This paper makes a connection between blood pressure pulsatility and baroreflex gain in human subjects. While baroreflex gain may be difficult to examine, low frequency oscillations found in human blood pressure can be used as a surrogate coarse measurement of the baroreflex gain. Analysis is performed via a mathematical model that examines the presence or absence of a low frequency oscillation in human blood pressure signal. Mayer waves are well-known low frequency oscillations that occur in human blood pressure signals at approximately 0.1Hz. This paper proposes that an examination of Mayer waves can be used to examine variation in baroreflex gain. This examination could provide more information on the effect of an absence of pulsatility in left ventricular assist device (LVAD) patients.

In recent years model and Digital Pre-Distortion dimension reduction has been widely researched. The operations involved when running DPD are often far less than those needed during the training of the DPD coefficients. The proposed partitioned Least Squares (LS) adaptation allows a selected subset of DPD coefficients to be updated while the remaining coefficients are held constant. This technique allows a more adaptive training procedure, improved interpretability of the important DPD coefficient's during training and the ability to partition the DPD function into specific groups. The Frisch-Waugh-Lovell (FWL) theorem is exploited to partition the coefficients of a DPD basis function trained using LS regression. The proposed methodology was experimentally validated with a Generalized Memory Polynomial (GMP) DPD function, used to linearize a 5W power amplifier (PA) driven by a 40MHz 5G-NR signal.

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As the physical makeup of cellular base-stations evolve into systems with multiple parallel transmission paths the effort involved in modelling these complex systems increases considerably. One task in particular which contributes to signal distortion on each signal path, is the power amplifier. In power amplifier modelling, Recursive Least Squares has been used in the past to train Volterra models with memory terms, however instability can occur when training the model weights. This manuscript provides a computationally efficient technique to detect the onset of instability and subsequently to inform the decision when to stop adaptive training of dynamic nonlinear behavioural models and avoid the onset of instability. This technique is experimentally validated using four different signal modulation schemes.

As the physical makeup of cellular basestations evolve into systems with multiple parallel transmission paths the effort involved in modelling these complex systems increases considerably. One task in particular which contributes to signal distortion on each signal path, is the power amplifier. In power amplifier (PA) modelling, Recursive Least Squares (RLS) has been used in the past to train Volterra models with memory terms. The Volterra model is widely used for modelling of PAs. In this paper we present a comparison of the stability performance for a PA model during training for various model memory lengths, model orders of non linearity and signal sample rates. This examination provides a technique to avoid instability occurring during the adaptive training of dynamic nonlinear behavioural models.