Robust Decentralized and Distributed Estimation of a Correlated Parameter Vector in MIMO-OFDM Wireless Sensor Networks

Rajput, Kunwar Pritiraj; Ahmed, Mohammad Faisal; Venkategowda, Naveen K. D.; Jagannatham, Aditya K.; Sharma, Govind; Hanzo, Lajos

doi:10.1109/tcomm.2021.3092409

Cited by 11 publications

(11 citation statements)

References 32 publications

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“…In a typical linear decentralized estimation setup, a large number of IoTNos are deployed in an area of interest which one wants to monitor. The IoTNos transmit their precoded observations over a coherent multiple access channel (MAC) to the FC for receiver combining (RC) and final estimation [16]. Coherent MAC requires that all the IoTNos in the network are synchronized and their individual transmitted signals arrive as a coherent sum at the FC.…”

Section: A Review Of Existing Literaturementioning

confidence: 99%

Robust Linear Hybrid Beamforming Designs Relying on Imperfect CSI in mmWave MIMO IoT Networks

Rajput

Maity

Srivastava

et al. 2023

IEEE Internet Things J.

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Linear hybrid beamformer designs are conceived for the decentralized estimation of a vector parameter in a millimeter wave (mmWave) multiple-input multiple-output (MIMO) Internet of Things network (IoTNe). The proposed designs incorporate both total IoTNe and individual IoTNo power constraints, while also eliminating the need for a baseband receiver combiner at the fusion center (FC). To circumvent the non-convexity of the hybrid beamformer design problem, the proposed approach initially determines the minimum mean square error (MMSE) digital transmit precoder (TPC) weights followed by a simultaneous orthogonal matching pursuit (SOMP)-based framework for obtaining the analog RF and digital baseband TPCs. Robust hybrid beamformers are also derived for the realistic imperfect channel state information (CSI) scenario, utilizing both the stochastic and norm-ball CSI uncertainty frameworks. The centralized MMSE bound derived in this work serves as a lower bound for the estimation performance of the proposed hybrid TPC designs. Finally, our simulation results quantify the benefits of the various designs developed.

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Section: A Review Of Existing Literaturementioning

confidence: 99%

Robust Linear Hybrid Beamforming Designs Relying on Imperfect CSI in mmWave MIMO IoT Networks

Rajput

Maity

Srivastava

et al. 2023

IEEE Internet Things J.

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“…The authors of [47] and [48] have proposed innovative robust LDE schemes based on majorization theory for non-sparse and sparse parameter estimation, respectively, where closed-form expressions for robust TPCs/ RC matrices were derived. Furthermore, the robust LDE of a temporally correlated parameter vector was considered in [49] under imperfect CSI considering both analog as well as quantized SN measurement transmission.…”

Section: A Existing Literaturementioning

confidence: 99%

“…Hence, in a practical system, it is imperative to take the CSI uncertainty into account for achieving robust parameter tracking. To this end, similar to [49], [53], let the channel between each SN and the FC be modeled as…”

Section: Robust Transceiver Designmentioning

confidence: 99%

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Robust Linear Decentralized Tracking of a Time-Varying Sparse Parameter Relying on Imperfect CSI

Rajput

Srivastava

Jagannatham

et al. 2023

IEEE Internet Things J.

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Robust linear decentralized tracking of a time varying sparse parameter is studied in a multiple-input multipleoutput (MIMO) wireless sensor network (WSN) under channel state information (CSI) uncertainty. Initially, assuming perfect CSI availability, a novel sparse Bayesian learning-based Kalman filtering (SBL-KF) framework is developed in order to track the time varying sparse parameter. Subsequently, an optimization problem is formulated to minimize the mean square error (MSE) in each time slot, followed by the design of a fast block coordinate descent (FBCD)-based iterative algorithm. A unique aspect of the proposed technique is that it requires only a single iteration per time slot to obtain the transmit precoder (TPC) matrices for all the sensor nodes (SNs) and the receiver combiner (RC) matrix for the fusion center (FC) in an online fashion. The recursive Bayesian Cramer Rao bound (BCRB) is also derived for benchmarking the performance of the proposed linear decentralized estimation (LDE) scheme. Furthermore, for considering a practical scenario having CSI uncertainty, a robust SBL-KF (RSBL-KF) is derived for tracking the unknown parameter vector of interest followed by the conception of a robust transceiver design. Our simulation results show that the schemes designed outperform both the traditional sparsity-agnostic KF and the state-of-the-art sparse reconstruction methods. Furthermore, as compared to the uncertainty-agnostic design, the robust transceiver architecture conceived is shown to provide improved estimation performance, making it eminently suitable for practical applications.

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“…By alternatively minimizing the sequential MMSE, they designed near-optimal and linear compression strategies under power constraints. Following from [ 16 ], the authors continued to develop optimal pre-coders minimizing the sum-MSE for the scenario of transmitting quantized observations in [ 18 ].…”

Section: Related Workmentioning

confidence: 99%

Joint Optimization of Data Freshness and Fidelity for Selection Combining-Based Transmissions

Chen

Wang

et al. 2022

Entropy

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Motivated by big data applications in the Internet of Things (IoT), abundant information arrives at the fusion center (FC) waiting to be processed. It is of great significance to ensure data freshness and fidelity simultaneously. We consider a wireless sensor network (WSN) where several sensor nodes observe one metric and then transmit the observations to the FC using a selection combining (SC) scheme. We adopt the age of information (AoI) and minimum mean square error (MMSE) metrics to measure the data freshness and fidelity, respectively. Explicit expressions of average AoI and MMSE are derived. After that, we jointly optimize the two metrics by adjusting the number of sensor nodes. A closed-form sub-optimal number of sensor nodes is proposed to achieve the best freshness and fidelity tradeoff with negligible errors. Numerical results show that using the proposed node number designs can effectively improve the freshness and fidelity of the transmitted data.

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Robust Decentralized and Distributed Estimation of a Correlated Parameter Vector in MIMO-OFDM Wireless Sensor Networks

Cited by 11 publications

References 32 publications

Robust Linear Hybrid Beamforming Designs Relying on Imperfect CSI in mmWave MIMO IoT Networks

Robust Linear Hybrid Beamforming Designs Relying on Imperfect CSI in mmWave MIMO IoT Networks

Robust Linear Decentralized Tracking of a Time-Varying Sparse Parameter Relying on Imperfect CSI

Joint Optimization of Data Freshness and Fidelity for Selection Combining-Based Transmissions

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