Limited-Feedback-Based Channel-Aware Power Allocation for Linear Distributed Estimation

TL;DR

This paper introduces a limited-feedback strategy for power allocation in distributed linear estimation within wireless sensor networks, reducing feedback requirements while maintaining near-optimal estimation performance.

Contribution

It proposes a novel codebook-based limited-feedback scheme for power allocation, avoiding the need for continuous channel state feedback in large-scale WSNs.

Findings

The proposed method achieves near-optimal estimation accuracy.

It significantly reduces feedback bandwidth requirements.

The approach is scalable for large sensor networks.

Abstract

This paper investigates the problem of distributed best linear unbiased estimation (BLUE) of a random parameter at the fusion center (FC) of a wireless sensor network (WSN). In particular, the application of limited-feedback strategies for the optimal power allocation in distributed estimation is studied. In order to find the BLUE estimator of the unknown parameter, the FC combines spatially distributed, linearly processed, noisy observations of local sensors received through orthogonal channels corrupted by fading and additive Gaussian noise. Most optimal power-allocation schemes proposed in the literature require the feedback of the exact instantaneous channel state information from the FC to local sensors. This paper proposes a limited-feedback strategy in which the FC designs an optimal codebook containing the optimal power-allocation vectors, in an iterative offline process, based on the generalized Lloyd algorithm with modified distortion functions. Upon observing a realization of the channel vector, the FC finds the closest codeword to its corresponding optimal power-allocation vector and broadcasts the index of the codeword. Each sensor will then transmit its analog observations using its optimal quantized amplification gain. This approach eliminates the requirement for infinite-rate digital feedback links and is scalable, especially in large WSNs.