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As externally powered microsystems become more common, designers need better tools to understand power delivery systems such as non-resonant capacitive coupling. In this paper we present the first general method which allows a designer to easily model power delivery through capacitive coupling. The method uses a power iteration technique which allows one to analyze systems when the time to charge the coupling capacitor is longer than a charge cycle, enabling us to analyze a greater range of systems than previously possible. In fact, we are able to model the entire system with an equivalent resistance. We show that our model accurately reproduces both static and dynamic characteristics of the exact solution and that this model is general, in that it is valid for capacitor charge times that are longer as well as shorter than a charge cycle. This model also reveals several regions of operation where different parameters (e.g., capacitance, frequency and series resistance) dominate, allowing the designer to quickly and intuitively understand the design space for capacitive power transfer. [ABSTRACT FROM PUBLISHER]

In this paper and in its companion, a novel cut-based decomposition for transfer functions of linear time-invariant circuits is introduced and an invariance property, with respect to the cut, is proven for the set of quantities introduced by this cut-based decomposition. These results naturally lead to a novel feedback theory of electric circuits which, unlike Bode's feedback theory, is based on the unambiguous decomposition of transfer functions. Such feedback theory provides a natural extension to the elementary model of feedback amplifiers based on block diagrams, from system theory to circuit theory, and a natural extension to Blackman's formula. [ABSTRACT FROM PUBLISHER]

We propose novel direct and indirect learning predistorters (PDs) that employ a new baseband simplicial canonical piecewise linear (SCPWL) function. The performance of the proposed PDs is easily controlled by varying the number of segments of the SCPWL function. When comparing to polynomial-based PDs, our SCPWL-based PDs are more robust for modeling strong nonlinearities and are less sensitive to input noise. In particular, we show that noise appearing in the feedback path of an indirect learning SCPWL-PD has negligible effect on the performance while the polynomial counterpart suffers from a noise-induced coefficient bias. We consider adaptive implementations of both Hammerstein-based and memory-based SCPWL PDs; the former featuring less parameters to be identified while the latter renders more straightforward parameter identification. When deriving the PD algorithms, we avoid a separate PA identification step which allows for a true real-time, or sample-by-sample, implementation without an alternating PA and PD identification procedure. However, to arrive at efficient sample-by-sample algorithms for Hammerstein PDs we need to bypass the problem of the associated nonconvex cost function. This is done by employing a modified, linear-in-the-parameter, Wiener model whose parameters can be explicitly or implicitly used for both indirect and direct learning. Extensive simulations confirm that the proposed SCPWL PDs outperform their polynomial counterparts, especially when noise is present in the feedback path of the indirect learning structure. The same is also verified by circuit level simulations on the Freescale MRF6S23100H class-AB PA in an 802.16d WiMAX system. [ABSTRACT FROM PUBLISHER]