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Viterbi Detector: Review of Fast Algorithm and Implementation

Viterbi Detector: Review of Fast Algorithm and Implementation . -Xiaohong Sheng ECE734 Project. Viterbi Algorithm. Viterbi Algorithm: The optimum decoding algorithm for convolutional code, it can also be used for speech and character recognition which is modeled by hidden Markov models .

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Viterbi Detector: Review of Fast Algorithm and Implementation

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  1. Viterbi Detector: Review of Fast Algorithm and Implementation -Xiaohong Sheng ECE734 Project

  2. Viterbi Algorithm • Viterbi Algorithm: • The optimum decoding algorithm for convolutional code, it can also be used for speech and character recognition which is modeled by hidden Markov models

  3. Convolutional Code • Convolutional code • Widely used for digital communication Cj=[Cj1,Cj2] Example: G0 Cj1 G1 Xj G2 State Cj2

  4. Problems on Viterbi Algorithm • Computational complexity increases exponentially with constraint length (state) of convolutional code • Nonlinear feedback loop in the VA presents a bottleneck for High speed implementations • Other issues such as: • Viterbi algorithm is a ML (optimum) algorithm if Euclidean distance is used. The usually used Hamming distance in VA is sub-optimum and therefore lose some performance. • If Euclidean distance is used, The use of multiplier increases the decoder complexity significantly

  5. Any Solution? • YES! • Two main solutions • Reduce state • At least half of the states can be reduced for DPSK sources. • Exciting! -Yes, Believe?-?, How? And any other problem can be induced? • Pipeline • Solve the bottleneck of nonlinear feedback? • Others solutions like • Linear distance metric can be used • Select some special convolutional codes

  6. Reduced state solution • DPSK sources Received signal at the ith receiver for QAM data communication system can be described as When Xi(t) is oversampled by K, hi(t) lasts a maximum of d symbol intervals and put all data from N receivers in a vector, the signals can be modeled as: Under the assumptions on: a)Si are orthonormal, b) Noise is Gaussian Use SVD Use Mahalanobis orthogonalization transform

  7. Reduced state solution (Cont.) • It can be proven that Where: So, Is affected by input data symbols [St, St-1…St-k-d+1] So, the optimal detection can be defined by: It can be achieved by VA to a M^(d+k-1) states, the original Rx optimal detection achieved by VA has M^(d+k) states. Half of States is reduced

  8. Pipeline Solution • Pipeline • M-Step Trellis (Look Ahead) or • M-Step Trellis+1-Step Trellis • Backward and forward Trellis

  9. Other solutions • Use Linear Distances (For QPSK 8-PSK, 16-QAM) • Avoid multiplication without losing the VA decoder performance • Use doubly complementary convolutional codes • Save 1/3 of real time operations over the VA with a state grouping and partitioning of the trellis

  10. Other issues I’m thinking... • Can we increase the decoder speed infinitely if we have infinite hardware? If not, what’s maximum speed we can achieve? • Is there optimal partitions given the size of the source need to be decoded so that we can achieve maximum decoding speed and use minimum hardware • Woo…, Really hard mathematical problem. And Perhaps no solution • Interested these problems also?

  11. Reference(1) • [1]. Implementing the Viterbi algorithm, Lou, H.-L. IEEE Signal Processing Magazine , Volume: 12 Issue: 5 , Sept. 1995, Page(s): 42 -52  • [2]. A reduced-state Viterbi algorithm for blind sequence estimation of DPSK sources,Tongtong Li; Zhi Ding Global Telecommunications Conference, 1999. GLOBECOM '99 , Volume: 4 , 1999 ,Page(s): 2167 -2171 vol.   • [3]. A reduced state Viterbi algorithm for multiuser detection in DS/CDMA systems ,Wang Zhaocheng; Ge Ning; Yao Yan; Qiang Wang Communication Technology Proceedings, 1996. ICCT'96., 1996 International Conference on , Volume: 2 , 1996 Page(s): 1102 -1105 vol.2  • [4]. Linear distances as branch metrics for viterbi decoding of trellis codes,Hut-Ling Lou Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on , Volume: 6 ,Page(s): 3267 -3270  • [5]. A constraint-length based modified Viterbi algorithm with adaptive effort Feldmann, C.; Harris, J.H. Communications, IEEE Transactions on, Volume: 47 Issue: 11 , Nov. 1999 Page(s): 1611 –1614

  12. Reference(2) • [6]. Complexity reduction of the Viterbi algorithm using doubly complementary convolutional codes, Haccoun, D.; Caron, M.; Nabli, M. Communications, Computers and Signal Processing, 1999 IEEE Pacific Rim Conference on , 1999 Page(s): 408 –411 • [7]. High-performance VLSI architecture for the Viterbi algorithm, Boo, M.; Arguello, F.; Bruguera, J.D.; Doallo, R.; Zapata, E.L. Communications, IEEE Transactions on , Volume: 45 Issue: 2 , Feb. 1997 Page(s): 168 -176  • [8]. Pipelined architectures for the Viterbi algorithm, Boo, M.; Brugera, J.D. TENCON '97. IEEE Region 10 Annual Conference. Speech and Image Technologies for Computing and Telecommunications., Proceedings of IEEE , Volume: 1 , 1997 Page(s): 239 -242 vol.   • [9]. A high speed Viterbi decoder using path limited PRML method and its application to 1/2 inch HD full bit rate digital VCR, Hara, M.; Yoshinaka, T.; Sugizaki, Y.; Ohura, S. Consumer Electronics, 2000. ICCE. 2000 Digest of Technical Papers. Page(s): 96 -97  • [10]. Novel Viterbi decoder VLSI implementation and its performance, Kubota, S.; Kato, S.; Ishitani, T. Communications, IEEE Transactions on, Volume: 41 Issue: 8 , Aug. 1993 Page(s): 1170 –1178   • [11], "A 1-Gb/s, four-state, sliding block Viterbi decoder," P. J. Black, T. H.-Y. Meng, IEEE J. Solid-State Circuits, vol. 32, no. 6, June 1997, pp. 797-805  

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