Low-Complexity Matrix-Based Conjugate Gradient Channel Estimation 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. A matrix-based conjugate gradient (CG) algorithm is first developed, which is then incorporated with the set-membership filtering framework to improve the performance of complex channel estimation for WSNs. Due to selective update, the proposed set-membership matrix-based CG (SM-MCG) algorithm can reduce the computational complexity significantly and extend the lifetime of WSN. In addition, by introducing an exponentially decaying bound and combining with SM-MCG algorithm, we design an updating criterion to accurately track the optimal maximum iteration number of each data window while reducing complexity further. Simulation results show good performance of our proposed algorithms in terms of convergence speed, steady state mean-square error, and indicate reduced complexity.