This paper presents an optimized Waveform Relaxation solver for electrically-long high-speed channels terminated by nonlinear networks. The time-domain scattering operators of channel and terminations are cast as recursive convolutions and nonlinear discrete-time filters, respectively. A transverse and longitudinal decoupling is then applied to the channel operator, with the introduction of suitable relaxation sources, and solved iteratively until convergence. A frequency-dependent over-relaxation parameter is introduced in order to optimize the convergence rate. Numerical results show significantly reduced runtime and iteration count for critical benchmarks with respect to previous Waveform Relaxation formulations.
This paper presents an optimized Waveform Relaxation solver for electrically-long high-speed channels terminated by nonlinear networks. The time-domain scattering operators of channel and terminations are cast as recursive convolutions and nonlinear discrete-time filters, respectively. A transverse and longitudinal decoupling is then applied to the channel operator, with the introduction of suitable relaxation sources, and solved iteratively until convergence. A frequency-dependent over-relaxation parameter is introduced in order to optimize the convergence rate. Numerical results show significantly reduced runtime and iteration count for critical benchmarks with respect to previous Waveform Relaxation formulations.
“…These accelerating techniques include windowing, Krylov subspace methods, matrix splitting etc. (see [7,9,10,12,14,16]). These techniques, of which the simplest one is windowing, can speed up convergence behaviors of WR.…”
We present a windowing technique of waveform relaxation for dynamic systems. An effective estimation on window length is derived by an iterative error expression provided here. Relaxation processes can be speeded up if one takes the windowing technique in advance. Numerical experiments are given to further illustrate the theoretical analysis.
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