Adaptive maximum likelihood algorithms for the blind tracking of time-varying multipath channels
✍ Scribed by Joël Grouffaud; Pascal Larzabal; Anne Ferréol; Henri Clergeot
- Publisher
- John Wiley and Sons
- Year
- 1998
- Tongue
- English
- Weight
- 311 KB
- Volume
- 12
- Category
- Article
- ISSN
- 0890-6327
No coin nor oath required. For personal study only.
✦ Synopsis
Transmissions through multipath channels suffer from Rayleigh fading and intersymbol interference. This can be overcome by sending a (known) training sequence and identifying the channel (active identification). However, in a non-stationary context, the channel model has to be updated by periodically sending the training sequence, thus reducing the transmission rate. We address herein the problem of blind identification, which does not require such a sequence and allows a higher transmission rate. We have first proposed a two-stage algorithm (see Reference 2) for the blind identification of multipath channel. We investigate here the maximum-likelihood approach for the blind estimation of channel parameters. In order to track non-stationary channels, we have derived an adaptive (Kalman) algorithm which directly estimates the entire set of characteristic parameters. An original adaptive estimation of the noise model has been proposed for this investigation. Furthermore, the proposed method can easily cope with a model including Doppler shift, which is not directly possible with more common methods. Monte-Carlo simulations confirm the expected results and demonstrate the performance.