Low-Complexity Channel Estimation with Set-Membership Algorithms for Cooperative Wireless Sensor Networks

TL;DR

This paper introduces low-complexity, set-membership algorithms for channel estimation in cooperative wireless sensor networks, significantly reducing computational load and power consumption while maintaining robust performance in dynamic environments.

Contribution

The paper develops two novel set-membership matrix-based algorithms, SM-NLMS and BEACON, with adaptive error bounds for efficient channel estimation in WSNs, improving speed and robustness.

Findings

Algorithms achieve faster convergence and lower steady-state MSE.

Reduced computational complexity compared to existing methods.

Robust performance across varying SNRs and time-varying channels.

Abstract

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.