Estimation of MIMO Channel Using Suboptimal Particle Filtering

Hoang, Hai H.; Kwan, Bing W.

doi:10.1109/icccn.2009.5235323

Cited by 1 publication

(1 citation statement)

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“…This paper proposes a suboptimal particle filter that overcomes the problem of uncertain noise characteristics without disregarding the current observation. Some primary discussions and simulation results of the suboptimal particle filter have been presented in . The basic idea behind the suboptimal particle filter is to propose a good likelihood distribution, and then propagate the particles to the high probability region.…”

Section: Introductionmentioning

confidence: 99%

Suboptimal particle filtering for MIMO flat fading channel estimation

Hoang

Kwan

2011

Int J Communication

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SUMMARY Particle filters have been successfully employed to track MIMO flat fading channels for wireless communications. However, an optimal importance density cannot be always found to optimize the performance of a particle filter. A suboptimal importance density such as the prior distribution can be used to reduce the complexity of the particle filtering; however, it has a problem of ignoring the current observations. A class of suboptimal particle filters uses the prior distribution as the important density and moves the predicted particles to the low‐error region. In addition, particle filters require knowledge of noise processes to estimate the posterior distribution. This paper presents a suboptimal particle filter that overcomes the drawbacks of the prior importance density and the noise uncertainty by utilizing the swarm behavior in the particle propagation. The presented method will be applied to estimate the channel state information and detect the transmitted symbols of a MIMO wireless communication system under Rayleigh flat fading channel. Computer simulation of a 2 ×2 MIMO system is presented to illustrate the performance of the proposed suboptimal particle filter technique. Copyright © 2011 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%