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Decision feedback was then introduced into the RBF equaliser, in order to reduceits computational complexity As a result, its performance improved, since the Euclidean distance between the channel states corresponding to different transmitted symbols increased, when the DFE scheme was used Recall that the parameters of the RBF DFE were chosen to be m = r 1 and n = L, where m, n, r and L 1 are the feedforward order, feedback order, delay and CIR length, which provide the best solution for a fixed equalizer delay T As expected, the performance degradation due to decision error propagation increased,the as BER increased, which became more significant for higher order QAM, as it was shown in Figure 842.

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For fading channel conditions, the performance degradation for higher order modulation schemes were higher, since they are more sensitive to fades due to the reduced Euclidean distance between the neighbouring channel states We investigated the performance of the adaptive RBF equalizer in symbol- and burstinvariant fading scenarios We observed the effects of inseparable channel state clusters for symbol-invariant fading in Figure 848(b), which was due to the fast-fading effects present across the burst duration This phenomenon, conjunction with thenon-ideal learnt channel in states, explain the residual BER present in our simulations Therefore, we have to note that even for relatively slow fading channels, the channel states value can change significantly on a symbol-by-symbol basis in a transmission burst duration In the next chapter, we will proceed to investigate the implementation and performance of the RBF equalizer in the context of adaptive modulation schemes..

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Opposed to the above variant, we can also postpone the normalisation, as we have done in the Gaussian elimination case. We then decompose A = QT = LU, for which we will find that the last row of U contains only 0 s. The solution of Lx = 0 will then always yield z - = 0, so that we can immediately solve Up- = 0. This triangular system of equations can easily be solved via a back-substitution procedure; however, we have to assume PN = o and compute the rest of p relative to a as well. A final normalisation ultimate steady-state probability vector p. Postponing the normalisation is prefirred will then yield the (i)

6. In the New User dialog box (see Figure 24-6), type the user information in the appropriate fields.

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