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1983

Error control coding : fundamentals and applications

1. Coding for Reliable Digital Transmission and Storage. 2. Introduction to Algebra. 3. Linear Block Codes. 4. Important Linear Block Codes. 5. Cyclic Codes. 6. Binary BCH Codes. 7. Nonbinary BCH Codes, Reed-Solomon Codes, and Decoding Algorithms. 8. Majority-Logic Decodable Codes. 9. Trellises for Linear Block Codes. 10. Reliability-Based Soft-Decision Decoding Algorithms for Linear Block Codes. 11. Convolutional Codes. 12. Trellis-Based Decoding Algorithms for Convolutional Codes. 13. Sequential and Threshold Decoding of Convolutional Codes. 14. Trellis-Based Soft-Decision Algorithms for Linear Block Codes. 15. Concatenated Coding, Code Decomposition ad Multistage Decoding. 16. Turbo Coding. 17. Low Density Parity Check Codes. 18. Trellis Coded Modulation. 19. Block Coded Modulation. 20. Burst-Error-Correcting Codes. 21. Automatic-Repeat-Request Strategies.

1972 - IEEE Trans. Inf. Theory

Class of algorithms for decoding block codes with channel measurement information

A class of decoding algorithms that utilizes channel measurement information, in addition to the conventional use of the algebraic properties of the code, is presented. The maximum number of errors that can, with high probability, be corrected is equal to one less than d , the minimum Hamming distance of the code. This two-fold increase over the error-correcting capability of a conventional binary decoder is achieved by using channel measurement (soft-decision) information to provide a measure of the relative reliability of each of the received binary digits. An upper bound on these decoding algorithms is derived, which is proportional to the probability of an error for d th order diversity, an expression that has been evaluated for a wide range of communication channels and modulation techniques. With the aid of a lower bound on these algorithms, which is also a lower bound on a correlation (maximum-likelihood) decoder, we show for both the Gaussian and Rayleigh fading channels, that as the signal-to-noise ratio (SNR) increases, the asymptotic behavior of these decoding algorithms cannot be improved. Computer simulations indicate that even for !ow SNR the performance of a correlation decoder can be approached by relatively simple decoding procedures. In addition, we study the effect on the performance of these decoding algorithms when a threshold is used to simplify the decoding process.

2005

Error Correction Coding: Mathematical Methods and Algorithms

1 阅读

Preface. List of Program Files. List of Laboratory Exercises. List of Algorithms. List of Figures. List of Tables. List of Boxes. PART I: INTRODUCTION AND FOUNDATIONS. 1. A Context for Error Correcting Coding. PART II: BLOCK CODES. 2. Groups and Vector Spaces. 3. Linear Block Codes. 4. Cyclic Codes, Rings, and Polynomials. 5. Rudiments of Number Theory and Algebra. 6. BCH and Reed-Solomon Codes: Designer Cyclic Codes. 7. Alternate Decoding Algorithms for Reed-Solomon Codes. 8. Other Important Block Codes. 9. Bounds on Codes. 10. Bursty Channels, Interleavers, and Concatenation. 11. Soft-Decision Decoding Algorithms. PART III: CODES ON GRAPHS. 12. Convolution Codes. 13. Trefils Coded Modulation. PART IV: INTERATIVELY DECODED CODES. 14. Turbo Codes. 15. Low-Density Parity-Check Codes. 16. Decoding Algorithms on Graphs. PART V: SPACE-TIME CODING. 17. Fading Channels and Space-Time Coding. References. Index.

论文关键词

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