Joint Transceiver Design for Linear MMSE Data Fusion in Coherent MAC Wireless Sensor Networks

Abstract

This paper considers the design of minimal mean square error (MMSE) transceivers in a wireless sensor network. The problem is nonconvex and challenging, and previous results (with partial solutions and/or with convergence unproved) left much to be desired. Here we propose several approaches-2 block coordinate descent (BCD), essentially cyclic multi-block method and its variants and distributive method to solve this problem. The proposed 2-BCD approach formulates the subproblem of joint beamformer optimization as a general second-order cone programming problem, which lends itself to standard numerical solvers and which requires no extra assumptions like previous works do. The proposed essentially cyclic multi-block approach further decomposes the joint beamformer design subproblem into multiple blocks, and rigorously solves each with semi-closed-form solution. The distributive algorithm optimizes transmitters in a decentralized manner and has never been considered in existing literature. The distributive algorithm has time complexity independent of number of sensors and is especially suitable for large-scale networks. All the previous BCD-based approaches left some singularity issue unattended as well as the convergence property unaddressed, our proposals are the first to provide a complete and provably-converging analytical solution. Extensive analysis and simulations demonstrate the merits of the novel approaches relative to existing alternatives.

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

“…By extending this framework, Behbahani et al [18] proposed a ground-breaking iterative procedure for a joint transceiver design relying on mean square error (MSE) minimization for vector parameter estimation at the FC. Along similar lines, Liu et al [19] developed decentralized and distributed schemes for the efficient estimation of a parameter vector. However, the algorithms developed in [18] and [19] are based on the iterative block coordinate descent (BCD) framework, which results in a high computational complexity, especially in a MIMO IoTNe associated with a large number of IoTNos.…”

Linear MMSE Precoder Combiner Designs for Decentralized Estimation in Wireless Sensor Networks

“…This restricts the applicability of the framework proposed therein. To overcome this shortcoming, iterative algorithms were developed using the block coordinate descent framework in [3] and [4], by considering a general non-diagonal MIMO channel matrix between each sensor and the fusion center. However, due to their iterative nature, the algorithms described therein require significant computational resources and time.…”

“…This paper considers a MIMO WSN with multiple antennas at the sensor nodes as well as at the fusion center for the transmission and estimation of a vector parameter. The proposed linear MMSE processing techniques are based on majorization theory [12], which leads to closed form solutions for the design of precoders and combiners, unlike the iterative schemes in the existing works [3], [4].…”

“…Fig.1:(a) MSE versus SNR FC for ideal sensors (b) MSE performance of the proposed scheme with the existing schemes in[3] and[4] versus SNR FC for ideal sensors.…”

Joint Collaboration and Compression Design for Distributed Sequential Estimation in a Wireless Sensor Network

“…Besides sensor collaboration, a number of works also focus on distributed estimation algorithms for reducing the communication cost for resource constrained WSNs via linear compression [12], [28]- [30], [33], [34]. In [12], the problem of distributed estimation of an unknown vector signal in resource constrained WSNs via coherent multiple access channels (MAC) is studied.…”