OPTIMIZED POWER ALLOCATION USING GAUSSIAN MIXTURE MODEL PRIOR KNOWLEDGE FOR WIRELESS SENSOR NETWORKS
Dr P SATISH REDDY · Journal of Engineering Sciences · 2023
This paper addresses power allocation in nonlinear sensor networks with a Gaussian Mixture (GM) information source. Sensor observations are transmitted through independent Rayleigh flat fading channels to a fusion center (FC). The transmit power across sensor nodes is optimally allocated to minimize the mean square error (MSE) of the estimate at the FC. Both Bayesian linear and optimal nonlinear estimators are employed at the FC to evaluate the performance of the proposed optimal power allocation scheme, comparing it to a uniform power allocation approach. Extensive simulation results demonstrate that the proposed Bayesian linear estimator, with optimized power gains, significantly improves estimation accuracy for the GM prior distribution