A Viterbi Detector, Based on Sinusoid Modelling of Fading
Mobile Radio Channels:
An Illustration of the Utility of Deterministic
Models of Time-Variations in Adaptive Systems.
STU Workshop on Digital Communication,
May 29-30, 1991.
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Parameters of time-varying systems are often estimated
by adaptive algorithms with sliding time-windows, which
discount old data.
We may then face a dilemma: the use of a short data window
(or, equivalently, a large adaptation gain) results in noisy estimates.
With a long data window (small gain), time varying parameters
are tracked with a considerable delay.
A satisfactory compromise can be hard to find, if it exists at all.
To improve the accuracy, a priori information about the
properties of the time-variations may be taken into account,
in the form of deterministic or stochastic models,
The algorithms proposed here was motivated by the
problem of tracking the channel coefficient in a
D-AMPS North American digital mobile radio system.
The channel can be described as a FIR filter with two
complex-valued time-varying taps.
These taps vary due to Rayleigh fading, with a speed determined
by the speed of the mobile.
We propose to parametrize the real and imaginary parts
of the two FIR coefficients by sinusoid functions with
The resulting algorithm consists of four separate
recursive prediction error algorithms.
The parameter estimation was tested on simulated data with
10dB SNR and with each data burst beginning with a known
training sequence. As compared to using recursive
identification with a sliding data window, the accuracy
was improved significantly, in particular in time intervals with
- Related publications:
Report from 1990,
which describes the algorithms and the simulations.
by L Lindbom, 1992, which develops the algorithm based on deterministic
Conference paper in IEEE
ICASSP'93 on using stochastic models of time-variations .
- A series of four papers outlining the later development of a
complete design methodology, based on stochastic models
of time-varying parameters:
of general constant-gain adaptation algorithms.
Part II: Analysis
of stability and performance, for slow and fast variations.
The Wiener LMS
adaptation algorithm, a special case with low complexity.
A Case Study on IS-136 1900MHz channels.
- PhD Thesis
by L Lindbom 1995, presenting the general design
Related research ;
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