Über die Autorin bzw. den Autor

Professor E. Krouk has worked in the field of communication theory and techniques for more than 30 years. His areas of interests are coding theory, the mathematical theory of communications and cryptography. He is now the Dean of the Information Systems and Data Protection Faculty of the Saint-Petersburg State University of Aerospace Instrumentation. He is author of 3 books, more than 100 scientific articles and 30 international and Russian patents.

Sergei Semenov received his Ph.D. degree from St.-Petersburg State University for Airspace Instrumentation (SUAI), Russia in 1993. Dr. Semenov joined Nokia Corporation in 1999 and is currently a Specialist in Modem Algorithm Design/Wireless Modem. His research interests include coding and communication theory and their application to communication systems.

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The high level of technical detail included in standards specifications can make it difficult to find the correlation between the standard specifications and the theoretical results. This book aims to cover both of these elements to give accessible information and support to readers. It explains the current and future trends on communication theory and shows how these developments are implemented in contemporary wireless communication standards.

Examining modulation, coding and multiple access techniques, the book is divided into two major sections to cover these functions. The two-stage approach first treats the basics of modulation and coding theory before highlighting how these concepts are defined and implemented in modern wireless communication systems. Part 1 is devoted to the presentation of main L1 procedures and methods including modulation, coding, channel equalization and multiple access techniques. In Part 2, the uses of these procedures and methods in the wide range of wireless communication standards including WLAN, WiMax, WCDMA, HSPA, LTE and cdma2000 are considered.

• An essential study of the implementation of modulation and coding techniques in modern standards of wireless communication
• Bridges the gap between the modulation coding theory and the wireless communications standards material
• Divided into two parts to systematically tackle the topic - the first part develops techniques which are then applied and tailored to real world systems in the second part
• Covers special aspects of coding theory and how these can be effectively applied to improve the performance of wireless communications systems

Aus dem Klappentext

The high level of technical detail included in standards specifications can make it difficult to find the correlation between the standard specifications and the theoretical results. This book aims to cover both of these elements to give accessible information and support to readers. It explains the current and future trends on communication theory and shows how these developments are implemented in contemporary wireless communication standards.

Examining modulation, coding and multiple access techniques, the book is divided into two major sections to cover these functions. The two-stage approach first treats the basics of modulation and coding theory before highlighting how these concepts are defined and implemented in modern wireless communication systems. Part 1 is devoted to the presentation of main L1 procedures and methods including modulation, coding, channel equalization and multiple access techniques. In Part 2, the uses of these procedures and methods in the wide range of wireless communication standards including WLAN, WiMax, WCDMA, HSPA, LTE and cdma2000 are considered.

• An essential study of the implementation of modulation and coding techniques in modern standards of wireless communication
• Bridges the gap between the modulation coding theory and the wireless communications standards material
• Divided into two parts to systematically tackle the topic - the first part develops techniques which are then applied and tailored to real world systems in the second part
• Covers special aspects of coding theory and how these can be effectively applied to improve the performance of wireless communications systems

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Modulation and Coding Techniques in Wireless Communications

John Wiley & Sons

Chapter One

Evgenii Krouk, Andrei Ovchinnikov, and Jussi Poikonen

1.1 Principles of Reliable Communication

Ideally, design, development and deployment of communication systems aims at maximally efficient utilization of available resources for transferring information reliably between a sender and a recipient. In real systems, typically some amount of unreliability is tolerated in this transfer to achieve a predefined level of consumption of limited resources. In modern communication systems, primary resources are time, space, and power and frequency bandwidth of the electromagnetic radiation used to convey information. Given such resources, systems must be designed to overcome distortions to transmitted information caused mainly by elements within the system itself, possible external communications, and the environment through which the information propagates. To achieve efficient utilization of available resources, knowledge of the mechanisms that cause interference in a given transmission scenario must be available in designing and analyzing a communication system.

In performance evaluation of wireless communication systems, significance of the communication channel is emphasized, since the degradation of a signal propagating from a transmitter to a receiver is strongly dependent on their locations relative to the external environment. Wireless mobile communication, where either the transmitter or the receiver is in motion, presents additional challenges to channel modelling, as it is necessary to account for variation in the signal distortion as a function of time for each transmitterreceiver pair. In developing and analyzing such systems, comprehensively modelling the transmitterreceiver link is a complicated task.

In the following, distortions caused by typical communication channels to transmitted signals are described. A common property of all communication channels is that the received signal contains noise, which fundamentally limits the rate of communication. Noise is typically modelled as a Gaussian stochastic process. The additive white Gaussian noise (AWGN) channel and its effects on typical digital modulation methods are presented in Section 1.2. Noise is added to transmitted signals at the receiver. Before arriving at the receiver terminal, signals are typically distorted according to various physical characteristics of the propagation medium. These distortions attenuate the received signal, and thus increase the detrimental effect of additive noise on the reliability of communication. In Section 1.3 to 1.5 typical cases of distortion in wireless communication channels and models for the effects of such distortion on transmitted signals are presented.

1.2 AWGN

Distortions occurring in typical communication systems can be divided into multiplicative and additive components. In the following, some remarks and relevant results concerning additive distortion – also referred to simply as noise – are presented.

Additive noise is introduced to a wireless communication system both from outside sources such as atmospheric effects, cosmic radiation and electrical devices – and from internal components of the receiver hardware, which produce thermal and shot noise. Typically, additive distortion in a received signal consists of a sum of a large number of independent random components, and is modelled as additive white Gaussian noise, where the term white means that the noise is assumed to have a constant power spectral density. The Gaussian, or normal, distribution of noise is motivated by the central limit theorem (one of the fundamental theorems of probability theory), according to which the distribution of a sum of a large number of random variables approaches a normal distribution, given that these variables fulfill Lyapunov's condition (for details, see for example).

In some cases, the received signal is also distorted by a channel-induced superposition of different components of the useful transmission, or by signals from other transmission systems. Such distortions are called interference, and differ from additive noise in that typically some source-specific statistical characteristics of interference are known. Thus interference is not in all cases best approximated as an additive white Gaussian process. Interference effects are strongly dependent on the communication systems and transmission scenarios under consideration. Later in this chapter, interference-causing effects of wireless communication channels are considered. In the following, we focus on considering the effects of additive white Gaussian noise on complex baseband modulation symbols. Principles of digital modulation methods and the effects of noise on the reception of various types of transmitted signals will be considered in more detail in Chapter 2; the following simple examples are meant to illustrate the concept of additive noise and its effect on digital communication.

1.2.1 Baseband Representation of AWGN

In the following examples, we consider digital data which is mapped to binary phase shift keying (BPSK), quaternary phase shift keying (QPSK/4-QAM), and 16-point quadrature amplitude modulation (16-QAM) symbols. We consider complex baseband signals, that is, for our purposes the transmitted modulation symbols corresponding to a given digital modulation scheme are represented simply as complex numbers. The constellation diagrams for these examples are illustrated in Figure 1.1. The effect of an AWGN channel is to shift these numbers in the complex plane. The receiver has to decide, based on an observed shifted complex number, the most likely transmitted symbol. This decision is performed by finding which, out of the set of known transmitted symbols, is the one with the smallest Euclidian distance to the received noisy symbol. This is a rather abstract representation of digital signals and noise, but sufficient for performing error performance analyses of different modulation schemes. For a more detailed discussion on basic modulation methods and the corresponding signal forms, see Chapter 2.

As outlined above, in complex baseband signal-space representations, the effect of additive white Gaussian noise in the receiver can be described as a complex number added to each transmitted modulation symbol value. The real and imaginary parts of these complex numbers are independent and identically distributed Gaussian random variables with zero mean and variance equal to σ²_N = [bar.P].sub. N/2, where [bar.P]_N denotes the total average power of the complex noise process (that is, the power of the noise is evenly distributed into the two signalling dimensions). In the following, the orthogonal components of the noise process are denoted by a common notation X_N ~ N (0, [bar.P]_N/2).

If the absolute value of either the real or the imaginary noise component is larger than half of the Euclidian distance d between adjacent modulation symbols, a transmitted symbol may be erroneously decoded into any symbol within a complex half-plane, as illustrated in the QPSK example of Figure 1.2. The probability of one of the independent and identically distributed noise components having such values can be written as:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1.1)

where...