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In Figure5, the relationship between the system delay and crosstalk canceller tap budget is shown.
Note that there is often a tradeoff between the system delay and the data approximation [8,16].
Figure 4 MSEs comparison for different SNRs. Figure 5 shows tradeoffs between the system delay and the MSE for the proposed adaptive sparse random projections and the conventional ones when SNR = 30 dB and k=5.
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Figure 5 Tradeoff between the MSE and the system delay for SNR = 30 dB and k=5.
In this paper, we propose an adaptive algorithm for sparse random projections in order to achieve a better tradeoff between the MSE and the system delay.
Compared to the conventional sparse random projections which keep the sparsity constant for the whole transmission slots, the proposed one achieves a better tradeoff between the MSE and the system delay.
We observe that the proposed adaptive sparse random projections achieves better tradeoff between the MSE and the system delay than the conventional one does when k is either 10 or 5.
We still observe that the proposed scheme outperforms the conventional one for both SNR = 20 dB and 30 dB resulting in a better tradeoff between the MSE and the system delay.
This shows that the system delay remains between the two thresholds, with delay-sensitive flows meeting their desired QoS/QoE and best-effort flows maximizing the system throughput.
Although the decaying rate and the stop-band attenuation increase with increasing the overlapping factor K, the choice of K=4 is considered as a good trade-off between the stop-band attenuation and the system delay caused by the time spread of the filter [36].
T sys denotes the system delay.
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