Contact-less Material Probing with Distributed Sensors: Joint Sensing and Communication Optimization

TL;DR

This paper explores optimizing joint sensing and communication in a distributed RF-based material probing system, focusing on power allocation and sensor amplification to improve material property detection.

Contribution

It introduces a joint optimization framework for power allocation and sensor amplification in a distributed sensing setup with multiple antennas and fusion center processing.

Findings

Optimal power allocation reduces total energy consumption.

Zero-forcing and MMSE improve signal processing at the fusion center.

Asymptotic analysis provides insights for large sensor deployments.

Abstract

The utilization of RF signals to probe material properties of objects is of huge interest both in academia as well as industry. To this end, a setup is investigated, in which a transmitter equipped with a two-dimensional multi-antenna array dispatches a signal, which hits objects in the environment and the reflections from the objects are captured by distributed sensors. The received signal at those sensors are then amplified and forwarded to a multiple antenna fusion center, which performs space-time post-processing in order to optimize the information extraction. In this process, optimal design of power allocation per object alongside sensors amplifications is of crucial importance. Here, the power allocation and sensors amplifications is jointly optimized, given maximum-ratio combining (MRC) at the fusion center. We formulate this challenge as a sum-power minimization under per-object SINR constraints, a sum-power constraint at the transmitter and individual power constraints at the sensors. Moreover, the advantage of deploying zero-forcing (ZF) and minimum mean-squared error (MMSE) at the fusion center is discussed. Asymptotic analysis is also provided for the case that large number of sensors are deployed in the sensing environment.