Millimeter-Wave Mobile Sensing and Environment Mapping Models, Algorithms and Validation


One relevant research paradigm, particularly at mm-wave and sub-THz bands, is to integrate efficient connectivity, positioning, and sensing functionalities into 5G New Radio (NR) and beyond mobile cellular systems. This is especially important at higher frequencies. The possibilities of radio-based sensing and environment mapping are discussed in this article, with a particular emphasis placed on the user equipment (UE) side of things. In the first part of this paper, we provide a detailed explanation of an effective method known as l1-regularized least-squares (LS) for obtaining sparse range–angle charts at individual measurement or sensing locations. We then present a novel state model for mapping diffuse and specular scattering in order to prepare for the subsequent environment mapping. This model enables the effective tracking of individual scatterers over time through the utilization of an interacting multiple model (IMM) extended Kalman filter and smoother. The issues with measurement selection and data association, which are closely related, are also tackled. We provide extensive numerical indoor mapping results at the 28 GHz band deploying OFDM-based 5G NR uplink waveform with 400 MHz channel bandwidth, covering both accurate ray-tracing based as well as actual RF measurement results. These results are based on the 5G New Radio's uplink waveform. The findings not only demonstrate the superiority of radio-based mobile environment sensing and mapping in future mm-wave networks, but also demonstrate the superiority of radio-based mobile environment sensing and mapping in comparison to static reference methods.

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