Indoor 60 GHz Radio Channel Dataset Enabling Digital Twin Construction
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910196325941248 |
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| author | Scazzoli, Davide de Santis, Daniele Linsalata, Francesco Santucci, Fortunato Spagnolini, Umberto Magarini, Maurizio |
| author_facet | Scazzoli, Davide de Santis, Daniele Linsalata, Francesco Santucci, Fortunato Spagnolini, Umberto Magarini, Maurizio |
| contents | The ambitious performance targets of modern wireless networks, including 6G and Industrial IoT (IIoT) systems, necessitate advanced hardware platforms utilizing millimeter-wave (mmWave) technology. High-frequency signals provide the bandwidth and low latency required for these systems, but rely on beamforming to overcome path loss and exploit channel sparsity. This kind of architecture provides all the specifications needed to build a SLAM (Simultaneous Localization and Mapping) system. This paper presents a dataset based on a validated, high-performance testbed integrating a Xilinx Zynq UltraScale+ RFSoC with a Sivers 60 GHz beamforming front-end. We demonstrate a novel methodology using segment-scrambled, quasi-orthogonal chirp waveforms to perform rapid exhaustive beamspace sampling. The system is integrated with Pynq Linux for real-time control and high-speed waveform upload. We present a high-density spatial dataset consisting of 350 measurement points across a 1.95 m x 3.60 m indoor grid. We exploited the system's ability to scan 63 transmit directions and construct a complete 63x63 beamspace intensity map in 200us. This dataset serves as a benchmark for spatial channel modeling, Digital Twins and Integrated Sensing and Communication (ISAC) research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_05824 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Indoor 60 GHz Radio Channel Dataset Enabling Digital Twin Construction Scazzoli, Davide de Santis, Daniele Linsalata, Francesco Santucci, Fortunato Spagnolini, Umberto Magarini, Maurizio Signal Processing The ambitious performance targets of modern wireless networks, including 6G and Industrial IoT (IIoT) systems, necessitate advanced hardware platforms utilizing millimeter-wave (mmWave) technology. High-frequency signals provide the bandwidth and low latency required for these systems, but rely on beamforming to overcome path loss and exploit channel sparsity. This kind of architecture provides all the specifications needed to build a SLAM (Simultaneous Localization and Mapping) system. This paper presents a dataset based on a validated, high-performance testbed integrating a Xilinx Zynq UltraScale+ RFSoC with a Sivers 60 GHz beamforming front-end. We demonstrate a novel methodology using segment-scrambled, quasi-orthogonal chirp waveforms to perform rapid exhaustive beamspace sampling. The system is integrated with Pynq Linux for real-time control and high-speed waveform upload. We present a high-density spatial dataset consisting of 350 measurement points across a 1.95 m x 3.60 m indoor grid. We exploited the system's ability to scan 63 transmit directions and construct a complete 63x63 beamspace intensity map in 200us. This dataset serves as a benchmark for spatial channel modeling, Digital Twins and Integrated Sensing and Communication (ISAC) research. |
| title | Indoor 60 GHz Radio Channel Dataset Enabling Digital Twin Construction |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2605.05824 |