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This paper considers the optimal power scheduling for the distributed estimation of a source parameter using quantized samples of noisy sensor observations in a wireless sensor network (WSN). Repetition codes are used to transmit quantization bits of sensor observations to achieve unequal error protection, and a quasi-best linear unbiased estimate is constructed to estimate the source parameter at...
This paper presents a new progressive distributed estimation scheme (DES) along with the power scheduling among sensors under AWGN channels. The progressive DES consists of a transmission bit allocation scheme and a quasi best linear unbiased estimate (BLUE) of the unknown parameter at each sensor. This scheme is shown to outperform the traditional progressive DES. Moreover, the power scheduling among...
This paper addresses the optimization of quantization at local sensors under strict energy constraint and imperfect transmission to improve the reconstruction performance at the fusion center in the wireless sensor networks (WSNs). We present optimized quantization scheme including the optimal quantization bit rate and the optimal transmission power allocation among quantization bits for BPSK signal...
This paper proposes a distributed incremental quantization and estimation scheme by which each sensor can make a maximum likelihood estimation (MLE) of the unknown parameter based on its local observations and the quantized messages transmitted by the previous sensor. We derive the upper bound of the estimation mean squared error (MSE) of the proposed scheme, and propose the bit allocation algorithms,...
This letter analyzes the performance of several typical one-bit universal distributed estimation schemes (DESs) in bandwidth-constrained sensor networks. We theoretically obtain the mean and mean-square error (MSE) of the DES based on binary form (BF) of the observation along with the MSE upper bound. It is proved that in some cases, the BF-based DES has larger MSE than the DES based on the complementary...
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