Effect of Window Size on the Performance of a Turbo Equalizer Using a SWMAP Algorithm

The excellent performance exhibited by the turbo codes is attributed, partly to the use of soft-in soft-out algorithms (SISO) and iterating the soft output between the constituent signal processors in the turbo decoder. The maximum a posteriori probability (MAP) and soft output Viterbi algorithm (SOVA) have been used as the workhorse of receivers that employ turbo principle. The MAP is preferred to SOVA because of its inherent ability to produce the log likelihood ratio (LLR) on each bit. However, it suffers from huge computational complexity, storage requirements. A sliding window MAP (SWMAP) algorithm addresses these two issues. Our objective in this paper is to investigate the effect of different window sizes on the average bit error rate (BER) performance of a turbo equalizer that uses this SWMAP as the equalizer and a SW SOVA algorithm as the decoder. This study is important as it establishes a lower limit on the window size that is acceptable without sacrificing much performance at a reasonable complexity. We evaluate the performance of this receiver by computer simulations. The results obtained for 3 different window sizes establish that, a window of size three times the length of the ISI channel is adequate to ensure a desirable performance with reduced complexity and storage.

[1]  Joachim Hagenauer,et al.  A Viterbi algorithm with soft-decision outputs and its applications , 1989, IEEE Global Telecommunications Conference, 1989, and Exhibition. 'Communications Technology for the 1990s and Beyond.

[2]  Javed I. Khan,et al.  Webspace surfing patterns and their impact on Web prefetching , 2003, Proceedings. 2003 International Conference on Cyberworlds.

[3]  Edith Cohen,et al.  Efficient algorithms for predicting requests to Web servers , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).

[4]  Ming-Syan Chen,et al.  Integrating Web Caching and Web Prefetching in Client-Side Proxies , 2005, IEEE Trans. Parallel Distributed Syst..

[5]  Xin Chen,et al.  Coordinated data prefetching for web contents , 2005, Comput. Commun..

[6]  F Levi,et al.  Mortality from major cancer sites in the European Union, 1955-1998. , 2003, Annals of oncology : official journal of the European Society for Medical Oncology.

[7]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  L. Tabár,et al.  REDUCTION IN MORTALITY FROM BREAST CANCER AFTER MASS SCREENING WITH MAMMOGRAPHY Randomised Trial from the Breast Cancer Screening Working Group of the Swedish National Board of Health and Welfare , 1985, The Lancet.

[9]  Alain Glavieux,et al.  Iterative correction of intersymbol interference : turbo-equalization : Iterative and Turbo decoding , 1995 .

[10]  B. Mandelbrot.  Fractal Geometry of Nature , 1984 .

[11]  John Cocke,et al.  Optimal decoding of linear codes for minimizing symbol error rate (Corresp.) , 1974, IEEE Trans. Inf. Theory.

[12]  Seungwon Shin,et al.  Improving World-Wide-Web performance using domain-top approach to prefetching , 2000, Proceedings Fourth International Conference/Exhibition on High Performance Computing in the Asia-Pacific Region.

[13]  Christos Bouras,et al.  Efficient Reduction of Web Latency through Predictive Prefetching on a WAN , 2003, WAIM.

[14]  S. Kay,et al.  Fractional Brownian Motion: A Maximum Likelihood Estimator and Its Application to Image Texture , 1986, IEEE Transactions on Medical Imaging.

[15]  Alain Glavieux,et al.  Reflections on the Prize Paper : "Near optimum error-correcting coding and decoding: turbo codes" , 1998 .

[16]  Baowen Xu,et al.  Data mining algorithms for web pre-fetching , 2000, Proceedings of the First International Conference on Web Information Systems Engineering.

[17]  Xin Chen,et al.  A Popularity-Based Prediction Model for Web Prefetching , 2003, Computer.

[18]  A. Glavieux,et al.  Near Shannon limit error-correcting coding and decoding: Turbo-codes. 1 , 1993, Proceedings of ICC '93 - IEEE International Conference on Communications.

[19]  Brian D. Davison.  Predicting web actions from HTML content , 2002, HYPERTEXT '02.

[20]  Ana Pont,et al.  Web prefetching performance metrics: A survey , 2006, Perform. Evaluation.

[21]  A. Naim,et al.  Automated morphological classification of APM galaxies by supervised artificial neural networks , 1995, astro-ph/9503001.

[22]  K. Revathy,et al.  Classifying Remotely Sensed Data Using Fractal Features , 2007, Artificial Intelligence and Pattern Recognition.

[23]  Qiang Yang,et al.  Model based predictive prefetching , 2001, 12th International Workshop on Database and Expert Systems Applications.

[24]  Alexander P. Pons.  Object prefetching using semantic links , 2006, DATB.

[25]  Dariush Divsalar,et al.  Serial Concatenation of Interleaved Codes: Performance Analysis, Design, and Iterative Decoding , 1997, IEEE Trans. Inf. Theory.

[26]  K. Revathy,et al.  Galaxy classification using fractal signature , 2003 .

[27]  Darrell D. E. Long,et al.  Exploring the Bounds of Web Latency Reduction from Caching and Prefetching , 1997, USENIX Symposium on Internet Technologies and Systems.

[28]  Alain Glavieux,et al.  Iterative correction of intersymbol interference: Turbo-equalization , 1995, Eur. Trans. Telecommun..

[29]  J.E. Mazo,et al.  Digital communications , 1985, Proceedings of the IEEE.

[30]  B. Mandelbrot.  Self-Affine Fractals and Fractal Dimension , 1985 .

[31]  Yin-Fu Huang,et al.  Mining web logs to improve hit ratios of prefetching and caching , 2008, Knowl. Based Syst..