In many scenarios such as in ad hoc cognitive radio networks, deploying a central controller may not be feasible. Therefore, distributed dynamic spectrum access would be required in such cognitive radio networks. Due to the absence of any central controller, each unlicensed user has to gather, exchange, and process the information about the wireless environment independently. Also, an unlicensed user has to make decisions autonomously based on the available information to access the spectrum, so that the unlicensed user can achieve its performance objective under interference constraints. The common behaviors of an unlicensed user in a cognitive radio network without a central controller are as follows:

1. • Cooperative or non-cooperative behavior: Since a central controller which controls a decision of spectrum sharing is not available, an unlicensed user can adopt either cooperative or non-cooperative behavior. An unlicensed user with cooperative behavior will make a decision on spectrum access to achieve a network-wide objective (i.e. a group objective), even though this decision may not result in the highest individual benefit for each unlicensed user. In other words, an unlicensed user is concerned more about the overall performance of the network than its individual performance. On the other hand, an unlicensed user with non-cooperative behavior will make a decision only to maximize its own benefits (i.e. an individual objective). In this case, the unlicensed user will not be aware of the effect on the overall network performance.

2. […]

Cognitive radios need to have the ability to learn and adapt their wireless transmission according to the ambient radio environment. Intelligent algorithms such as those based on machine learning, genetic algorithms, and fuzzy control are therefore key to the implementation of cognitive radio technology. In general, these algorithms are used to observe the state of the wireless environment and build knowledge about the environment. This knowledge is used by a cognitive radio to adapt its decision on spectrum access. For example, a cognitive radio (i.e. an unlicensed user) can observe the transmission activity of primary (i.e. licensed) users on different channels. This enables the cognitive radio to build knowledge about the licensed users activity on each channel (e.g. how often and how long the channel will be occupied by the licensed user). This knowledge is then used by the cognitive radio to decide which channel to access so that the desired performance objectives can be achieved (e.g. throughput is maximized while the interference or collision caused to the licensed users is maintained below the target level).

Applications of different learning and intelligent algorithms to a cognitive radio system were summarized in [2] (Figure 6.1). These algorithms can be used for learning/reasoning and decision/adaptation in cognitive radio systems. In this chapter, three major categories of intelligent algorithm, namely, machine learning, genetic algorithm, and fuzzy logic based algorithms, are discussed. Machine learning can be divided into three categories: supervised learning, unsupervised learning, and reinforcement learning. The related algorithms and their applications to cognitive radio are discussed in Sections 6.1–6.3.

Dynamic spectrum access (DSA) models for cognitive radio can be categorized as exclusive-use, shared-use, and commons models. In the exclusive-use model, a licensed user can grant an unlicensed user the right to have exclusive access to the spectrum. In a shared-use model, an unlicensed user accesses the spectrum opportunistically without interrupting a licensed user. In a commons model, an unlicensed user can access the spectrum freely. DSA can be implemented in a centralized or a distributed cognitive network architecture. DSA can be optimized globally in a cognitive radio network if a central controller is available. On the other hand, when a central controller is not available, distributed algorithms would be required for dynamic spectrum access. Issues related to spectrum trading such as pricing will also need to be considered for dynamic spectrum access, especially with the exclusive-use model. For DSA-based cognitive radio networks, MAC protocols designed for traditional wireless networks have to be modified to include spectrum sensing and spectrum access, as well as spectrum trading between licensed and unlicensed users.

In this chapter, we describe the different spectrum access models and the system architectures for DSA. Then, two major components of dynamic spectrum access, namely, spectrum sensing and spectrum access, are presented. Spectrum sensing, which can be implemented in both physical and MAC layers, is used to detect the presence of a licensed user. In this case, an unlicensed user observes the target frequency band and searches for a signal from a licensed user. The spectrum sensing result is used by the unlicensed user to access the spectrum without interfering with the licensed user and colliding with other unlicensed users.

Introduction

In a centralized dynamic spectrum access architecture, a central controller is deployed to gather and process information about the wireless environment. With a central controller, the decision of cognitive radio users to access the spectrum can be made such that the desired system-wide objectives are achieved.

In this chapter, we review centralized dynamic spectrum access schemes. A summary of these schemes is provided in Table 8.1. In a centralized scheme, every cognitive radio user communicates with a central controller to inform their states and objectives/requirements. The central controller then makes the decisions in terms of the action for each cognitive radio user to access the spectrum so that their requirements are satisfied under given system constraints. To implement centralized dynamic spectrum access, two approaches, namely, optimization approach and auction-based approach, can be used. With an optimization-based approach, different types of optimization problems can be formulated (e.g. convex optimization, assignment problem, linear programming, and graph theory). Standard methods in optimization theory can then be applied to obtain the optimal solution for dynamic spectrum access. Alternatively, centralized dynamic spectrum access can be designed based on auction theory which is well developed in the field of economics. In this approach, cognitive radio users submit their bids to the spectrum owner. The winning cognitive radio user is determined from the bids, and the spectrum is allocated accordingly.

Optimization-based approach

Quality of service (QoS)-constrained dynamic spectrum access

With spectrum underlay access (i.e. the shared-use model), an optimization problem was formulated by considering QoS differentiation for different unlicensed users and also interference temperature constraints [452].

Frequency spectrum is a limited resource for wireless communications and may become congested owing to a need to accommodate the diverse types of air interface used in next generation wireless networks. To meet these growing demands, the Federal Communications Commission (FCC) has expanded the use of the unlicensed spectral band. However, since traditional wireless communications systems also utilize the frequency bands allocated by the FCC in a static manner, they lack adaptability. Also, many studies show that while some frequency bands in the spectrum are heavily used, other bands are largely unoccupied most of time. These potential spectrum holes result in the under-utilization of available frequency bands.

The concepts of software-defined radio and cognitive radio have been recently introduced to enhance the efficiency of frequency spectrum usage in next generation wireless and mobile computing systems. Software radio improves the capability of a wireless transceiver by using embedded software to enable it to operate in multiple frequency bands using multiple transmission protocols. Cognitive ratio, which can be implemented through software-defined radio, is able to observe, learn, optimize, and intelligently adapt to achieve optimal frequency band usage. Through dynamic spectrum access, a cognitive wireless node is able to adaptively and dynamically transmit and receive data in a changing radio environment. Therefore, techniques for channel measurement, learning, and optimization are crucial in designing dynamic spectrum access schemes for cognitive radio under different communication requirements.

In fact, cognitive radio based on dynamic spectrum access has emerged as a new design paradigm for next generation wireless networks. Cognitive radio aims at maximizing the utilization of the limited radio bandwidth while accommodating the increasing number of services and applications in wireless networks.

In this chapter, a number of different spectrum trading models based on economic theory are presented. In the first model, dynamic competitive spectrum sharing is modeled as a Cournot competition, which is formulated as static and dynamic non-cooperative games. From this competition, given the pricing function adopted by the primary user, the optimal amount of spectrum for secondary users needs to be determined so that the utility of each of the secondary users is maximized. In the second model, competitive spectrum pricing among primary users (or service providers) is modeled as a Bertrand competition where multiple primary service providers sell the available spectrum opportunities to a secondary service provider. The third model is a cooperative pricing model for which spectrum pricing can be obtained as the solution of an optimization model solved by a central controller. Another model is the market-equilibrium pricing model in which there is neither competition nor cooperation among primary service providers. A comparison between market-equilibrium, competitive, and cooperative spectrum pricing is presented. The characteristics of these pricing schemes are qualitatively and quantitatively compared. Also, competitive spectrum pricing in the Bertrand model is formulated as a repeated game to investigate the long-term behavior of the primary service providers. In this case, if a punishment mechanism is used and the primary service providers properly weigh their profits in the future, a collusion can be maintained to achieve the highest profit for all primary service providers. To this end, a hierarchical framework for spectrum trading in IEEE 802.22 WRANs is presented. This framework consists of a double auction model, a non-cooperative game model, and an evolutionary game model.

As the title indicates, the text is intended for persons who are undertaking a study of digital communications for the first time. Though it can be used for self-study the orientation is towards the classroom for students at the fourth-year (senior) level. The text can also serve readily for a beginning-level graduate course. The basic background assumed of the reader is: (i) introductory linear circuit and systems concepts, (ii) basic signal theory and analysis, and (iii) elementary probability concepts. Though most undergraduate electrical and computer engineering students have this background by their final year, the text does include two review chapters which the reader is strongly encouraged to read.

By reading these chapters she/he will obtain a sense of the authors' pedagogical style and the notation used. The notation used is quite standard except (perhaps) in the case of random variables or events. They are denoted (faithfully and slavishly) by boldface. As importantly, because of their importance in digital communications, several topics that may or may not be covered in typical introductory courses, are explained in detail in these chapters. The primary topic is random signals which, after a treatment of random variables and probability concepts, are explained in the necessary depth in Chapter 3. Another topic of importance that typically is not touched on or is treated in only a cursory fashion in an introductory signal course is auto- and crosscorrelation and the corresponding energy and power spectral densities.

The main objective of a communication system is the transfer of information over a channel. By its nature, the message signal that is to be transmitted is best modeled by a random signal. This is due to the fact that any signal that conveys information must have some uncertainty in it, otherwise its transmission is of no interest. When a signal is transmitted through a communication channel, there are two types of imperfections that cause the received signal to be different from the transmitted signal. One type of imperfection is deterministic in nature, such as linear and nonlinear distortions, intersymbol interference (ISI), etc. The second type is nondeterministic, such as addition of noise, interference, multipath fading, etc. For a quantitative study of these nondeterministic phenomena, a random model is required.

This chapter is concerned with the methods used to describe and characterize a random signal, generally referred to as a random process and also commonly called a stochastic process. Since a random process is in essence a random variable evolving in time we first consider random variables.

Random variables

Sample space and probability

The fundamental concept in any probabilistic model is the concept of a random experiment. In general, an experiment is called random if its outcome, for some reason, cannot be predicted with certainty. Examples of simple random experiments are throwing a die, flipping a coin, and drawing a card from a deck.

Introduction

Up to now we have considered only the detection (or demodulation) of signals transmitted over channels of infinite bandwidth, or at least a large enough bandwidth that any signal distortion is negligible and can be ignored. Though in some situations this assumption is reasonable, satellite communications is a common example, bandlimitation is also common. The classical example is the telephone channel where the twisted-pair wires used as the transmission medium have a bandwidth on the order of kilohertz. But even a medium such as optical fiber exhibits a phenomenon called dispersion which results in an effect very analogous to bandlimitation.

It is important to realize that bandlimitation depends not only on the channel medium but also on the source, specifically the source rate, Rs (symbols/second). One common measure of the bandwidth needed or occupied by a source is W = 1/Ts = Rs (hertz). As source rates keep increasing to accommodate more data eventually any channel starts to look bandlimited. Bandlimitation can also be imposed on a communication system by regulatory requirements. A user is usually allotted only so much bandwidth in which to transmit her/his information.

The general effect of bandlimitation on a transmitted signal of finite time duration, Ts seconds, is to disperse it or to spread it out. Therefore the signal transmitted in a particular time slot (or symbol interval) will interfere with signals in other time slots resulting in what is called intersymbol interference (each signal represents a data symbol) or ISI.

Introduction

As pointed out in the previous chapter, binary digits (or bits) “0” and “1” are used simply to represent the information content. They are abstract (intangible) quantities and need to be converted into electrical waveforms for effective transmission or storage. How to perform such a conversion is generally governed by many factors, of which the most important one is the available transmission bandwidth of the communication channel or the storage media.

In baseband data transmission, the bits are mapped into two voltage levels for direct transmission without any frequency translation. Such a baseband data transmission is applicable to cable systems (both metallic and fiber optics) since the transmission bandwidth of most cable systems is in the baseband. Various baseband signaling techniques, also known as line codes, have been developed to satisfy a number of criteria. Typical criteria are:

1. (i) Signal interference and noise immunity Depending on the signal sets, certain signaling schemes exhibit superior performance in the presence of noise as reflected by the probability of bit error.

2. (ii) Signal spectrum Typically one would like the transmitted signal to occupy as small a frequency band as possible. For baseband signaling, this implies a lack of high-frequency components. However, it is sometimes also important to have no DC component. Having a signaling scheme which does not have a DC component implies that AC coupling via a transformer may be used in the transmission channel.

3. (iii) […]

The previous two chapters have reviewed and discussed the various characteristics of signals along with methods of describing and representing them. This chapter begins the discussion of transmitting the signals or messages using a digital communication system. Though one can easily visualize messages produced by sources that are inherently digital in nature, witness text messaging via the keyboard or keypad, two of the most common message sources, audio and video, are analog, i.e., they produce continuous time signals. To make them amenable for digital transmission it is first required to transform the analog information source into digital symbols which are compatible with digital processing and transmission.

The first step in this transformation process is to discretize the time axis, which involves sampling the continuous time signal at discrete values of time. The sampling process, primarily how many samples per second are needed to exactly represent the signal, practical sampling schemes, and how to reconstruct, at the receiver, the analog message from the samples is considered first. This is followed by a brief discussion of three pulse modulation techniques, a sort of half-way house between the analog modulation methods of AM and FM and the various digital modulation–demodulation methods which are the focus of the rest of the text.

Though time has been discretized by the sampling process the sample values are still analog, i.e., they are continuous variables.

Introduction

The main objective of a communication system is the reliable transfer of information over a channel. Typically the information is represented by audio or video signals, though one may easily postulate other signals, e.g., chemical, temperature, and of course text, i.e., the written word which you are now reading. Regardless of how the message signals originate they are, by their nature, best modeled as a random signal (or process). This is due to the fact that any signal that conveys information must have some uncertainty in it. Otherwise its transmission would be of no interest to the receiver, indeed the message would be quite boring (known knowns so to speak). Further, when a message signal is transmitted through a channel it is inevitably distorted or corrupted due to channel imperfections. Again the corrupting influences such as the addition of the ever present thermal noise in electronic components, the multipath fading experienced in wireless communications, are unpredictable in nature and again best modeled as nondeterministic signals or random processes.

However, in communication systems one also utilizes signals that are deterministic, i.e., completely determined and therefore predictable or nonrandom. The simplest example is perhaps the carrier used by AM or FM analog modulation. Another common example is the use of test signals to probe a channel's characteristics. Channel imperfections can also be modeled as deterministic phenomena: these include linear and nonlinear distortion, intersymbol interference in bandlimited channels, etc.

Anytime, anywhere, anything can be taken as the motto and objective of digital communications. Anytime means that one can communicate on a 24/7 basis; anywhere states this communication can take place in any geographical location, at minimum one no longer is tied to being close to one's land line; anything implies that not only traditional voice and video but also other messages can be transmitted over the same channel, principally text, and not only individually but in combination. In large part digital communication systems over the past three decades have achieved the three objectives. Text messaging in all its forms, such as email, internet access, etc., is a reality. Webcasting and podcasting are becoming common. All parts of the globe are connected to the world wide communication system provided by the Internet.

Perhaps, and arguably just as important, a fourth “any” can be added, anybody. Though perhaps not as well developed as the first three, digital communication has the potential to make communication affordable to everyone. One feature of digital circuitry is that its cost, relative to its capability, keeps dropping dramatically. Thus though analog communication could achieve the above objectives, digital communications, due to this increasingly low cost, flexibility, robustness, and ease of implementation, has become the preferred technology.

This text is an introduction to the basics of digital communication and is meant for those who wish a fundamental understanding of important aspects of digital communication design.

This chapter looks at three important modulation paradigms. Trellis-coded modulation (TCM) is considered first. Developed in the late 1970s as a method to conserve bandwidth without sacrificing error performance it has become an extremely important modulation.

The second technique is called code-division multiple access (CDMA). It falls within the broad class of multiple access methods and is an administrator's favorite. It makes the addition (or deletion) of new users essentially transparent, easing the administrator's work. However, the technique does have technical merits that warrant its study by communication engineers. It forms the basis for the so-called 3G (third generation) and beyond wireless communication systems. In contrast to TCM, CDMA is a very wideband modulation technique.

The last modulation method studied uses space-time codes which provide diversity gain for the fading channel through the use of multiple transmit antennas. The important Alamouti's space-time block code (STBC) is discussed in some detail. It was first described in 1998, almost at the same time Tarokh et al. published their paper on space-time trellis codes.

Trellis-coded modulation (TCM)

By now one can appreciate that over an AWGN channel, the basic idea in digital communications is to find signal constellations with as large a distance between signals as possible without increasing the energy Eb (joules) expended per bit inordinately and with as small a bandwidth as possible.