  Effective wireless communications are increasingly important in maintaining
the successful closed-loop operation of mission-critical industrial
Internet-of-Things (IIoT) applications. To meet the ever-increasing demands on
better wireless communications for IIoT, we propose an orthogonal
time-frequency space (OTFS) waveform-based joint communication and radio
sensing (JCAS) scheme -- an energy-efficient solution for not only reliable
communications but also high-accuracy sensing. OTFS has been demonstrated to
have higher reliability and energy efficiency than the currently popular IIoT
communication waveforms. JCAS has also been highly recommended for IIoT, since
it saves cost, power and spectrum compared to having two separate radio
frequency systems. Performing JCAS based on OTFS, however, can be hindered by a
lack of effective OTFS sensing. This paper is dedicated to filling this
technology gap. We first design a series of echo pre-processing methods that
successfully remove the impact of communication data symbols in the
time-frequency domain, where major challenges, like inter-carrier and
inter-symbol interference and noise amplification, are addressed. Then, we
provide a comprehensive analysis of the signal-to-interference-plus-noise ratio
(SINR) for sensing and optimize a key parameter of the proposed method to
maximize the SINR. Extensive simulations show that the proposed sensing method
approaches the maximum likelihood estimator with respect to the estimation
accuracy of target parameters and manifests applicability to wide ranges of key
system parameters. Notably, the complexity of the proposed method is only
dominated by a two-dimensional Fourier transform.
