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[287] S K Patra and B Mulgrew, Computational aspects of adaptive radial basis function equalizer design, in IEEE International Symposium on Circuits and Systems, ISCAS 97, vol 1, pp 521-524, IEEE, Piscataway, NI, USA, June 1997 [288] P Robertson, E Villebrun, and P Hoher, A comparison of optimal and sub-optimal map decoding algorithms operating in the log domain, IEEE lnrernational Conference on Communications,pp 1009-1013, 1995 [289] J Erfanian, S Pasupathy, and G Gulak, Reduced complexity symbol dectectors with parallel structures for IS1 channels, IEEE Transactions on Communications,vol 42, pp 1661-1671, 1994 [290] S Chen, B Mulgrew, E-S Chng, and G J Gibson, Space translation properties and the minimum-BER linear-combiner D E , IEE Proceedings on Communications,vol 145, pp 316-322, October 1998 [291] S Chen, Importance sampling simulation for evaluation the lower-bound BER of the bayesian DFE submitted to IEEE Transactions on Communications [292] S Chen, E-S Chng, B.



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In each test series we varied the parameters n (number of vertices) and r (number of services) as follows: n {50, 100}, r {10, 20, 50, 100, 250}. For each of the 10 resulting parameter combinations we generated 20 random problem instances with the same number of vertices and services i.e., a total of 200 instances in each series for which we computed the following values: TS: number of colors calculated for the best ensemble assignment found by TS GFF: number of colors calculated for a GFF ensemble assignment SFF: number of colors calculated for a SFF ensemble assignment BEST: min{TS, GFF, SFF} S: lower bound on the clique packing number, using the generalized Carraghan/ Pardalos algorithm [4] with the sum estimate for the packing number [10]. Both the SFF and GFF algorithms were invoked using a service order by decreasing sizes. Coloring was always done using the saturation algorithm [2]. In the case of SFF and GFF, we furthermore employed an iterated version of the saturation algorithm with 100 runs per coloring. Thus, especially for GFF, we can expect slightly better results than given in [10]. For the TS algorithm, we employed the termination criterion discussed earlier, that is, the search was terminated after a prescribed number N of iterations without an improvement on the best known solution. However, the number of iterations was chosen in relation to the size of the problem instance, using the following heuristic formula: N= where D is the density of the graph, D= 2|E| |V |(|V | 1)





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+ N d p = 0, + i,dP,

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Mulgrew, and G J Gibson, Minimum-BER linear-combiner DFE, in Proceedings o ICC 96, (Dallas, Texas), pp 1173-1 177, 1996 f [293] S Chen, S Gunn, and C JHarris, Decision feedback equalizer design using support vector machines, IEE Proceedings Vision, Image and Signal Processing, 2000 Accepted for publication [294] S Chen and C J Harris, Design of the optimal separating hyperplane for the decision feedback equalizer using support vector machine, in Proceedings ofIEEE International Conference on Accoustic, Speech and Signal Processing, pp 2701-2704, IEEE, June 5-9 2000 [295] B Sklar, Rayleigh fading channels in mobile digital communication systems, part 1: Characterization, IEEE Communications Magazine, vol 35, pp 136146, September 1997 [296] C H Wong, Wideband Adaptive Full Response Multilevel Transceivers and Equalizers PhD thesis, University of Southampton, United Kingdom, November 1999 [297] W Press, S Teukolsky, W Vetterling, and B Flannery, Numerical Recipes in C Cambridge: Cambridge University Press, 1992.

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col{d^(nB+ B - I), . . . ,d^(nB I ) ,d^(nB)} c o l { e ( n B + B - I), . . . ,e(nB 1 ) , e ( n B ) }

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