Eigenvalue-Spread-Based Combination Rule for Distributed Blind Equalization in Networked System

Abstract

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.

Signal-Power-Based Combination Weight Rule for Distributed Blind Equalization in WSN Model

“…2. As described in [13] and [15], the input for the distributed blind equalizer at the q-th sensor node, y q (n), is given as…”

“…The performance of the distributed equalization was analyzed with various channels and noise variances, but it was difficult to achieve excellent performance with high noise variance. In [15], one combination weight rule was proposed for the WSN to improve the performance of the distributed equalization. Only channel conditions were considered while noise conditions were not considered in [15].…”

“…In [15], one combination weight rule was proposed for the WSN to improve the performance of the distributed equalization. Only channel conditions were considered while noise conditions were not considered in [15]. The performance of the DEAs is also affected by noise.…”

Distributed Blind Equalization Based on the Best Local Network in Networked System

“…For this purpose, we calculate the total average deviation (20). In (21), we consider finding the location of the minimum total average deviation of the local sensor network among all local sensor networks at the WSN, which is where the best sensor is located.…”

“…However, good performance was obtained only under well-channel and common noise conditions. In [21], a combination weight rule using the condition number was proposed to mitigate the effect between the interconnected sensor nodes for improving the performance of the estimation algorithms.…”