Adaptive Algorithms for
Wireless Channel Estimation<
Accurate channel state information
is important in communication systems. This
is especially challenging in a wireless
environment where the channel exhibits strong
frequency and time selectivity. There has
been considerable prior work on this problem.
We present two contributions to an unified
approach to channel estimation problems.
In the first contribution, we study generalized
LMS algorithms with different types of error
and data non-linearity. We show that we
can analyze the performance issues such
as steady state error, stability and convergence
rates by using a weighted energy relation
method. Our technique solves all known generalizations
of the LMS algorithm with error or data
non-linearity, as well as provides more
accurate estimates of the performance.
In the second contribution, we propose joint
channel identification and data detection
algorithms for OFDM transmission. Our algorithm
exploits multiple structures of both the
data and channel. Data structures include
pilots, cyclic prefix, channel code and
finite alphabet. Channel structures include
tap sparsity, delay spread and Doppler spread.
We show that a Kalman filter framework can
be used to provide a unified solution.