Here we examine two of the most common real-world mesh deployments: firstly wireless cities and secondly community Internet. We show how their reasons for success align with the content presented in earlier chapters in this book. Interestingly, wireless city deployments are targeted at urban areas which already have wired Internet connectivity but where the addition of mobility is valued, whilst in contrast community Internet is targeted at those places where the wired Internet is sparse and connectivity can be added most easily by using wireless to serve fixed locations.

Thirdly, we also show a rising application of mesh networking – vehicular ad hoc networks (VANETs). These systems are targeted at improving road safety and have had spectrum allocated in many countries, and enjoyed success in industrial trials. We expect VANETs to experience particularly strong future growth.

Wireless cities

Several wireless cities are now up and running which provide easy Internet access on the move. In the UK, London and Bristol were early examples, whilst in the USA there is New York, Portland, OR and a rapidly growing number of others. The aim in each case is to enable easy mobile connection to the Internet. This can serve the general public, business users and the city authorities, who may use it for operational purposes, including for public services such as law enforcement.

The wireless nodes are deployed at street level and each includes a normal WiFi access point, so that users may connect with their existing WiFi enabled devices, such as laptops and a growing number of converged cellular-WiFi mobile handsets and PDAs.

To summarise once more, at this point in the book it has been shown that practical mobile meshes are not chosen primarily for spectral efficiency nor for any notion of self-generation of capacity. Meshes should be chosen because they have other benefits. Section 2.2 provided an introduction to how meshes offer coverage benefits, which is possibly their major attribute. In this chapter we revisit our six most likely applications which we have been considering throughout the book. These are

• cellular multi-hopping or WiFi hotspot extension,

• community networking,

• home and office indoor networking,

• micro base station backhaul,

• vehicle ad hoc networks (VANETs), and

• wireless sensor networks (WSNs).

The first five applications are considered in detail in this chapter, whilst wireless sensor networks receive their own treatment in Chapter 10, since they have some unique features. In this chapter, we also look at the barriers to mesh adoption and the time scales likely for them to be overcome.

For the following discussion we find it useful to group the applications into those which form a mesh on the user side and those which form a mesh on the network side, in other words those where the users' nodes themselves mesh together, versus those where only the backhaul forms a mesh. There is one case where the mesh can be for both users and network backhaul; this occurs in VANETs.

Introduction

Cognitive radio is a topic of great interest and holds much promise as a technology that will play a strong role in communication systems of the future. This book focuses on the essential elements of cognitive radio technology and regulation. This is a challenging task in that cognitive radio is still very much an emerging technology. There is much debate over its exact definition, its potential role in communication systems, whether cognitive radios should in fact be permitted in the first place and if yes, what the regulatory policies should be. However, while acknowledging the flux in this field, the book aims to identify the core concepts that will remain central to the field irrespective of how precisely it develops. The aim of this first chapter is to briefly define cognitive radio and to then focus on the all important question of why cognitive radios are needed. This chapter therefore motivates all that is to come in the book.

Brief history and definition

The term cognitive radio was coined by Mitola in an article he wrote with Maguire in 1999 [1]. In that article, Mitola and Maguire describe a cognitive radio as a radio that understands the context in which it finds itself and as a result can tailor the communication process in line with that understanding.

Introduction

To study observations, we return yet again to the definition of the cognitive radio laid out in Chapter 1 and note once more that ‘A cognitive radio is a device which has four broad inputs, namely, an understanding of the environment in which it operates, an understanding of the communication requirements of the user(s), an understanding of the regulatory policies which apply to it and an understanding of its own capabilities.’ Getting these four inputs is what we mean by the phrase ‘observing the outside world’.

We can further detail some of the observations that are needed if we go through the various action categories outlined in the last chapter. To take action from a frequency perspective the cognitive radio must observe which signals are currently being transmitted, which channels are free, the bandwidth of those channels and perhaps whether the available channels are likely to be short lived or more durable. To take action from a spatial perspective, the cognitive radio needs to make observations about the spatial distribution of systems that must be avoided, or the spatial distribution of interferers and of the target radios. The cognitive radio needs to be able to monitor its power output and the power output of other systems. To take action to make a signal more robust or to maximise the throughput of the transmitted signal, the cognitive radio needs to make observations about the signal-to-noise ratio (SNR) at the target receivers, about the bit error rates and about the propagation conditions experienced by the transmitted signal (e.g. delay spread, doppler spread).

Introduction

To discuss regulation and standardisation in the context of cognitive radio is a challenge. Currently there are almost no regulations or standards in place for cognitive radio, as cognitive radios are still very much a thing of the future. Hence this chapter is more about classifying the general types of regulations that may be needed and the standards that are emerging than discussing what is already in place. In reality there is a wealth of regulatory issues that relate directly, indirectly or just ‘kind of relate’ to cognitive radio. Chapter 1 explored the role of cognitive radios in delivering new ways of managing the spectrum and looked at applications in the military, public safety and commercial domains. The new spectrum management regimes and the various potential applications may each give rise to the need for new regulations, some of which are specifically related to cognitive radios and some of which are related to creating the kind of environment in which cognitive radio applications can thrive. The purpose of this chapter, therefore, is to give a broad sense of what those issues might be, as well as to describe the current status of the standardisation efforts.

Regulatory issues and new spectrum management regimes

Much of the discussion about ‘regulations for cognitive radio’ is about ‘regulations for new spectrum management regimes in which cognitive radios can operate’.

Introduction

The first chapter of this book focused on the application areas that will drive cognitive radio technology. This chapter acts as a bridge to the remainder of the book. It seeks to provide the reader with a broad sense of all that is involved in cognitive radio technology. In order to do this we go to the heart of the cognitive radio but not at first using technology as an example. Instead we step back and take a look at how decisions are made in a more abstract manner before returning to the radio world. The final part of the chapter provides a roadmap for the rest of the book.

Setting the scene for understanding cognitive radio

The first question to think about is: how do we make decisions? How do we reason and come to conclusions? We begin this discussion by looking at a simple example.

The lone radio

Scenario 1: I am about to go out and must decide whether I should take an umbrella with me or not. The umbrella is heavy and cumbersome and, while I don't want to get wet, I don't want to take the umbrella with me if it is not necessary.

In this example two actions are possible, namely take umbrella or don't take umbrella. I need to determine how likely it is to rain in order to decide whether to take the umbrella or not.

Introduction

We now reach the ‘decide’ part of the ‘observe, decide and act’ cycle. In very simple terms the decision-making process is about selecting the actions the cognitive radio should take. Using the vocabulary introduced in Chapter 2, it is about choosing which ‘knobs’ to change and choosing what the new settings of those ‘knobs’ should be. Decision-making goes very much to the heart of a cognitive radio.

The decision-making process: part 1

In Table 3.2 a variety of cognitive radio applications and the main highlevel actions associated with them were presented. On examining the table we noted that many of the actions, whether commercial, public safety or military based, centre on two activities:

1. The cognitive radio shapes its transmission profile and configures any other relevant radio parameters to make best use of the resources it has been given or identified for itself, while at the same time not impinging on the resources of others.

2. If and when those resources change, it reshapes its transmission profile and reconfigures any other relevant operating parameters, and in doing so it redirects resources around the network.

A re-examination of Table 3.2 will confirm that these actions are standard throughout a whole variety of applications. It therefore comes as no surprise that two kinds of decisions that regularly need to be made are decisions that map to these two activities, namely decisions about how resources are distributed and decisions about how those resources are exactly used.

During the production phase of this book, the FCC released two reports that are of relevance to this book. At that stage it was too late to include details of the reports in the main body of the text. This short appendix addresses the issues briefly.

On 15 October 2008 the FCC released their report (FCC/OET 08-TR-1005) on the Evaluation of the Performance of Prototype TV-Band White Space Devices Phase II. The opening paragraph of the report summarises what the report shows:

The Federal Communications Commission's Laboratory Division has completed a second phase of its measurement studies of the spectrum sensing and transmitting capabilities of prototype TV white space devices. These devices have been developed to demonstrate capabilities that might be used in unlicensed low power radio transmitting devices that would operate on frequencies in the broadcast television bands that are unused in each local area. At this juncture, we believe that the burden of ‘proof of concept’ has been met. We are satisfied that spectrum sensing in combination with geo-location and database access techniques can be used to authorize equipment today under appropriate technical standards and that issues regarding future development and approval of any additional devices, including devices relying on sensing alone, can be addressed.

The report goes on to state that

All of the devices were able to reliably detect the presence a clean DTV signal on a single channel at low levels in the range of – 116 dBm to – 126 dBm; the detection ability of each device varied little relative to the channel on which the clean signal was applied.

Introduction

In Chapter 1 the working definition for cognitive radio used throughout this book was presented. That definition ended with the statement ‘A cognitive radio is made from software and hardware components that can facilitate the wide variety of different configurations it needs to communicate.’ In this chapter we look at the hardware involved. There is no one right way to build a cognitive radio so the chapter merely aims to give a sense of what kind of hardware can be used and some of the related performance issues.

A complete cognitive radio system

In a cognitive radio receiver, the antenna captures the incoming signal. The signal is fed to the RF circuitry and is filtered and amplified and possibly downconverted to a lower frequency. The signal is converted to digital format and further manipulation occurs in the digital domain. On the transmit side the opposite occurs. The signal is prepared and processed and at some stage is converted from digital to analogue format for transmission, upconverted to the correct frequencies and launched on to the airwaves via the antenna.

Throughout this book we have been using the terms ‘cognitive radio’ and ‘cognitive node’ interchangeably. The reason for this is that a cognitive radio will almost all of the time function as a node in a network. Therefore it is useful to think of the complete cognitive radio system in terms of a communication stack.

Having covered the fundamentals of meshes, we now arrive at the point where we may begin to consider the big and often asked questions about mesh, four of which we consider together, via our list of hypotheses. As a reminder, these are that

1. meshes self-generate capacity,

2. meshes improve spectral efficiency,

3. directional antennas help a mesh, and

4. meshes improve the overall utilisation of spectrum.

We will examine them formally, via analysis of existing peer reviewed publications, followed by some more recent analysis and insight of our own [1, 2]. A key problem in assessing the published literature is that different assumptions are made in different published papers; a direct comparison is thus at risk of being inconsistent. We spend some time at the outset to ensure we avoid this issue.

We will bear in mind that we are predominantly interested in our six application examples of Chapter 2. This will set helpful bounds to our scope for testing the hypotheses.

When we look at Hypothesis 1 which is concerned with capacity, we form our initial viewpoint via a simple thought experiment, which looks at how we expect the capacity of a mesh might behave versus demand, relative to the known case of cellular. This is followed by a summary of four important peer reviewed research papers in the field, which concern system capacity. We contend that the important conclusions presented in these papers were never intended to be used by readers as evidence that a real-world mesh can self-generate capacity.

The aim of using a mobility model is to reflect as accurately as practicable the real conditions themselves. One way to do this is to use motion traces, which are logs of real-life node movements over a representative period of time. There are not many such logs available for use even with established cellular schemes, and none are known to this author which cover mesh environments. The focus then must move to synthetic models. Such a model will deal with a number of nodes and may include parameters such as speed and direction of movement, the ability to pause at some locations and a bound to the model area. The models available are mostly fairly simple to implement, since they are intended for use in simulators where a tractable run time is expected. It is probably the case that present models err on the side of simplicity at the expense of realism. On the other hand, moving too close to the actual environment requires a very specific model – which may then not be adequately representative of all environments. The choice of model is thus a subject which needs to be understood, in order to interpret specific protocol and other simulation results for wider contexts.

Camp et al. [1] review 12 different mobility models which have been applied to mesh simulations at various points in the published literature. Their work is an often quoted indication that the choice of model alone can strongly affect the results when testing the exact same routing protocol. For the purposes of this book three models are noted as being appropriate.

Building upon the fast growing technological advance of video compression in the 1980s, along with the availability of affordable fast computing processors and digital memories in the early 1990s, the evolution in use of digital multimedia broadcasting proceeded rapidly (see Table 6.1). The arrival of digital broadcasting was significant; what was happening was not just a simple move from an analog system to a digital system. Rather, digital broadcasting permits a level of quality and flexibility unattainable with analog broadcasting and provides a wide range of convenient services, thanks to its high picture and sound quality, interactivity, and storage capability. European broadcasters initiated the first attempt to implement a complete direct-to-home satellite digital television program delivery infrastructure having a capacity in excess of 100 channels from a single satellite. This was the digital video broadcasting (DVB) project in 1993, and the main standardization work for satellite (DVB-S) and cable (DVB-C) delivery systems was completed in 1994 [1] [2]. The fixed terrestrial version (DVB-T) was soon added to the DVB family to offer one-to-many broadband wireless data broadcasting based on roof-top antenna and the use of IP packets.

All these DVB sub-standards basically differ only in the specifications to the physical representation, modulation, transmission, and reception of the signal. Digital video broadcasting is, however, much more than a simple replacement for existing analog television transmission. More specifically, DVB provides superior picture quality with the opportunity to view pictures in standard format or wide screen (16:9) format, along with mono, stereo, or surround sound.

The rapid growth of wireless broadband networking infrastructures, such as 3G and 3.5G, WLAN and WLAN-mesh, and WiMAX, makes available multimedia (audio and video) information and entertainment (“infotainment”) in our lives anytime, anywhere, on any device. However, wireless multimedia delivery faces several challenges, such as a high error rate, bandwidth variation and limitation, battery power limitation, and so on. Take, for example, the voice over IP (VoIP) and video streaming applications, which are quite mature in wireline infrastructure. At the same time, wireless broadband based on WLAN and WiMAX is also becoming widespread. While these wireless networks were not designed with real-time multimedia communication services in mind, their widespread availability and low cost makes them an inviting solution for adding mobility to these communication services. The major issue is how to achieve a wireless broadband system which can deliver real-time interactive multimedia smoothly and still satisfy the QoS metrics typically used to define the quality of a VoIP or video conferencing session, e.g., the one-way delay, jitter, packet loss rate, and throughput (see Section 7.2).

Advances in media coding over wireless networks are governed by two dominant rules [1]. One is the well-known Moore's law, which states that computing power doubles every 18 months. Moore's law certainly applies to media codec evolution, and there have been huge advances in technology in the ten years since the adoption of MPEG-2. The second governing principle is the huge bandwidth gap (one or two orders of magnitude) between wireless and wired networks. This bandwidth gap demands that coding technologies must achieve efficient compact representation of media data over wireless networks.