering coefficient. These features are highly desirable in future wireless networks where the availability of links and nodes can be uncertain. For these reasons, in this book we are interested to redesign heterogeneous wireless networks by including small world properties and frequency channels backups.

The considered network model, that we envision for 5G and further to 6G, includes the multi-hop concept to model future networks with dense user populations and enables mobile to mobile (m2m) connections which are already standardized. We see multi-hop cellular networks as an extension or generalization of the existing m2m concept. The potential users acting as relays may belong to different operators and as such may or may not want to cooperate. Consequently, the existence of those links will be uncertain. Some subareas of the cell will be covered by other technologies such as femto cells, small cells, or WLANs enabling the possibility for the cellular system to offload the traffic. The existence of those links depends on the relaying distance and coverage of the WLAN, as well as the cooperation agreement between the operators. In such a complex network, cognitive links might also be available with limited certainty due to unpredictable activity of the primary user (PU). Complex network theory will be used to aggregate all these characteristics of the network into a unified model enabling a tractable analysis of the overall system performance.

Despite of the extensive work in each of the previous fields, to the best of our knowledge, our book is the first to provide a unified model of the network that will include simultaneously all those technologies. The dynamic characteristics of the network results into a dynamic network topology. The work developed by [20] represents the first attempt to model the link uncertainty by complex networks concepts, although in this work, the uncertainty was a consequence only of fading and dynamic channel access. More specifically, our book emphasizes the following aspects of the design and analysis of complex heterogeneous wireless networks:

1. A unified model for heterogeneous wireless complex networks based on the probabilistic characterization of the node/link uncertainty. The model captures the existence of uncertain and time varying links and nodes inherently present in the latest solutions in wireless networks.
2. Analytical tools for the unified analysis of the multi-operator collaboration, m2m transmission, different traffic offloading options, and channel availability in cognitive heterogeneous networks.
3. Redesign of heterogeneous networks by using specific techniques to systematically add, in a controlled way, network redundancy in order to increase the network robustness to link/node failures.
4. Traffic distribution aware rewiring of the heterogeneous network.
5. A set of new routing protocols for such network.
6. Comprehensive analysis of the network in terms of average path length, clustering, robustness, power consumption, and complexity.

In this introduction we start with a general model of the future wireless network, referred to as generic network model, and later in separate chapters we elaborate in more detail each component of such network.

1.1 Network Model

We start by considering a macro cellular network where users transmit uplink by relaying to their adjacent users (neighbors) on the way to the base station (BS). Multi-hop transmission is modeled by considering a virtual cell tessellation scheme presented in Figure 1.1.1, where the macro cell of radius R is divided into inner hexagonal subcells of radius r < R. This partition is not physically implemented in the network but rather used to capture the mutual relations between the terminals in the cell that are potentially available for relaying each other’s messages. For this purpose, it is assumed that, if available, a potential, ready to cooperate transmitter/receiver is on average situated in the center of each subcell.

Figure 1.1.1 Macro cell tessellation

We assume that within a cell the BS is surrounded by H concentric rings of subcells. For the example in Figure 1.1.1, H = 3. The shortest path (in hop count) between the user location and the BS is given by the hop index h, h = 1, … , H. Due to the terminal unavailability, there may be routes towards the BS where the length of the path is longer than h. The number of subcells per ring is nh = 6∙h and the number of subcells per cell is N = 3H(H + 1).

In the sequel, we present a number of characteristics of heterogeneous networks that lead to the uncertain existence of nodes and links. Node percolation will be used to model and quantify the unavailability of users to relay as a consequence of lack of coverage or terminals belonging to a different operator with no mutual agreement for cooperation. When cognitive links are used, link percolation is used to model the link unavailability due to the return of the PU to the channel. These options will be elaborated in detail in the subsequent subsections.

1.1.1 Node Percolation

1.1.1.1 Multiple Operator Cooperation in Cellular Network

Here we model the scenario where a number of operators coexist in the cellular network. It is assumed that a single operator i has a terminal available in a given subcell with probability . In a multi-operator cooperative network, a terminal will be available for relaying in the same subcell if at least one operator has a terminal at that location. This will occur with probability .

This probability is higher for higher number of operators willing to cooperate. In general, this will result into a reduction of the relaying route length. If the operators cooperate and let their users to flexibly connect to the BS that is more convenient to them, the network capacity of both operators will be improved. Thus, a better performance of the network will be obtained in the multi-operator cooperative scenario, as will be shown later in this chapter. The node unavailability for the message forwarding in complex network terminology is referred to as node (or site) percolation.

1.1.1.2 Multiple Operators in Cooperation with Multiple Technologies

In general multiple technologies will be available in a heterogeneous network. Each technology has its own characteristics which enables more appropriate AP choice at a specific place and time based on the users’ requirements. Figure 1.1.1 shows an example of a cellular network overlapping in coverage with a WLAN. In the analysis, we will be interested to generalize this model as follows. The relative coverage between the cellular network and other access technologies, that is WLAN will be characterized by probability pwlan which is the probability that in the next hop the connection will have the opportunity to make a handoff to a different technology and so, terminate the route. The probability pwlan = A/A is...

Produkt-Details

• Erscheint lt. Verlag: 22.7.2016
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Netzwerke
• Themenwelt: Technik ► Elektrotechnik / Energietechnik
• Themenwelt: Technik ► Nachrichtentechnik
• Schlagworte: 5G • 5th generation mobile technology • Communication technology • Communication Technology - Networks • Drahtlose Kommunikation • Electrical & Electronics Engineering • Elektrotechnik u. Elektronik • Kommunikationsnetz • Kommunikationsnetze • Kommunikationstechnik • Mobile & Wireless Communications • Next Generation Networks • Self organizing Networks (SON) • wireless networks
• ISBN-10: 1-119-09688-X / 111909688X
• ISBN-13: 978-1-119-09688-7 / 9781119096887