Michael Lunglmayr

Approximate least squares

We present a novel iterative algorithm for approximating the linear least squares solution with low complexity. After a motivation of the algorithm we discuss the algorithms properties including its complexity, and we present theoretical results as well as simulation based performance results. We describe the analysis of its convergence behavior and show that in the noise free case the algorithm converges to the least squares solution.

Least Squares Equalization for RFID

The continuously increasing number of applications for contactless smartcard systems also led to an increase of the amount of data that is transmitted from RFID tags to reader devices. This also requires high transmission speeds beyond one Mbit/s. Recently a prototype transmitting at

Step-adaptive approximate least squares

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Efficient linearized Bregman iteration for sparse adaptive filters and Kaczmarz solvers

Mbits/s was proposed. When transmitting data from an RFID tag to an RFID reader at such high transmission speeds the transmission suffers from inter-symbol interference. Contrary to standard communication scenarios this inter-symbol interference (ISI) is not caused by linear time invariant systems.Instead the transmission by load modulation can be interpreted as transmission through a non-linear system. For counteracting the intersymbol interference we propose a least squares (LS) equalization method. This method does not rely on the assumption that ISI is caused by a linear system but only constructs a linear system to correct ISI. We discuss the application of LS equalization for tag to reader communication by load modulation.We show how an LS equalizer can be incorporated in a RFID receiver and show by simulation that least squares equalization represents a feasible method for counteracting ISI in RFID systems. In addition simulation results concerning the effect of the training sequence length on the equalizers performance are shown.

Battery state-of-charge estimation prototype using EMF voltage prediction

Recently, we proposed approximate least squares (ALS), a low complexity approach to solve the linear least squares problem. In this work we present the step-adaptive linear least squares (SALS) algorithm, an extension of the ALS approach that significantly reduces its approximation error. We theoretically motivate the extension of the algorithm, and introduce a low complexity implementation scheme. Our performance simulations exhibit that SALS features a practically negligible error compared to the exact LS solution that is achieved with only a marginal complexity increase compared to ALS. This performance gain is achieved with about the same low computational complexity as the original ALS approach.

Vertex Packing Decoding

Linearized Bregman iterations are low complexity and high precision approaches for solving the combined l1/l2 minimization problem. In this work we give a derivation of the linearized Bregman iteration and show the links to Kaczmarzs algorithm as well as to sparse least mean squares (LMS) filters. We present a novel extension allowing to perform combined l1/l2 minimization either in an LMS based adaptive filter or in a Kaczmarz based batch solution. By means of simulations we demonstrate that the performance of our extension is comparable to the original linearized Bregman approaches. Furthermore, we show that with this extension l1/l2 minimization can be performed with less complexity than the corresponding l2 minimization.

Feasibility study of particle filters for mobile station receivers

A reliable knowledge of cell parameters like the state-of-charge (SoC) is essential for the optimization of battery-powered applications. Usually, during relaxation (the phase of no or low loads) the SoC is determined based on the measurement of the batterys electro-motive force (EMF). To obtain a reliable measurment, it is required that the battery voltage transient is in a well-relaxed state, which is rarely reached in practice (e.g. due to periodic discharge activities). In this paper, a predictive methodology is presented which is able to forecast the EMF and therewith the SoC already during a not well-relaxed state of the voltage transient. A nonlinear relaxation voltage model is reformulated such that the problem can be treated as a linear least squares estimation problem. Based on this estimation, the performance is evaluated with respect to the following aspects: prediction time, current rate influence, SoC influence, cell-to-cell deviation, or rather aging and temperature effects. Experimental results are presented for a fixed-point implementation of the estimation scheme on a CY8CKIT-050 PSOC5 programmable system on chip. For validation, measurements of 2.25Ah Sanyo UR18650A lithium cells have been used. It is shown that the presented approach offers an improved re-initialization methodology for the Coulomb counting method, and that it clearly outperforms the usual EMF-measurement based SoC determination method.

Digital compensation of DC-DC converter voltage ripple for Switched-Capacitor Power Amplifiers

In this work, we present a new framework for developing decoding algorithms for linear block codes. We define a new graphical model, called configuration graph and show that maximum likelihood (ML) sequence decoding can be performed by finding a maximum vertex packing on a configuration graph. We present examples of low-cost decoding algorithms developed using this graphical model and show that remarkable performance improvements can be achieved especially by combining these algorithms with belief propagation (BP) decoding for Low Density Parity Check (LDPC) codes.

Simulation based optimization of signal processing for RFID

The application of an equalisation method for mobile station terminals using particle filters is presented. To use particle filters for equalisation, a mathematical model is shown which allows the transmitted symbols to be represented as the state of a stochastic system, which can be estimated by particle filters. The authors propose an equaliser structure with particle filters for application in mobile station receivers, especially for GSM/EDGE (Global System for Mobile Communications/Enhanced Data Rate for GSM Evolution). Several improvement strategies, which help to obtain better estimation results with a lower number of particles, are discussed. In addition, performance evaluations of the particle filter equaliser for GSM/EDGE are presented.

Scaled linearized Bregman iterations for fixed point implementation

This paper presents a novel method to digitally compensate the voltage ripple of a DC-DC switching converter supplying a Switched-Capacitor Power Amplifier (SCPA). Switched DC-DC converters are highly efficient, but their output voltage exhibits a small switching ripple. These unwanted variations on the supply of the SCPA mix with the input code and create unwanted harmonics in the output signal. This effect decreases the linearity of the SCPA, and potentially causes violations of the spectral mask. Currently, additional circuitry is used to block the DC-DC voltage ripple and provide a stable power supply for the SCPA. Such circuits decrease the efficiency of the system and need additional active and passive components on and off-chip. This work proposes a digital compensation of the distortions of the DC-DC voltage ripple in the SCPA to omit additional circuitry in the SCPA supply. Furthermore, a proper solution to eliminate timing mismatches of the modulated input code and the true ripple at the SCPA is presented. The concept has been validated with MATLAB/Simulink and shows a significant suppression of the distortions created by the DC-DC voltage ripple.