Robust Decentralized and Distributed Estimation of a Correlated Parameter Vector in MIMO-OFDM Wireless Sensor Networks
Abstract : An optimal precoder design is conceived for the decentralized estimation of an unknown spatially as well as temporally correlated parameter vector in a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) based wireless sensor network (WSN). Furthermore, exploiting the temporal correlation present in the parameter vector, a rate-distortion theory based framework is developed for the optimal quantization of the sensor observations so that the resultant distortion is minimized for a given bit-budget. Subsequently, optimal precoders are also developed that minimize the sum-MSE (SMSE) for the scenario of transmitting quantized observations. In order to reduce the computational complexity of the decentralized framework, distributed precoder design algorithms are also developed which design precoders using the consensus based alternating direction method of multipliers (ADMM), wherein each SN determines its precoders without any central coordination by the fusion center. Finally, new robust MIMO precoder designs are proposed for practical scenarios operating in the face of channel state information (CSI) uncertainty. Our simulation results demonstrate the improved performance of the proposed schemes and corroborate our analytical formulations.
? The existing contributions have considered maximal ratio combining (MRC) at the FC in an amplify-and-forward based measurement transmission scheme for fully exploiting the benefits of a massive MIMO system, followed by the design of the optimal detectors, which can significantly reduce the complexity of parameter sensing.
? The proposed framework considers the effects of parameter correlation, which naturally exists due to the spatial proximity of the sensor nodes.
? Most existing contributions on massive MIMO consider single antenna devices and sensors to limit the device complexity.
? It has been widely exploited in the existing literature on massive MIMO systems that linear processing employing MRC achieves good performance in the large antenna regime, i.e., when M » K.
? Distributed parameter estimation problem for wireless sensor networks (WSNs) has a rich literature in signal processing community.
? A converse problem is considered in , where the authors minimize the MSE, subject to a total transmit power constraint.
? A related problem is studied in, in which the FC employs a spatial BLUE for field reconstruction and the MSE is compared with a posterior CRLB.
? However, the problem formulations in these works naturally cannot have a bandwidth constraint.
? we should discritize in a way that, the positive impact of rounding up a rate on Da would dominate the negative effect of decreasing the rates of some other sensors on Da.
• The proposed detectors along with their analysis for scenarios having imperfect CSI in a massive MIMO WSN.
• Closed-form analytical expressions are derived for characterizing the performance of the proposed detectors in terms of the resultant PD and PF A at the FC.
• Our goal is that of further enhancing the detection performance of the test proposed in via the optimal sharing of the total sensor power P.
• A compares the receiver operating characteristic (ROC) of the proposed detector into that of the detector proposed.
• It is observed that the proposed low-complexity scheme is only suitable for a massive MIMO system, which has a similar performance as the detector.
? There is a collection of elegant results on energy efficient distributed parameter estimation with analog transmission, also known as Amplify-and-Forward (AF).
? We show that the bounds are good approximations of the simulated MSE and the performance of the proposed schemes approaches the clairvoyant centralized estimation when total transmit power or bandwidth is very large.
? From practical perspectives, having a total transmit power constraint enhances energy efficiency in battery-powered WSNs.
? Putting a cap on total bandwidth can further improve energy efficiency, since data communication is a major contributor to the network energy consumption.
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