1 research outputs found
Low-Complexity Channel Estimation with Set-Membership Algorithms for Cooperative Wireless Sensor Networks
In this paper, we consider a general cooperative wireless sensor network
(WSN) with multiple hops and the problem of channel estimation. Two
matrix-based set-membership algorithms are developed for the estimation of the
complex matrix channel parameters. The main goal is to reduce the computational
complexity significantly as compared with existing channel estimators and
extend the lifetime of the WSN by reducing its power consumption. The first
proposed algorithm is the set-membership normalized least mean squares
(SM-NLMS) algorithm. The second is the set-membership recursive least squares
(RLS) algorithm called BEACON. Then, we present and incorporate an error bound
function into the two channel estimation methods which can adjust the error
bound automatically with the update of the channel estimates. Steady-state
analysis in the output mean-squared error (MSE) are presented and closed-form
formulae for the excess MSE and the probability of update in each recursion are
provided. Computer simulations show good performance of our proposed algorithms
in terms of convergence speed, steady state mean square error and bit error
rate (BER) and demonstrate reduced complexity and robustness against the
time-varying environments and different signal-to-noise ratio (SNR) values.Comment: 15 Figure