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More explicitly, 8 provided a brief overview of neural networks and augmented, why channel equalizationcan also beviewed as a classification problem, namely that of classifying the received phasor into one of the M phasors associated with an M-ary modulation scheme We studied the performanceof the RBF equalizerassisted QAM schemes and their adaptive convergence performance in conjunction with both clustering algorithms and LMS channel estimators The RBF equalizer provided superior performance compared to linear the MSE equalizer using an equivalent equalizer orderat the expense of a higher computational complexity, as it was shown in Figure 828 and 829 According to Figure 828 and 829, the RBF equalizer ( m = 9) provided performance improvements of lOdB and 20dB over the linear MSE equalizer fortransmission over two-path and three-path Gaussian channels, respectively, at a BER of lop3.

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We note that both the linear MSE equalizer and the RBF equalizer exhibited residual BER characteristics, if the channel states corresponding to different transmitted symbols are inseparable the channel s output observation space, as it was in shown in Figure 830 The adaptive performance of the RBF equalizer employing the LMS channel estimator of Section 894, the vector centre clustering algorithm of Section 895 and the scalar centre clustering algorithm of Section 810 was compared The convergence rate of the clustering algorithm depends on the number of channel coefficients to be adapted and therefore also on the modulation scheme used as well as on the CIR length However, the convergence of the LMS channel estimation technique only depends on the CIR length and therefore this technique is preferred for high-order modulation schemes and high CIR lengths In Section 8.

1 1 decision feedback was introduced into the RBFequalizer, in order to reduce its computational complexity and to improve its performance, since due to itsemployment the Euclidean distance between the channel states corresponding to different transmitted symbols was increased The performance degradation due to decision error propagation increased as the BER increased,which became more significant for higher-order QAM constellations, as it was shown in Figure 842 The performance degradation for higher-order modulation schemes was higher for fading channel conditions, since are more sensitive they to fades due to the reduced Euclidean distance between the neighbouring channel states We 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 Inseparable channel state clusters were observed for symbol-invariant - but burst-variant - fading, as it was shown in Figure 8.

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48(b), which is due the fading effects manifesting themselves across the to burst duration These phenomena, together with the non-ideal learnt channel states, explain the residual BERs present in our simulations 9 introduced the concept of adaptive modulation invoked for improving the throughput of the system, while maintaining a certain target BER performance The RBF DFE s on-line BER estimation of the received data burst was used as the AQAM modem mode switching metric in order to quantify the channel s quality Our simulation results of Section 945 showed that the proposed RBF DFE-assisted BbB adaptive modem outperformed the individual constituent fixed modulation modes in terms of the mean BER and BPS TheAQAM scheme employingRBF DFEwas compared to theAQAM scheme using a conventional DFE, in terms of mitigating the effects of the dispersive wideband channel Our results in Section 94.

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5 showed that the AQAM RBF DFEscheme was capable performing of as well as the conventional AQAM DFE at a lower decision delay and lower feedforward as well as feedback order It is worth noting futhermore that the performance of the AQAM.

is z* = 2. By Theorem 5.2 all eigenvalues of the matrix A in a stable linear autonomous system (5.1 1) are nonpositive. Using the fact that

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