Practical Channel Splicing using OFDM Waveforms for Joint Communication and Sensing in the IoT

TL;DR

This paper demonstrates a practical OFDM-based channel splicing method for joint communication and sensing in IoT, enabling accurate wideband channel measurements with limited hardware by combining narrow-band data.

Contribution

It introduces a novel integration of channel splicing into an OFDM system for IoT, validating its effectiveness through simulation and real-world experiments.

Findings

Using only 50% of the spectrum yields accurate CIR measurements.

The method is validated both in simulation and indoor experiments.

Channel splicing enables wideband channel estimation with narrow-band hardware.

Abstract

Channel splicing is a rather new and very promising concept. It allows to realize a wideband channel sounder by combining multiple narrow-band measurements. Among others, channel splicing is a sparse sensing techniques suggested for use in joint communication and sensing (JCAS), channel measurements and prediction using cheap hardware that cannot measure wideband channels directly such as in the internet of things (IoT). This work validates the practicality of a channel splicing technique by integrating it into an OFDM-based IEEE 802.11ac system, which we consider representative for many IoT solutions. Our system allows computing both the channel impulse response (CIR) and the channel frequency response (CFR). In this paper, we concentrate on the impact of the number of sub-bands in our study and show that even using only 50% of the overall spectrum leads to very accurate CIR measures. We validate the system in simulation and confirm the results in an experimental in-door scenario using software defined radios.