Novel Double-Chirp Preamble Design for Multiuser Asynchronous Massive MIMO LoRa Networks

[2512.17835] Novel Double-Chirp Preamble Design for Multiuser Asynchronous Massive MIMO LoRa Networks Skip to main content Search Submit Donate Log in Electrical Engineering and Systems Science > Signal Processing arXiv:2512.17835 (eess) [Submitted on 19 Dec 2025 ( v1 ), last revised 27 Aug 2026 (this version, v2)] Title: Novel Double-Chirp Preamble Design for Multiuser Asynchronous Massive MIMO LoRa Networks Authors: The Khai Nguyen , Ebrahim Bedeer View PDF HTML (experimental) Abstract: This paper proposes a novel preamble design and detection method for multiuser asynchronous massive MIMO LoRa networks. Unlike existing works, which only consider the preamble detection for a single end device (ED), we propose to simultaneously detect the preambles of multiple EDs that asynchronously transmit their uplink (UL) packets to a multiple-antenna gateway (GW). First, we show that the preamble detection in multiuser LoRa networks with the conventional single-chirp preamble suffers from the so-called preamble resemblance effect. This means that the preamble of any single ED can resemble the preambles of all EDs in the network and make it impossible to determine to which ED a preamble belongs. To address this problem, a novel double-chirp preamble design and a preamble assignment method are proposed, which can mitigate the preamble resemblance effect by making the preamble of each ED unique and recognizable. Next, a maximum-likelihood (ML) based detection scheme for the proposed double-chirp preamble is derived. Finally, since the proposed algorithm requires the calculation of the discrete Fourier transform (DFT) every sampling period, we propose a low-complexity technique to calculate the DFT recursively to reduce the complexity of our proposed design. Simulation shows that the proposed preamble design and detection require just about 2 dB more power to increase the number of EDs from one to 15 in the Rayleigh fading channel while achieving the same preamble detection error performance. Comments: 14 pages, 12 figures Subjects: Signal Processing (eess.SP) Cite as: arXiv:2512.17835 [eess.SP] (or arXiv:2512.17835v2 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2512.17835 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Khai Nguyen [ view email ] [v1] Fri, 19 Dec 2025 17:43:45 UTC (322 KB) [v2] Thu, 27 Aug 2026 18:03:15 UTC (317 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: eess.SP < prev | next > new | recent | 2025-12 Change to browse by: eess References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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