# Distributed MIMO Measurements for Integrated Communication and Sensing in an Industrial Environment
## 7 Conclusions  
A new, truly distributed MIMO channel sounding system was developed. The channel sounder was then used to perform measurements in an industry environment. The results show that distributing the antennas will achieve significant channel hardening and avoid deep fading dips due to small-scale and large-scale fading. We also investigated the stationarity regions, in which the WSSUS assumptions held. This showed that the regions are quite small, with stationarity regions in the order of 2 m. We further showed that the RMS delay spread is in line with previous measurements conducted in similar settings and is around 50 ns; however, it varies between the distributed infrastructure antennas. Also, the Doppler spectral density was investigated by applying two super-resolution algorithms. We showed that in our data, both algorithms can find the dominant Doppler component. Finally, we have highlighted the potential of positioning with D-MIMO in these environments. Despite NLoS conditions, multipath propagation, and rich scattering in an industrial scenario, even a simple Bartlett beamformer can produce promising positioning results with an MSE below 20 cm when paired with a suitable state-space filter. In the future, we will demonstrate a more elaborate estimator that outperforms our current solution. The initial results hint at possible centimeter-level positioning accuracy. There are several directions for future work from here. Further investigations of channel characteristics are ongoing, where all the link combinations over the measured scenarios are classified as NLoS or LoS and where all available data can contribute to the statistics of the channel. In addition, work investigating the performance of positioning capabilities in the more challenging _loop_ scenario is currently being carried out in parallel to further improvements of the positioning presented in this paper. Another path is to investigate bi- or multistatic radar when the user is device free. Conceptualization, C.N. and F.T. ; methodology, all; software, C.N., A.F. and B.D. ; validation, C.N., A.F. and B.D. ; formal analysis, all; investigation, C.N., X.L., A.F. and F.T. ; data curation, C.N. ; writing--original draft preparation, all; writing--review and editing, all; visualization, C.N. and B.D. ; supervision, A.F. and F.T. ; project administration, F.T. ; funding acquisition, F.T. All authors have read and agreed to the published version of the manuscript. This research was partially funded by Connected Systems at Lund University, the strategic research area ELIIT, and the REINDEER project of the European Union's Horizon 2020 research and innovation program under grant agreement no. 101013425. Not applicable. Figure 20: Initial trajectory estimation result based on the Bartlett estimator using Equation (22) with a particle filter. The particle filter is initialized at a position \(\mathbf{p}=[9.5,6.5]^{\mathrm{T}}\), close to its first estimate \(\hat{\mathbf{p}}_{1}^{(13)}\). Data Availability Statement: Data available on request due to legal restrictions. We would like to thank the Sustainable Production Initiative at Lund University for making their workshop available for the measurements. We also want to thank Associate [PERSON] and [PERSON] for their invaluable help during the measurement campaign as well as [PERSON] for her discussions, feedback, and proofreading of the material. We also extend our appreciation to Xuesong Cai and the Kungl. Fysiografiska Sallskapet i Lund (Royal Physiographic Society of Lund) for providing funding for the lidar and IMU system. The authors declare no conflicts of interest.