Rainer Laur

Numerical steady state analysis of electronic circuits driven by multi-tone signals

ContentsCharacteristics of analogue circuits such as intermodulation distortion and transfer characteristics can often be received from the steady state behavior. This paper presents a unified approach for the simulation of non-autonomous circuits with multi-tone excitation. The steady state is here regarded as the solution of a partial differential-algebraic equation. A suitable numerical method for its solution is a variational method with trigonometric basis functions. The Harmonic Balance technique based either on the multi-dimensional Fourier transformation or the Artificial Frequency Map technique can be interpreted as a special variant of this method.ÜbersichtDie Eigenschaften analoger Schaltungen, die etwa Intermodulationsverzerrungen und Übertragungscharakteristiken beschreiben, lassen sich häufig im eingeschwungenen Zustands ermitteln. Dieser Beitrag stellt ein vereinheitlichtes Verfahren zur Simulation von nicht-autonomen Schaltungen bei einer Mehrton-Erregung vor. Der eingeschwungene Zustand wird als spezielle Lösung einer partiellen Algebro-Differentialgleichung formuliert. Zur numerischen Berechnung eignen sich Variationsverfahren mit trigonometrischen Ansatzfunktionen. Interessant ist, daß das Verfahren der Harmonischen Balance, sowohl basierend auf einer mehrdimensionalen Fouriertransformation als auch basierend auf einer Transformation auf ein künstliches Hilfsspektrum, als spezielle Variante dieses Ansatzes angesehen werden kann.

Parameter Study for Differential Evolution Using a Power Allocation Problem Including Interference Cancellation

The performance of evolutionary algorithms is strongly dependent on the setting of control parameters. Not only the convergence speed is influenced, but also if the optimum of a function is reached at all. For differential evolution premature convergence or even stagnation can occur due to certain parameter settings. In this paper a parameter study for differential evolution is conducted. As basis for the examination a real-world problem is employed that consists of optimizing the power allocation for a CDMA (code division multiple access) system. For the CDMA system interference cancellation methods are applied as the detection performance is significantly degraded by multi-user interference. The convergence of the interference cancellation method establishes a constraint for the single-objective optimization problem. Optimization results for both parallel and successive interference cancellation are given. The findings of the parameter study are compared with results from literature, and recommendations concerning settings of DE control parameters are given.

MEDUSA--A Simulator for Modular Circuits

A modular circuit is defined as a combination of a certain number of k-modules, which are embedded in a carrier network. The modules may represent subcircuits of varying complexity down to single devices. Such modular circuits lend themselves to a description by a system of modular equations. MEDUSA is a user-oriented simulator which utilizes system modularity during the simulation process itself, i.e., for solving the underlying equations. This approach enables the consistent numerical solution of both the basic equations of several bipolar devices and the network equations for a circuit environment. Hence, MEDUSA in its present state is a merged device-circuit simulator meeting simultaneously the requirements of device and circuit design.

Constrained Single-Objective Optimization Using Differential Evolution

Differential evolution (DE) is a rather new evolutionary optimization algorithm that has been shown to be fast and simple for unconstrained single-objective optimization problems. In this work DE is employed for the constrained optimization test suite of the special session on constrained real parameter optimization at CEC06. Constraints are handled using a modified selection procedure that does not require additional parameters. For the control parameters of the DE algorithm the best found settings from another examination were used so that almost no parameter tuning was necessary. Most of the test functions are successfully and reliably optimized. Difficulties arise mainly from a high number of equality constraints.

Optimization of Power Allocation for Interference Cancellation With Particle Swarm Optimization

In code division multiple access (CDMA) systems a significant degradation in detection performance due to multiuser interference can be avoided by the utilization of interference cancellation methods. Further enhancement can be obtained by optimizing the power allocation of the users. The resulting constrained single-objective optimization problem is solved here by means of particle swarm optimization (PSO). It is shown that the maximum number of users for a CDMA system can be increased significantly if an optimized power profile is employed. Furthermore, an extensive study of PSO control parameter settings using three different neighborhood topologies is performed on the basis of the power allocation problem, and two constraint-handling techniques are evaluated. Results from the parameter study are compared with examinations from the literature. It is shown that the von-Neumann neighborhood topology performs consistently better than gbest and lbest. However, strong interaction effects and conflicting recommendations for parameter settings are found that emphasize the need for adaptive approaches.

Stopping Criteria for Differential Evolution in Constrained Single-Objective Optimization

Because real-world problems generally include computationally expensive objective and constraint functions, an optimization run should be terminated as soon as convergence to the optimum has been obtained. However, detection of this condition is not a trivial task. Because the global optimum is usually unknown, distance measures cannot be applied for this purpose. Stopping after a predefined number of function evaluations has not only the disadvantage that trial-and-error methods have to be applied for determining a suitable number of function evaluations, but the number of function evaluations at which convergence occurs may also be subject to large fluctuations due to the randomness involved in evolutionary algorithms. Therefore, stopping criteria should be applied which react adaptively to the state of the optimization run. In this work several stopping criteria are introduced that consider the improvement, movement or distribution of population members to derive a suitable time for terminating the Differential Evolution algorithm. Their application for other evolutionary algorithms is also discussed. Based on an extensive test set the criteria are evaluated using Differential Evolution, and it is shown that a distribution-based criterion considering objective space yields the best results concerning the convergence rate as well as the additional computational effort.

Intelligent Containers and Sensor Networks Approaches to apply Autonomous Cooperation on Systems with limited Resources

RFIDs, sensor networks and low-power microcontrollers are increasingly applied in logistics. They are characterized by restrictions on calculation power, communication range and battery lifetime. In this article we consider how these new technologies can be utilized for autonomous cooperation and how these processes could be realized on systems with limited resources.

On the VLSI implementation of the international data encryption algorithm IDEA

This paper describes a new VLSI realization of the International Data Encryption Algorithm IDEA. The presented VLSI architecture is compared with the VINCI chip, the first silicon realization of the IDEA. Principal design aspects are also discussed. Although the VINCI circuit can be used for real-time encryption in high-speed networks such as ATM, there are requirements for VLSI circuits with faster encryption ability. Further, the implemented test during the normal operation in the VINCI chip cannot detect all possible errors. The proposed new implementation is motivated by the requirement for higher data rates and a new solution for the on-line test problem, which is a main task in implementing cryptographic algorithms. First design results show that the resulting circuit can achieve an encryption rate of about 355 Mb/s. This is achieved by the implementation of one round in a 0.8 /spl mu/m CMOS technology. The architecture includes a concurrent self-test based on a mod-3 residue code self-checking system which allows the detection of permanent and temporary single- and multiple-bit errors in the IDEA datapath and hence the secure prevention of faulty encrypted or decrypted data.

Constrained Single-Objective Optimization Using Particle Swarm Optimization

Particle Swarm Optimization (PSO) is an optimization method that is derived from the behavior of social groups like bird flocks or fish schools. In this work PSO is used for the optimization of the constrained test suite of the special session on constrained real parameter optimization at CEC06. Constraint-handling is done by modifying the procedure for determining personal and neighborhood best particles. No additional parameters are needed for the handling of constraints. Numerical results are presented, and statements are given about which types of functions have been successfully optimized and which features present difficulties.

A novel time-frequency method for the simulation of the steady state of circuits driven by multi-tone signals

In this paper we introduce a novel algorithm for efficient computing of the steady state of circuits driven by multi-tone excitation. The efficiency is the result of combining finite difference and the harmonic balance techniques. Unlike existing techniques the algorithm shown here is based on reformulating the underlying system of ordinary DAEs by a system of partial DAEs. The steady state leads to simple boundary conditions for these partial DAEs.