西安电子科技大学：《现代数字通信与编码理论》课程教学资源（讲义）Chapter 01 Introduction（主讲：白宝明）.pdf_大学文库

References [1]C.E.Shannon,"A mathematical theory of communication,"Bell Syst.Tech.J.,vol.27. pp.379-423,623-656,July-Oct.1948;Reprinted in C.E.Shannon and W.Weaver,The athematical Theory of Communication.Urbana,IL:Univ.Illinois Press,1949. “Claude E.Shannon: A Retrospective on His Life,Work,and Impact," ran ory,vol.4 209D, 31G.D. and line r Gaussian I P m. ol44, chann nd c aspe cts,”EEE Trans. rm.Theory, ol 44 pp.2619-26920 1998 [5]D.J.Costello,J.Hagenauer,H.Imai,and S.B.Wicker coding: [6]A.R.Calderbank."The art of signaling:Fifty yea 0l4,06Pp25312500L198cc0ntoy Inform.Theory,vol.44,no.6,pp.2561-2595,Oct.1998. 肉9em&ma学ak2 ed.Kluwer Academic shers,Boston 99 [9]G D.Fomey and R.Gallager,Principles of Digital Communications.Course notes [1O]R G Gallager, ormation Theory and Reliable Communication.New York:Johr [12]J.L.Masse Course n s ETH 13]S.G.Wilson.Digital Modulation and Coding.Prentice-Hall,1996 [14]E.Biglierli,D.Divsalar,P.J.McLane,and M.K.Simon,Introduction to Trellis-Coded Modulation with Applications.New York:MacMillan,1991. [15]D.N.C.Tse and P.Viswanath,Fundamentals of Wireless Communication.Cambridge University Press,2005. [16]A.Goldsmith,Wireless Communications.Cambridge University Press,2005. 17]T.J.Richardson and R.L.Urbanke,Modern Coding Theory.Course notes.EPFL. 18]C.Schlegel and L.Pere Trellis and Turbo Coding.IEEE Press,2004. ireless commun.,vol.92,no.2,Feb.2004 Signa [22]M.Medard and R.G.Gallager,"Bandwidth scaling for fading multipath channels." vol.48.no.4. [23]I.C.Abou-Faycal,M.D.Trott,and S.Shamai (Shitz). m2Cpesyotdsem memoryless Rayleigh-fading channels,"IEEE Trans.Inform.Theory,vol.47,no.4. pp.1290-130L,May2001. [24JE.Biglieri,G.Caire,and G.Taricco,"Limiting performance of block-fading channels with multiple antennas,"IEEE Trans.Inform.Theory,vol.47,no.4,pp.1273-1289,May 2001 [25]G.J.Foschini and M.J.Gans,"On limits of wireless communications in a fading ltiple antennas,"Wireless Personal Communications,vol.6. elata mul sian channels,"European Trans 5

5 References [1] C. E. Shannon, “A mathematical theory of communication,” Bell Syst. Tech. J., vol. 27, pp. 379-423, 623-656, July-Oct. 1948; Reprinted in C. E. Shannon and W. Weaver, The Mathematical Theory of Communication. Urbana, IL: Univ. Illinois Press, 1949. [2] R. G. Gallager, “Claude E. Shannon: A Retrospective on His Life,Work, and Impact,” IEEE Trans. Inform. Theory, vol.47, no.7, pp. 2681-2695, Nov. 2001. [3] G. D. Forney, Jr. and G. Ungerboeck, “Modulation and coding for linear Gaussian channels,” IEEE Trans. Inform. Theory, vol.44, no.6, pp. 2384-2415, Oct. 1998. [4] E. Biglieri, J. Proakis, and S. Shamai (Shitz), “Fading channels: Information-theoretic and communication aspects,” IEEE Trans. Inform. Theory, vol.44, pp.2619-2692, Oct. 1998. [5] D. J. Costello, J. Hagenauer, H. Imai, and S. B. Wicker, “Applications of error-control coding,” IEEE Trans. Inform. Theory, vol.44, no.6, pp.2531-2560, Oct. 1998. [6] A. R. Calderbank, “The art of signaling: Fifty years of coding theory,” IEEE Trans. Inform. Theory, vol.44, no.6, pp.2561-2595, Oct. 1998. [7] J. G. Proakis, Digital Communications. 4rd ed. New York: McGraw-Hill, 2000. [8] E. A. Lee and D. G. Messerschmitt, Digital Communication, 2nd ed. Kluwer Academic Publishers, Boston, 1994. [9] G. D. Forney and R. Gallager, Principles of Digital Communications. Course notes. MIT. [10]R. G. Gallager, Information Theory and Reliable Communication. New York: John Wiley and Sons, 1968. [11]T. M. Cover and J. A. Thomas, Elements of Information Theory. Wiley, 1991. [12]J. L. Massey, Applied Digital Information Theory. Course notes. ETH. [13]S. G. Wilson, Digital Modulation and Coding. Prentice-Hall, 1996. [14]E. Biglierli, D. Divsalar, P. J. McLane, and M. K. Simon, Introduction to Trellis-Coded Modulation with Applications. New York: MacMillan, 1991. [15]D. N. C. Tse and P. Viswanath, Fundamentals of Wireless Communication. Cambridge University Press, 2005. [16]A. Goldsmith, Wireless Communications. Cambridge University Press, 2005. [17]T. J. Richardson and R. L. Urbanke, Modern Coding Theory. Course notes. EPFL. [18]C. Schlegel and L. Perez, Trellis and Turbo Coding. IEEE Press, 2004. [19]Proceedings of The IEEE, Special issue on wireless commun., vol.92, no.2, Feb. 2004. [20]IEEE Signal Processing Magazine, Jan. 2004. [21]IEEE Communication Magazine, Aug. 2003. [22]M. Medard and R. G. Gallager, “Bandwidth scaling for fading multipath channels,” IEEE Trans. Inform. Theory, vol.48, no.4, pp.840-852, April 2002. [23]I. C. Abou-Faycal, M. D. Trott, and S. Shamai (Shitz), “The capacity of discrete-time memoryless Rayleigh-fading channels,” IEEE Trans. Inform. Theory, vol.47, no.4, pp.1290-1301, May 2001. [24]E. Biglieri, G. Caire, and G. Taricco, “Limiting performance of block-fading channels with multiple antennas,” IEEE Trans. Inform. Theory, vol.47, no.4, pp.1273-1289, May 2001. [25]G. J. Foschini and M. J. Gans, “On limits of wireless communications in a fading environment when using multiple antennas,” Wireless Personal Communications, vol.6, no.3, pp.311-335, Mar. 1998. [26]I. E. Telatar, “Capacity of multi-antenna Gaussian channels,” European Trans. Telecomm., vol.10, no.6, pp.585-596, Nov.-Dec. 1996

7 0122229 现代数字通信与编码理论 September 1, 2010 XDU, Fall 2010 Lecture Notes Introduction Digital communication is a field in which theoretical ideas have had an unusually powerful impact on actual system design. The basis of the theory was developed 58 years ago by Claude Shannon, and is called information theory. The goal of this course is to get acquainted with some of these ideas and to gain deep understanding on how to efficiently and reliably communicate through a channel, especially to better understand the advanced techniques for signal transmission and coding used in modern digital communication systems. We will focus on point-to-point systems consisting of a single transmitter, a channel and a receiver. A. Block diagram of a digital communication system In 1948, Claude E. Shannon of the Bell Telephone Laboratories published one of the most remarkable papers in the history of engineering. This paper (“A Mathematical Theory of Communication", Bell System Tech. Journal, Vol. 27, July and October 1948, pp. 379 - 423 and pp. 623 - 656) laid the groundwork of an entirely new scientific discipline, “information theory", in which Shannon first introduced the following figure to model a digital communication system. The source encoder involves the efficient representation of source signals. It has the function of converting the input from its original form, e.g., speech waveforms, image waveforms, and text, into a sequence of bits. The objective of doing this is as efficiently as possible. i.e., transmitting as few bits as possible, subject to the need to reconstruct the input adequately at the output. In this case source encoding is often called data compression. Shannon showed that the ultimate data compression is the entropy of the source. The channel encoder box in the figure above has the function of mapping the binary sequence at the source/channel interface into channel inputs. The channel inputs might be waveforms, or might be discrete sequences. The general objective here is to map binary inputs at the maximum bit rate possible into waveforms or sequences such that the channel decoder can recreate the original bits with low probability of error. One simple approach to performing this is called modulation and demodulation. From the geometric signal-space viewpoint, the modulation process may be thought of a two-step process: first mapping 信源信源编码器 ECC 编码器数字调制器信道干扰信宿信源译码器 ECC 译码器数字解调器调制信道 Channel decoder n(t) bits Channel encoder symbols waveform

binary digits into signals(e.g.signal levels)and then signals into waveforms. Since high error probability is frequently incurred with simple modulation and demodulation in the presence of noise,the error-correcting codes was introduced and the channel coder is separated into two layers,first an error-correcting encoder,and then a simple modu lator.Shannon showed that,with appropriate coding schemes,arbitrarily low error probabilities can be achieved at any data rate below a certain data rate called the cha s,channel codin ally involved a ystem similar to that above t then the err a m rrect ode the 198 e Ungerbo alled In s The nurnose of the modulation svstem is to create a good discrete channel from the modulator input to the demodulator output and the purnose of the coding system is to transmit the information bits reliably through this discrete channel at the highest practicable rate."-Massev In this we the d fundamental A、unic ad d te ed in are information-theoretic ideas.This course will attempt to reflect these new evolutions Som of exposition has benefited from the excellent notes written by Gallager and Forney for the MIT courses 6.450 and 6.451. We will present the material in such a unified way that the channel model and the corresponding channel capacity are introduced first,and then the coding and signal process techniques for approaching these optimal performance limits are presented,and followed by the discussion on the performance of the actual systems with these channel coding schemes commu cat ons problem can be brok n dow without loss of reliab the shown in a communi can any rate elow mitted over the channel.This redundancy lowers the raw data ror rate after using the edundand to correct errors.(distance The net effect is to increase the rate at which clean data is delivered C.Historical notes ·Hamming codes"1g50 Convolutional codes:1955 (by Elias) ·BCH,Reed-Solomo ncodes:1960 ·LDPC codes: 1962(by Gallager)(rediscoved in late 1990's) ·Concatenated codes.l966(by Forney)】 Viterbi algorithm:1967 TCM:1982(by Ungerboeck) 8

8 binary digits into signals (e.g. signal levels) and then signals into waveforms. Since high error probability is frequently incurred with simple modulation and demodulation in the presence of noise, the error-correcting codes was introduced and the channel coder is separated into two layers, first an error-correcting encoder, and then a simple modulator. Shannon showed that, with appropriate coding schemes, arbitrarily low error probabilities can be achieved at any data rate below a certain data rate called the channel capacity. By the 1980’s, channel coding usually involved a two layer system similar to that above, where an error-correcting code is followed by a modulator. At the receiver, the waveform is first demodulated, and then the error correction code is decoded. Since the Ungerboeck’s work in 1982, it has been recognized that coding and modulation should be considered as a unit, resulting in the schemes called coded modulation. In such schemes, the lower error probability can be achieved without sacrificing bandwidth efficiency. “The purpose of the modulation system is to create a good discrete channel from the modulator input to the demodulator output, and the purpose of the coding system is to transmit the information bits reliably through this discrete channel at the highest practicable rate.” - Massey In this course, we will study the concepts and fundamental principles involved in advanced digital communication systems. We will focus on the channel coding component in the above figure. As we will see later, many advanced techniques used in modern digital communication systems (including mobile communication systems) are developed using information-theoretic ideas. This course will attempt to reflect these new evolutions. Some of exposition has benefited from the excellent notes written by Gallager and Forney for the MIT courses 6.450 and 6.451. We will present the material in such a unified way that the channel model and the corresponding channel capacity are introduced first, and then the coding and signal process techniques for approaching these optimal performance limits are presented, and followed by the discussion on the performance of the actual systems with these channel coding schemes. B. Relevant results from information theory The communications problem can be broken down without loss of reliability or efficiency into the separate components shown in the above diagram.Reliable communication can be achieved at any rate below the capacity of the communications channel. We add controlled redundancy to data transmitted over the channel. This redundancy lowers the raw data rate, but reduces the error rate after using the redundancy to correct errors. (distance gain) The net effect is to increase the rate at which clean data is delivered C. Historical notes • Hamming codes: 1950 • Convolutional codes: 1955 (by Elias) • BCH, Reed-Solomon codes: 1960 • LDPC codes: 1962 (by Gallager) (rediscoved in late 1990’s) • Concatenated codes: 1966 (by Forney) • Viterbi algorithm: 1967 • TCM: 1982 (by Ungerboeck)