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The paper addresses the problem of reconstructing a signal at some high sampling rate from a set of signals sampled at a lower rate and subject to additive noise and distortion. A set of periodically time-varying filters are employed in reconstructing the underlying signal. Results are presented for a one-dimensional case involving simulated data, as well as for a two-dimensional case involving real image data where the image is processed by rows. In both cases, considerable improvement is evident after the processing.

In this paper, the base station identification and timing adjust measurements are used to geolocate mobile subscribers in a WiMAX network. The uplink and downlink subframes of the physical layer and management messages of the medium access control layer are examined to extract the necessary data for geolocation. Using a hidden Markov model [1] based algorithm to estimate the track of the mobile subscriber, we demonstrate that the position error can be further reduced by incorporating timing adjust measurements. Simulation results of the proposed scheme are included to demonstrate the effectiveness of the combined use of base station ID and timing adjust measurements.

A novel scheme for mobile subscriber positioning is proposed based on the hidden-Markov model (HMM) and the cell-ID maximum-likelihood database correlation method also known as fingerprinting. Using a simulated channel environment, based on the Clearwire deployment of WiMAX base stations in San Jose, CA, we show that matching the right configuration of the model to the deployment environment can realize significant gains in performance. The proposed scheme balances the scalability inherent in hidden-Markov-based motion models deployed in large areas of interest against the existing channel conditions and computational capability. By utilizing a simulated channel this paper demonstrates the effect of base station deployment and shadowing on the fingerprint-based HMM motion model. Further, the benefits gained through scaling the HMM are explored.