Introduction

This chapter develops the fundamental capacity limits and associated transmission techniques for different cognitive radio network paradigms. These limits are based on the premise that the cognitive radios of secondary users are intelligent wireless communication devices that exploit side information about their environment to improve spectrum utilization. This side information typically consists of knowledge about the activity, channels, encoding strategies, and/or transmitted data sequences of the primary users with which the secondary users share the spectrum. Based on the nature of the available side information as well as regulatory constraints on spectrum usage, cognitive radio systems seek to underlay, overlay, or interweave the secondary users' signals with the transmissions of primary users. This chapter develops the fundamental capacity limits for all three cognitive radio paradigms. These capacity limits provide guidelines for the spectral efficiency possible in cognitive radio networks, as well as practical design ideas to optimize performance of such networks.

While the general definition of cognitive radio was provided in Chapter 1, we now interpret that definition in a mathematically precise manner that can be used in the development of cognitive radio capacity limits. Specifically, in the mathematical terminology of information theory, it is the availability and utilization of network side information that defines a cognitive radio, which we formalize as follows.

A cognitive radio is a wireless communication device that intelligently utilizes any available side information about the (a) activity, (b) channel conditions, (c) encoding strategies, or (d) transmitted data sequences of primary users with which it shares the spectrum.

Motivation for cognitive radios: spectrum is underutilized

Wireless spectrum is one of the most important resources required for radio communications. Throughout the world, spectrum utilization is regulated so that essential services can be provided and also protected from harmful interference. Traditional spectrum governance across the world has tended toward static long-term exclusivity of spectrum use in large geographic areas, often based on the radio technologies employed at the time of decision making. In particular, until recently spectrum regulatory bodies such as the Federal Communications Commission (FCC) in the US or the European Telecommunications Standards Institute (ETSI) in Europe have always allocated spectrum frequency blocks for specific uses, and assigned licenses for these blocks to specific groups or companies.

While the more or less static spectrum allocation strategy has led to many successful applications like, for example, broadcasting and cellular phones, it has also led to almost all of the prime available spectrum being assigned for various applications (see [63]). It may thus seem that there is little or no spectrum available for emerging wireless products and services.

On the other hand, there have been several studies and reports over the years that show that spectrum is in fact vastly underutilized. A report presenting statistics regarding spectrum utilization showed that even during the high demand period of a political convention such as the one held in 2004 in New York City, only about 13% of the spectrum opportunities were utilized [59].

Introduction

Chapter motivation

Chapter 4 introduced the basic spectrum sensing framework for detecting the presence of primary users in a cognitive radio system. In this chapter, we develop this framework further by taking a look at how a cognitive radio system or a cognitive radio network can optimize its spectrum sensing and access functions in order to explore and exploit the spectrum in the most efficient and effective manner. Chapters 1 and 2 described three cognitive radio network paradigms, i.e., the underlay, overlay, and interweave paradigms. In this chapter, we focus almost exclusively on the interweave paradigm in which the secondary users try to exploit the spatio-temporal spectral opportunities (i.e., spectrum holes) resulting from intermittent and uneven spatial and temporal use of the spectrum by the primary users. As described in Chapter 4, these spectrum holes are typically considered to be white spaces, i.e., completely empty of any signals, except for noise. In addition, we will consider a few approaches suitable also for the underlay paradigm. In particular, we will consider game-theoretic techniques and algorithms for spectrum access and sharing in cognitive radio networks that operate under interference temperature constraints, as described in Chapter 4. The remaining third paradigm, the overlay paradigm, is not explicitly considered in this chapter.

Introduction

The fundamental principal of cognitive radio (CR) is to detect other radios in the environment that are using the same spectral resources, and to then deploy transmission and reception strategies that permit secondary users to communicate, while minimizing interference to and from those radios. For the design, analysis, and implementation of such transmission and reception strategies, it is essential to understand the relevant propagation channels [80]. The power emitted by a transmitter (TX) might be determined by the system designer, but it is the channel that dictates how much of it arrives as useful power at the intended receiver (RX), and also how much interference it creates at unintended receivers. Similarly, the signal sensing process of the CR system might be determined by the system designer, but it is the channel that dictates the intervals in time, frequency, and space at which samples should be taken. For example, the temporal variation rate of the channel response dictates how often samples are needed and, thus, how often transmit or receive strategies might have to be adapted. As we will see, many other properties of the channel influence CR design and analysis as well.

Propagation in the cognitive radio bands

CRs may be deployed over a wide range of the frequency spectrum. The bands below about 3.5 GHz have relatively low propagation loss and are sought after by all services. These bands are therefore ideal candidates for the deployment of CR.

Introduction

As discussed in Chapter 2, cognitive radio (CR) aims at maximizing the throughput of secondary users coexisting with primary users under a noninterference or a limitedinterference assumption. Both assumptions require the secondary user to collect cognition about the radio environment, or spectrum sensing. Here the concept of spectrum space is extended to multiple dimensions, such as time, space, frequency, and code, and sensing may include not only detecting and classifying the regions of the spectrum space that can be used by secondary users, but also determining what type of signals are occupying the spectrum, including modulation, waveform, bandwidth, carrier frequency, etc. [50]. All these operations assume that the primary user is not aware of the presence of a secondary user.

Spectrum sensing classifies spectrum spaces as follows:

• white space, one which is completely empty, except for noise;

• gray space, one which is partially occupied by interfering signals;

• black space, one which is fully occupied by communication signals, interfering signals, and noise.

With reference to the CR paradigms categorized in Chapter 2, white spaces are relevant to interweaving, which allows secondary users to operate in spectrum regions that are unused, gray spaces to underlaying, which tries to keep the interference on the primary user at a tolerable level, and black spaces to overlaying, where the primary user transmission is overheard, and signals are processed in a way that makes the quality of this transmission unimpaired by the secondary user.

In this chapter we examine spectrum sensing for application to the first two paradigms.

Introduction to theoretical considerations in wireless systems

This chapter is intended to provide a short summary of the relevant portions of each of the theories that are considered to be fundamental to the development of cognitive wireless systems. Some introductory material on radio-frequency (RF) propagation and device operating constraints is also provided for those not familiar with the relevant device and communications physics. Only those aspects of each area that are utilized in the analysis in the latter chapters are included, so this material should not be considered as even an introduction to any of the four areas. In most cases, the results are stated, and the reader is referred to the references, or further reading, for their derivation, or for a more comprehensive and complete development of each field of study.

Decision theory in cognitive wireless systems

Role of decision theory

One of the principles that we wish to introduce to wireless networking is that nodes have no absolute or mandatory requirement to perform any sensing, protocol reporting, or content-storage requirements. Instead of performing these operations automatically, they are considered to be performed only if the node can justify performing the operation on the basis of the likely performance benefits it achieves, or the performance risks it avoids. Information theory expresses the probabilistic considerations.

Decision-theory literature generally partitions into “normative,” which addresses how rational decisions should be made, and “descriptive,” which describes how humans actually make decisions.

Introduction to wireless system challenges

In this chapter, we will explore some of the fundamental challenges that are common to most, if not all, wireless networking systems and architectures. The intent is to approach the problem in a general framework that can derive meaningful insights into the broad categories of wireless architectures, as well as specific issues associated with specific architectures and designs. Although many of the current wireless architectures are highly specialized and homogeneous, it will be shown that the necessity for increased capability and cost-effective performance, within increasing spectrum constraints, is driving architectures to become more expansive and heterogeneous in their structure. These structures introduce opportunities for optimization across a range of heterogeneous techniques, technologies, and architectures, as well as a requirement for unique optimization methods within each of the homogeneous architectures. This chapter will also introduce some of the fundamental metrics that will be the basis for subsequent analysis and for the development of decision criteria.

Evolution of wireless and mobile architectures

Although cellular and mobile communications are a special case of a wide number of wireless architectures, their impact on popular usage, and society in general, is profound. It is important that their specific trends, and design considerations, be reflected in even the most general treatment of wireless networking.

Not only is spectrum a highly constrained resource, but also energy consumption, real-estate for towers, visual obstruction, and other aspects of the wireless ecosystem are significant considerations in the evolution of wireless technology.