Manuel E. Guzman-Renteria

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In this correspondence, the use of superimposed training (ST) as a mean to estimate the finite impulse response (FIR) components of a widely linear (WL) system is proposed. The estimator here presented is based on the first-order statistics of the signal observed at the output of the system and its variance is independent of the channel components if(More)
This paper presents a flexible and portable digital framework for Built-in Self-Test (BIST) and calibration of RF/analog circuitry. Novel to the proposed testing framework, is a reusable, flexible, dropin IP core, composed of a centralized custom processing engine with data path, memory architecture and instruction set optimized for efficient execution of(More)
A programmable digital engine for RF/analog on/off-line calibration and Built-in Self-Test (BIST) enables a new level of robustness and portability for mixed signal SoCs. The drop-in IP-block is based on a dedicated CPU with data path and instruction set extensions optimized for the compute intensive RF calibration algorithms. The concept is demonstrated(More)
In this paper, the training sequence synchronization (TSS) problem for Widely Linear (WL) system estimation is addressed. This problem appears when superimposed training (ST) is considered as the methodology to perform the identification of WL systems. The proposed method exploits the ciclostationarity induced in the process at the output of the WL system(More)
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