DESIGN OF EMBEDDED FILTERS FOR INNER-LOOP POWER CONTROL IN WIRELESS CDMA COMMUNICATION SYSTEMS

ABSTRACT We study inner-loop power control for mobile wireless communication systems using code division multiple access transmission. We focus on the uplink, i.e., on communication from the mobile-to the base-station, and show how to minimise the variance of the signal-to-interference ratio (SIR) tracking error through incorporation of recursive filters. These filters complement existing power controllers and are designed by using a linear model which takes into account quantisation of the power control signal, dynamics of channel gains, interference from other users, target SIR, and SIR estimation errors. Simulation results indicate that significant performance gains can be obtained, even in situations where the models used for design are only an approximation

Sustainability in Industrial Logistics Operations

CO2 concentration in the atmosphere has been rising at a rapid rate. As per the numbers quoted, the concentration has surpassed the safest upper limit suggested by the scientists. CO2 is a green-house gas and is partially responsible for global warming. With the current rate of emissions, it poses a threat to the planet\u27s future and prosperity. Transportation contributes 30% of the overall CO2 emissions. Companies that use transportation are taking sustainability initiatives to reduce the emissions. This project documented some of the sustainability practices followed by the companies currently. It was found that the practices followed three general methods to reduce the emissions

Input-output data sets for development and benchmarking in nonlinear identification

This report presents two sets of data, suitable for development, testing and benchmarking of system identification algorithms for nonlinear processes. The first data set is recorded from a laboratory process that can be well described by a block oriented nonlinear model. The data set is challenging; it consists of only 500 samples, the nonlinear effect is large and the damping is not too good. The second data set is recorded from a laboratory process known to be governed by nonlinear differential equations

MATLAB Software for Nonlinear and Delayed Recursive Identification : Revision 1

This report is the user's manual for a package of MATLAB™ scripts and functions, developed for recursive prediction error identification of nonlinear state space systems. The identified state space model incorporates delay, which allows a treatment of general nonlinear networked identification, as well as of general nonlinear systems with delay. The core of the package is an implementation of an output error identification algorithm. The algorithm is based on a continuous time, structured black box state space model of a nonlinear system. The software can only be run off-line, i.e. no true real time operation is possible. The algorithms are however implemented so that true on-line operation can be obtained by extraction of the main algorithmic loop. The user must then provide the real time environment. The software package contains scripts and functions that allow the user to either input live measurements or to generate test data by simulation. The scripts and functions for the setup and execution of the identification algorithms are somewhat more general than what is described in the references. The functionality for display of results include scripts for plotting of e.g. data, parameters, prediction errors, eigenvalues and the condition number of the Hessian. The estimated model obtained at the end of a run can be simulated and the model output plotted, alone or together with the data used for identification. Model validation is supported by two methods apart from the display functionality. First, a calculation of the RPEM loss function can be performed, using parameters obtained at the end of an identification run. Secondly, the accuracy as a function of the output signal amplitude can be assessed

MATLAB Software for Nonlinear and Delayed Recursive Identification : Revision 2

This report is the user’s manual for a package of MATLAB™ scripts and functions, developed for recursive prediction error identification of nonlinear state space systems. The identified state space model incorporates delay, which allows a treatment of general nonlinear networked identification, as well as of general nonlinear systems with delay. The core of the package is an implementation of two output error identification algorithms. The algorithms are based on a continuous time, structured black box state space model of a nonlinear system. The present revision adds a new algorithm, where also the output is determined via a parameterized measurement equation in the states and inputs. The software can only be run off-line, i.e. no true real time operation is possible. The algorithms are however implemented so that true on-line operation can be obtained by extraction of the main algorithmic loop. The user must then provide the real time environment. The software package contains scripts and functions that allow the user to either input live measurements or to generate test data by simulation. The scripts and functions for the setup and execution of the identification algorithms are somewhat more general than what is described in the references. The functionality for display of results include scripts for plotting of e.g. data, parameters, prediction errors, eigenvalues and the condition number of the Hessian. The estimated model obtained at the end of a run can be simulated and the model output plotted, alone or together with the data used for identification. Model validation is supported by two methods apart from the display functionality. First, a calculation of the RPEM loss function can be performed, using parameters obtained at the end of an identification run. Secondly, the accuracy as a function of the output signal amplitude can be assessed

MATLAB Software for Nonlinear and Delayed Recursive Identification : Revision 1

This report is the user's manual for a package of MATLAB™ scripts and functions, developed for recursive prediction error identification of nonlinear state space systems. The identified state space model incorporates delay, which allows a treatment of general nonlinear networked identification, as well as of general nonlinear systems with delay. The core of the package is an implementation of an output error identification algorithm. The algorithm is based on a continuous time, structured black box state space model of a nonlinear system. The software can only be run off-line, i.e. no true real time operation is possible. The algorithms are however implemented so that true on-line operation can be obtained by extraction of the main algorithmic loop. The user must then provide the real time environment. The software package contains scripts and functions that allow the user to either input live measurements or to generate test data by simulation. The scripts and functions for the setup and execution of the identification algorithms are somewhat more general than what is described in the references. The functionality for display of results include scripts for plotting of e.g. data, parameters, prediction errors, eigenvalues and the condition number of the Hessian. The estimated model obtained at the end of a run can be simulated and the model output plotted, alone or together with the data used for identification. Model validation is supported by two methods apart from the display functionality. First, a calculation of the RPEM loss function can be performed, using parameters obtained at the end of an identification run. Secondly, the accuracy as a function of the output signal amplitude can be assessed

MATLAB Software for Identification of Nonlinear Autonomous Systems : Revision 1

This report is intended as a user's manual for a package of MATLAB™ scripts and functions, developed for recursive and batch identification of nonlinear autonomous state space models of order 2. The core of the package consists of implementations of four algorithms for this purpose. There are two least squares batch schemes and two recursive algorithms based on Kalman filtering techniques. The algorithms are based on a continuous time, structured black box state space model of a nonlinear autonomous system of order 2. The software can only be run off-line, i.e. no true real time operation is possible. The recursive algorithms are however implemented so that true on-line operation can be obtained by extraction of the main algorithmic loops. The user must then provide the real time environment. The software package contains scripts and functions that allow the user to either input live measurements or to generate test data by simulation. The functionality for display of results include scripts for plotting of data and parameters. The estimated model obtained at the end of a run can be simulated and the model output plotted, alone or together with the data used for identification