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optimal and heuristic scheduling algorithms will be introduced. One commonly employed heuristic algorithm the greedy -based algorithm was shown to be optimal for single-antenna systems but suboptimal for multiple antenna systems. This motivates the genetic-based scheduling algorithm, which achieves near-optimal performance at signi cant computational savings. Finally, we consider the cross-layer design and system performance in the presence of imperfect CSI. Part 2. 7 provides a high level review of MAC layer design for contemporary wireless systems with design examples for TDMA-based and CDMA-based systems. 8 contains overviews of various practical fairness notions and scheduling algorithms. 9 gives a detailed description of how cross-layer scheduling is applied in UMTS (W-CDMA) systems for packet-switched data users. Part 3. 10 discusses an advanced topic of cross-layer adaptive scheduling design for wideband systems. Both DS-CDMA and OFDM are promising physical layer technologies for dealing with wideband multipath channels. We shall compare and contrast the multiuser performance of the cross-layer designs based on DS-CDMA and OFDMA systems. 11 gives the advanced application of information theory and queueing theory for the cross layer design. The concepts of stability region, throughput optimal scheduler as well as delay optimal scheduler will be discussed. Finally, 12 provides a detailed account of the design of a channel-adaptive ad hoc routing algorithm. Vincent and Ricky
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The authors would like to thank all the people who have helped in this book in one way or another. In particular, the authors would like to thank Professor Ross Murch, soon-to-be doctor Mr. Edward Au Miss Zaleta Ho, Ph. Peter Chan for the detail review and comments on the book. Furthermore, we would like to thank Mr. Ray Wang, David Hui, Yi Wu, and Tianyu Wu for helping us to contribute some of the materials and gures in the book. Indeed, we owe a deep sense of gratitude to our friends and families, especially now that this project has been completed. V. R.
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In this chapter, we review the important and basic concepts in wireless communications. In Section 1.2, we rst review different types of wireless channel models, namely, time dispersion, multipath dispersion, and spatial dispersion in microscopic fading. Concepts of frequency-selective fading, frequency at fading, fast fading, slow fading, coherence bandwidth, coherence time, and coherence distance will be introduced. In Section 1.3, we establish the equivalence of discrete-time and continuous-time models in wireless communications for both the frequency at fading and frequency-selective fading channels. In Section 1.4, we review the important and fundamental concepts of entropy, mutual information, and channel capacity, which are critical to the understanding of the materials and approaches in the subsequent chapters. Finally, in Section 1.5, we conclude with a brief summary of main points.
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A typical communication system consists of a transmitter, a receiver, and a channel. The channel is de ned as the physical medium linking the transmitter output and the receiver input. For instance, telephone wire, optical ber, and the atmosphere are different examples of communication channels. In fact, communication channel plays a very important role in communication system design because the transmitter and receiver designs have to be optimized with respect to the target channel.
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Channel-Adaptive Technologies and Cross-Layer Designs for Wireless Systems with Multiple Antennas: Theory and Applications. By V. K. N. Lau and Y.-K. R. Kwok ISBN 0-471-64865-5 2006 by John Wiley & Sons, Inc.
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In this book, we focus on the wireless communication channels involving radiofrequencies. In other words, the atmosphere is the medium carrying radiowaves. Please refer to References 15 and 114 for a more detailed introduction to wireless communication channels. Speci cally, we brie y review the statistical models of wireless communication channels for single-antenna and multiple-antenna systems, which are frequently used in the analysis and the design of wireless communication systems. 1.2.1 AWGN Channel Model
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We consider the simplest wireless channel, the additive white Gaussian noise (AWGN) channel. Without loss of generality, we consider single-antenna systems as illustrative in this section. The received signal (y(t)) is given by the transmitted signal (x(t)) plus a white Gaussian noise (z(t)) y(t ) = Lx(t ) + z(t ) (1.1)
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where L is the power attenuation from the transmitter to the receiver. In free space, L obeys the inverse square law.1 The AWGN channel is in fact quite accurate in deep-space communications and the communication links between satellite and Earth station. However, it is far from accurate in most terrestrial wireless communications, due to multipath, re ection, and diffraction. Yet, AWGN channel serves as an important reference on the performance evaluation of communication systems. In terrestrial wireless communications, signals travel to the receiver via multiple paths, and this creates additional distortion to the transmitted signal on top of the channel noise. In general, the effect of multipath and re ections could be modeled as wireless fading channels or microscopic fading. Factors affecting the microscopic fading include multipath propagation, speed of the mobile (unit), speed of the surrounding objects, the transmission symbol duration, and the transmission bandwidth of the signal. 1.2.2 Linear Time-Varying Deterministic Spatial Channel
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Consider a general linear channel that can be characterized by a lowpass equivalent time-domain impulse response denoted by h(t; t, r) (where t is the time-varying parameter, t is the path delay parameter, and r is the spatial position parameter). The general linear channel is therefore characterized by three independent dimensions: the time dimension (characterized by the time parameter t), the delay dimension (characterized by the delay parameter t), and the spatial dimension (characterized by the position parameter r). Given a lowpass equivalent input signal x(t), the lowpass equivalent received signal
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This is the received power level reduced by 4 times whenever the distance between the transmitter and the receiver increases by 2 times.