José Manuel Pardo-Martín

An adaptive phase alignment algorithm for cartesian feedback loops [Applications corner]

An adaptive algorithm to correct phase misalignments in Cartesian feedback linearization loops for power amplifiers has been presented. It yields an error smaller than 0.035 rad between forward and feedback loop signals once convergence is reached. Because this algorithm enables a feedback system to process forward and feedback samples belonging to almost the same algorithm iteration, it is suitable to improve the performance not only of power amplifiers but also any other digital feedback system for communications systems and circuits such as all digital phase locked loops. Synchronizing forward and feedback paths of Cartesian feedback loops takes a small period of time after the system starts up. The phase alignment algorithm needs to converge before the feedback Cartesian loop can start its ideal behavior. However, once the steady state is reached, both paths can be considered synchronized, and the Cartesian feedback loop will only depend on the loop parameters (open-loop gain, loop bandwidth, etc.). It means that the linearization process will also depend only on these parameters since the misalignment effect disappears. Therefore, this algorithm relieves the power amplifier linearizer circuit design of any task required for solving phase misalignment effects inherent to Cartesian feedback systems. Furthermore, when a feedback Cartesian loop has to be designed, the designer can consider that forward and feedback paths are synchronized, since the phase alignment algorithm will do this task. This will reduce the simulation complexity. Then, all efforts are applied to determining the suitable loop parameters that will make the linearization process more efficient.

Antennas' correlation influence on the GMD-assisted MIMO channels performance

The use of multiple antennas in MIMO (multiple-input multiple-output) systems at both the transmit and receive sides produces the effect known as antennas correlation which impact the overall channel performance, throughput and bit-error rate (BER). The geometric mean decomposition (GMD) is a signal processing technique which can be used to process transmit and receive signals in MIMO channels. The GMD pre- and post-procesing in conjunction with dirty-paper precoding shows some advantages over the popular singular value decomposition (SVD) technique which provides GMD-assisted MIMO systems a superior performance particularly when the channel is affected by antennas correlation. This paper analyses the impact of antennas correlation on the performance of GMD-assisted wireless MIMO channels highlighting the advantages over SVD-assisted ones.

Efficient adaptive compensation of I/Q imbalances using spectral coherence with monobit kernel

Abstract This paper shows a new algorithm to improve the performance of IQ demodulators and frequency converters exhibiting gain and phase imbalances between their branches. This algorithm does not require any input calibration signal and is independent of the input signal level. It exploits the spectral coherence (SC) concept using a monobit kernel to achieve optimization targets with minimum time to convergence, low computational load, and a wide range of input levels. Its effectiveness is shown through a low-IF receiver that improves its image rejection ratio (IRR) from 30 dB to 60 dB.