Abstract
Integrating efficient connectivity, positioning and sensing functionalities into 5G New Radio (NR) and beyond mobile cellular systems is one timely research paradigm, especially at mm-wave and sub-THz bands. In this article, we address the radio-based sensing and environment mapping prospects with specific emphasis on the user equipment (UE) side. We first describe an efficient <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula>-regularized least-squares (LS) approach to obtain sparse range–angle charts at individual measurement or sensing locations. For the subsequent environment mapping, we then introduce a novel state model for mapping diffuse and specular scattering, which allows efficient tracking of individual scatterers over time using interacting multiple model (IMM) extended Kalman filter and smoother. Also the related measurement selection and data association problems are addressed. 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. The results illustrate the superiority of the dynamic tracking-based solutions, compared to static reference methods, while overall demonstrate the excellent prospects of radio-based mobile environment sensing and mapping in future mm-wave networks.
Highlights
F IFTH generation (5G) New Radio (NR) mobile cellular systems provide large improvements in terms of, e.g, peak data rates, network capacity, number of connected devices, and radio access latency, compared to earlier Long-Term Evolution (LTE) based networks [1]
To harness the sparsity of the mm-wave propagation channels, and to obtain sparse range–angle charts from (12), we propose to consider the following l1-regularized LS problem: min b y − Φb where b ≜ vec (B), y = [y0T, . . . , yM T −1]T, ym ≜ vec (Ym), Φ is a dictionary matrix defined in Appendix A, and λ denotes the regularization parameter
To solve the sparse map recovery problem in (14), we resort to the iterative shrinkage/thresholding algorithm (ISTA) [49]–[51], as outlined in Algorithm 1, where λmax(·) denotes the maximum eigenvalue of a matrix and (·)+ yields the positive part of a real number while sgn(z) = z/|z| yields the sign of a complex number
Summary
F IFTH generation (5G) New Radio (NR) mobile cellular systems provide large improvements in terms of, e.g, peak data rates, network capacity, number of connected devices, and radio access latency, compared to earlier Long-Term Evolution (LTE) based networks [1]. Techniques consider user-centric approaches where localization and mapping functionalities are implemented at each agent independently, without the need for fixed infrastructure [37] In this case, each moving agent is equipped with transmit and receive antennas, similar to a monostatic radar system, that measures the MPCs and performs SLAM . We describe a novel application framework for user-centric mm-wave indoor mapping systems, propose associated signal processing methods in 5G NR UE context, assuming a known agent trajectory, and perform validation with ray-tracing and RF measurements. In the considered approach, illustrated conceptually, the UE senses the surrounding environment through orthogonal frequency-division multiplexing (OFDM)-based beamformed uplink transmissions while observing and collecting the target reflections with synchronous receiver beamforming patterns This is followed by range–angle processing, while the corresponding range– angle charts are further post-processed in the subsequent dynamic mapping stage. Further details of the LS-based range– angle processing solution are described in the Appendices A and B
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