DIGITAL. COMMUNICATIONS. Fundamentals and Applications. Second Edition. BERNARD SKLAR. Communications Engineering Services, Tarzana, California. ruthenpress.info - Free ebook download as PDF File .pdf), Text File .txt) or read book online for free. Digital communications: Fundamentals and Applications. by Bernard Sklar. 2 .. of observed vector are independent Gaussian random variables. Its pdf is.
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Request PDF on ResearchGate | Digital Communications, Fundamentals and Applications | This Bernard Sklar at Communications Engineering Services. Designing Digital Communication Systems by Bernard Sklar Introduction This article is intended to serve as a “road map” for outlining typical steps to be. The syllabus – ruthenpress.info~bazuinb/ECE/Syl_pdf Bernard Sklar, Digital Communications, Fundamentals and Applications.
Preview — Digital Communications by Bernard Sklar. Digital Communications: Fundamentals and Applications by Bernard Sklar. The clear, easy-to-understand introduction to digital communications Completely updated coverage of today's most critical technologies Step-by-step implementation coverage Trellis-coded modulation, fading channels, Reed-Solomon codes, encryption, and more Exclusive coverage of maximizing performance with advanced "turbo codes" "This is a remarkably comprehensive treatment of t The clear, easy-to-understand introduction to digital communications Completely updated coverage of today's most critical technologies Step-by-step implementation coverage Trellis-coded modulation, fading channels, Reed-Solomon codes, encryption, and more Exclusive coverage of maximizing performance with advanced "turbo codes" "This is a remarkably comprehensive treatment of the field, covering in considerable detail modulation, coding both source and channel , encryption, multiple access and spread spectrum.
It can serve both as an excellent introduction for the graduate student with some background in probability theory or as a valuable reference for the practicing ommunication system engineer.
For both communities, the treatment is clear and well presented. Digital Communications, Second Edition is a thoroughly revised and updated edition of the field's classic, best-selling introduction. With remarkable clarity, Dr. Bernard Sklar introduces every digital communication technology at the heart of today's wireless and Internet revolutions, providing a unified structure and context for understanding them -- all without sacrificing mathematical precision.
Sklar begins by introducing the fundamentals of signals, spectra, formatting, and baseband transmission. Next, he presents practical coverage of virtually every contemporary modulation, coding, and signal processing technique, with numeric examples and step-by-step implementation guidance.
Coverage includes: Signals and processing steps: With nearly illustrations and problems and exercises, there's never been a faster way to master advanced digital communications. Get A Copy. Hardcover , pages. Published January 21st by Prentice Hall first published October 1st More Details Original Title. Other Editions 5.
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Showing Rating details. Complex Envelope. Error Performance for Binary Systems. M-ary Signaling and Performance. Communications Link Analysis. The Channel. Received Signal Power and Noise Power. Link Budget Analysis. Sample Link Analysis. Satellite Repeaters. System Trade-Offs. Channel Coding: Part 1. Waveform Coding.
Types of Error Control. Structured Sequences. Linear Block Codes. Error-Detecting and Correcting Capability. Usefulness of the Standard Array. Cyclic Codes. Well-Known Block Codes. Channel Coding: Part 2. Convolutional Encoding.
Convolutional Encoder Representation. Formulation of the Convolutional Decoding Problem. Properties of Convolutional Codes.
Other Convolutional Decoding Algorithms. Channel Coding: Part 3.
Reed-Solomon Codes. Interleaving and Concatenated Codes. Turbo Codes. Appendix 8A. The Sum of Log-Likelihood Ratios. Modulation and Coding Trade-Offs. Goals of the Communications System Designer.
Error Probability Plane. Nyquist Minimum Bandwidth. Shannon-Hartley Capacity Theorem. Bandwidth Efficiency Plane.
Defining, Designing, and Evaluating Systems. This definition separates the system performance due to bandwidth spreading from the performance due to error- correction coding.
Since we ultimately want to relate all of the coding mechanisms relative to the information source, we will conform to the definition for processing gain, as expressed in Equations 34 and A spread-spectrum system can be used for interference rejection and for multiple access allowing multiple users to access a communications resource simultaneously. In such systems, the large value of Gp allows the signaling chips to be transmitted at a power level well below that of the thermal noise.
At the receiver, the despreading operation correlates the incoming signal with a 22 Designing Digital Communications Systems synchronized copy of the PN code, and thus accumulates the energy from multiple Gp chips to yield the energy per data bit. The value of Gp has a major influence on the performance of the spread-spectrum system application.
In other words, spread-spectrum techniques offer no error-performance advantage over thermal noise. Sometimes such spread-spectrum radio systems are employed only to enable the transmission of very small power- spectral densities, and thus avoid the need for FCC licensing .
For simplicity, assume that there are no bandwidth constraints. In this example, our task is simply to determine whether the required error performance can be achieved using the given system architecture and design parameters. In evaluating the system, we will use the same type of transformations that were used in the previous examples. When we consider the relationships in transforming from data bits to channel bits to symbols to chips, we can see the same pattern of subtle but straightforward transformations in rates and energies as in Figures 2 and 3.
It should be apparent that Equation 36 is valid for any such transformation when the rate and energy are modified in a reciprocal way. There is a kind of conservation of power or energy phenomenon that exists in the transformations.
The total received average power or total received energy per symbol duration is fixed regardless of how it is computed—on the basis of data bits, channel bits, symbols, or chips. Therefore, the DS spreading transformation has no effect on the error performance of an AWGN channel , and thus the value of Gp will have no bearing on the resulting value of PB in this example. We can therefore verify that for the given architecture and design parameters of this example, the system does in fact achieve the required error performance.
Code Selection Consider a real-time communication system, in which the specifications cause it to be power-limited but there is ample available bandwidth, and the users require a very small bit-error probability. Error-correction coding is called for. Suppose that we were asked to select one of the BCH codes listed in Table 2.
Since the system is not bandwidth-limited, and it requires very good error performance, one might be tempted to simply choose the most powerful code in Table 2, that is, the , 8 code, capable of correcting any combination of up to 31 flawed bits within a block of code bits. Would anyone use such a code in a real-time communication system?
Let me explain why such a choice would be unwise. Whenever error-correction coding is used in a real-time communication system, there are two mechanisms at work that influence error performance. One mechanism works to improve the performance, and the other works to degrade it. The improving-mechanism is the coding; the greater the redundancy, the greater will be the error-correcting capability of the code. The degrading mechanism is the energy reduction per channel symbol or code bit compared to the data bit.
This reduced energy stems from the increased redundancy and faster signaling in a real-time communication system. The reduced symbol energy causes the demodulator to make more errors. Eventually, the second mechanism wins out, and thus at very low code rates we see degradation. This is demonstrated in Example 5 below.
Note that the degrading mechanism applies for coding only in a real-time system where messages cannot be delayed. For systems that can endure message Designing Digital Communications Systems 25 delays, the tradeoff for getting the benefits of the code redundancy is delay not reduced symbol energy.
Choose a code from Table 2 that will fulfill these requirements. Start by considering the , 8 code. It appears attractive because it has the greatest bit-error correcting capability on the list.
But when pc is large, as here, computer assistance is 26 Designing Digital Communications Systems helpful. It is not as capable as the first choice because it corrects only 10 flawed bits in a block of code bits.
But watch what happens. Be guided by the fact that very high rates and very low rates generally perform poorly in a real-time communication system. As was described earlier, this comes about because there are two mechanisms at work: 1 an improving mechanism; more redundancy means greater error-correcting capability, and 2 a degrading mechanism; energy reduction per channel symbol causes the demodulator to make more errors. As the code rate is reduced, the second mechanism eventually wins out, and thus at very low code rates the system experiences error-performance degradation .
Conclusion The goal in this article has been to review fundamental relationships used in designing digital communication systems. First, we examined the concept of bandwidth-limited and power-limited systems and how such conditions influence the design. Most importantly, we focused on the definitions and computations involved in transforming from data bits to channel bits to symbols to chips.