In this paper, we propose a modified-error adaptive feedback active noise control (ANC) system using a linear prediction filter. The proposed ANC system is advantageous in terms of the rate of convergence, while maintaining stability, because it can reduce narrowband noise while suppressing disturbance, including wideband components. The estimation accuracy of the noise control filter in the conventional system is degraded because the disturbance corrupts the input signal to the noise control filter. A solution of this problem is to utilize a linear prediction filter. The linear prediction filter is utilized for the modified-error feedback ANC system to suppress the wideband disturbance because the linear prediction filter can separate narrowband and wideband noise. Suppressing wideband noise is important for the head-mounted ANC system we have already proposed for reducing the noise from a magnetic resonance imaging (MRI) device because the error microphones are located near the user's ears and the user's voice consequently corrupts the input signal to the noise control filter. Some simulation and experimental results obtained using a digital signal processor (DSP) demonstrate that the proposed feedback ANC system is superior to a conventional feedback ANC system in terms of the estimation accuracy and the rate of convergence of the noise control filter.

In this paper, we present and analyze a predictive closed-loop power control (CLPC) scheme which employs a comb-type sample arrangement to effectively compensate multiple power control group (PCG) delays over mobile fading channels. We consider both least squares and recursive least squares filters in our CLPC scheme. The effects of channel estimation error, prediction filter error, and power control bit transmission error on the performance of the proposed CLPC method along with competing non-predictive and predictive CLPC schemes are thoroughly investigated. Our results clearly indicate the superiority of the proposed scheme with its improved robustness under non-ideal conditions. Furthermore, we carry out a Monte-Carlo simulation study of a 55 square grid cellular network and evaluate the user capacity. Capacity improvements up to 90% are observed for a typical cellular network scenario.