Paul Edward Strauch

Active control of nonlinear noise processes in a linear duct

This paper investigates two scenarios in active noise control (ANC) that lead to performance degradation with conventional linear control techniques. The first scenario addresses the noise itself. The low-frequency noise, traveling as plane waves in a duct, is usually assumed to be broadband random or periodic tonal noise. Linear techniques applied to actively control this noise have been shown to be successful. However, in many practical applications, the noise often arises from dynamical systems, which cause the noise to be nonlinear and deterministic or stochastic, colored, and non-Gaussian. Linear techniques cannot fully exploit the coherence in the noise and, therefore, perform suboptimally. The other scenario is that the actuator in an ANC system has been shown to be nonminimum phase. One of the tasks of the controller, in ANC systems, is to model the inverse of the actuator. Obviously, a linear controller is not able to perform that task. To combat the problems, as mentioned above, a nonlinear controller has been implemented in the ANC system. It is shown in this paper that the nonlinear controller consists of two parts: a linear system identification part and a nonlinear prediction part. The standard filtered-x algorithms cannot be used with a nonlinear controller, and therefore, the control scheme was reconfigured. Computer simulations have been carried out and confirm the theoretical derivations for the combined nonlinear and linear controller.

Advanced signal-processing algorithms for energy-efficient wireless communications

Substantial progress has been made in the receiver signal-processing algorithms for wireless communications to minimize the requirements on signal-to-noise (and/or interference) power ratio and computational complexities for the same quality of service. In cellular infrastructure systems, one of the key system design objectives in the base stations is to maximize the receiver sensitivity, so that the required signal level from the mobile stations can be minimized. The use of advance signal-processing algorithms, based on maximum a posteriori (MAP) estimation, iterative (turbo) channel estimation, equalization, and decoding, allows for a reduction of the required transmitter power by one-third to one-half. Lower computational complexities in the terminals, which implies a reduced power drain on the digital circuits, can be achieved by using techniques that adapt the state complexity of the receiver to the propagation channel. We give an in-depth review of these algorithms, and discuss their performance and implementation requirements.

Comparison of linear and MLSE spatio-temporal interference rejection combining with an antenna array in a GSM system

By using multiple receiving antennas and spatial or spatio-temporal processing techniques cochannel interference (CCI) may be suppressed. The maximum likelihood (ML) estimator requires a spatio-temporal model of the CCI. The model may be inaccurate because of estimation errors or difficulties in the parametrization of the generally asynchronous CCI. In this case linear spatio-temporal filtering based on training or semi-blind algorithms can be competitive compared to the ML-based algorithms. This paper compares the performance of ML and linear spatio-temporal techniques in synchronous and asynchronous interference-limited GSM urban scenarios. The best results for the asynchronous CCI and/or propagation channels with fading are obtained with a linear spatio-temporal semi-blind filtering technique based on minimization of the dual least squares constant modulus criterion.

Off-line frequency correction for equalization in the EDGE system

A robust and low complexity receiver is addressed, which meets the EDGE specification requirements by using an off-line non-adaptive equalizer. Specifically, the problem of correcting a random burst-by-burst frequency offset in an EDGE receiver is studied. Simulation results show that the EDGE (handset) requirements for the TU50 propagation scenario can be met for all coding schemes with 8-PSK modulation by using a simple off-line training-based DFE with decision-directed frequency offset correction.