2019 Volume 23 Issue 6 Pages 243-256
In this paper, we discuss distributed blind equalization based on a single-input multipleoutput (SIMO) channel model. To deal with a realistic situation, it is assumed that different channels make distributed blind equalization more difficult in a wireless sensor network (WSN). The performance is affected by the degree of severity of the noisy channel output. The eigenvalue spread of the input correlation matrix is utilized to propose a new combination rule whose coefficients are estimated from the neighboring channel outputs instead of only the degrees of the nodes. The eigenvalue-spread-based combination rule is implemented by two approaches, and compared with conventional combination rules by utilizing one of the distributed blind equalization algorithms. Mean square error (MSE) and symbol error rate (SER) are investigated for several communication channels. Computer simulations validate the superiority of the proposed combination rule to the conventional rules.
