High-quality channel tracking is central for coherent detection in wireless communication systems. Typical tracking algorithms assume the number of multipath components and delays to be known and constant, while their amplitudes may vary in time. In this work we focus on the more realistic assumption that also the number of channel taps is unknown and time-varying. The more complex estimation problem arising from this assumption is solved using Random-Set Theory, which allows one to regard the multipath- channel response as a single set-valued variable. The set- dynamic model and the observation can be described through a dynamic system. Under the hypothesis that the system is Conditionally Linear and Gaussian (CLG), the problem of jointly tracking the number of multipath components and their amplitude can be efficiently solved by the Rao-Blackwellized Particle-Filter (RBPF), wherein some of the variables can be analytically calculated with a decrease of the estimation error and complexity.

Multipath Channel Tracking using Random Sets with application to OFDM Systems

LOPS, Marco
2007

Abstract

High-quality channel tracking is central for coherent detection in wireless communication systems. Typical tracking algorithms assume the number of multipath components and delays to be known and constant, while their amplitudes may vary in time. In this work we focus on the more realistic assumption that also the number of channel taps is unknown and time-varying. The more complex estimation problem arising from this assumption is solved using Random-Set Theory, which allows one to regard the multipath- channel response as a single set-valued variable. The set- dynamic model and the observation can be described through a dynamic system. Under the hypothesis that the system is Conditionally Linear and Gaussian (CLG), the problem of jointly tracking the number of multipath components and their amplitude can be efficiently solved by the Rao-Blackwellized Particle-Filter (RBPF), wherein some of the variables can be analytically calculated with a decrease of the estimation error and complexity.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11580/20934
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