New Combination Rule over Wireless Sensor Networks

Distributed blind equalization over networks based on single-input multiple-output (SIMO) channels model relies on cooperation rule among neighboring sensor nodes. Different channels make the distributed blind equalization more difficult in the wireless sensor network (WSN). However, the performance is affected by the degree of severity of the noisy channel output. In doing so, 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 degree of the nodes. The eigenvalue spread based combination rule is compared with conventional combination rules by utilizing one of the distributed blind equalization algorithms. Symbol error rate (SER) is investigated on several communication channels. Simulation result shows that the resulting combination rule attains an improved performance over networks.