{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T12:59:42Z","timestamp":1790686782388,"version":"4.1.0"},"reference-count":44,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T00:00:00Z","timestamp":1790467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Physical-layer jamming directed at the LoRa preamble may become detectable before substantial degradation of the acquisition structure is evident at the receiver. The practically relevant question is therefore not only whether an attack can eventually be detected, but how much jammer-power headroom a monitor provides before the selected preamble-degradation onset is reached. This paper introduces a detection-theoretic evaluation framework that expresses this pre-degradation warning capability in decibels. Three quantities are defined: the detection sensitivity floor, i.e., the smallest relative jammer level yielding a detection probability of 0.9 at a false-alarm probability of 10\u22122; the preamble-based link-degradation onset, i.e., the level at which the preamble symbol error rate increases by five percentage points relative to its jamming-free baseline; and the resulting early-warning margin separating the two thresholds. The resulting early-warning margin is a power-domain measure of jammer-power headroom and should not be interpreted as elapsed warning time. The framework is evaluated over 13 isolated impairment configurations, corresponding to 221 jammer-level operating points, and complemented by a three-step severity-graded composite-channel ladder that jointly combines noise, multipath, co-channel interference, and synchronization impairments, for more than 5\u00d7105 packet-level evaluations. Alongside a convolutional autoencoder and a one-class support vector machine using the same three-channel in-phase, quadrature, and magnitude preamble representation, we evaluate a training-light dechirp-domain peak-to-mean ratio statistic and a simple score-level fusion rule; under independent threshold calibration, the fusion rule preserves the separability of its constituents but, under fading, interference, and synchronization impairments, does not recover the fixed-operating-point sensitivity of the dechirp statistic. Under strong co-channel interference, the proposed scalar statistic reaches the target detection probability at \u22128.5 dB, whereas the autoencoder does not reach the target detection probability anywhere within the evaluated sweep, corresponding to a sensitivity advantage of more than 14 dB and showing that the observed loss of autoencoder sensitivity is detector-dependent rather than caused by an absence of discriminative information in the received preamble. No detector dominates across all isolated impairments: the dechirp statistic is particularly effective under co-channel interference and synchronization offsets, whereas the autoencoder remains advantageous under severe multipath fading. Some learned-detector margins become negative under moderate additive noise and under severe multipath fading, and the composite-channel ladder shows a progressive degradation of detection sensitivity as joint impairment severity increases. These results provide a detector-agnostic, physically interpretable yardstick for evaluating the pre-degradation sensitivity of LoRa physical-layer jamming monitors.<\/jats:p>","DOI":"10.3390\/s26196123","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T15:57:57Z","timestamp":1790611077000},"page":"6123","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Early-Warning Margins for Preamble-Directed Jamming in LoRa: A Dechirp-Domain Sensitivity Framework with Severity-Graded Composite-Channel Stress Testing"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8550-8990","authenticated-orcid":false,"given":"Carlos","family":"Herrera-Loera","sequence":"first","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Panamericana, \u00c1lvaro del Portillo 49, Zapopan 45010, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0272-3275","authenticated-orcid":false,"given":"Carolina","family":"Del-Valle-Soto","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Panamericana, \u00c1lvaro del Portillo 49, Zapopan 45010, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7545-7441","authenticated-orcid":false,"given":"Leonardo J. Valdivia","family":"Parga","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Panamericana, \u00c1lvaro del Portillo 49, Zapopan 45010, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8682-5983","authenticated-orcid":false,"given":"Carlos","family":"Mex-Perera","sequence":"additional","affiliation":[{"name":"College of Engineering Technology, Rochester Institute of Technology, Rochester, NY 14623, USA"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/MWC.2016.7721743","article-title":"Long-Range Communications in Unlicensed Bands: The Rising Stars in the IoT and Smart City Scenarios","volume":"23","author":"Centenaro","year":"2016","journal-title":"IEEE Wirel. Commun."},{"key":"ref_2","unstructured":"Bor, M., Vidler, J., and Roedig, U. (2016, January 15\u201317). LoRa for the Internet of Things. Proceedings of the International Conference on Embedded Wireless Systems and Networks (EWSN), Graz, Austria."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Aras, E., Small, N., Ramachandran, G.S., Delbruel, S., Joosen, W., and Hughes, D. (2017, January 7\u201310). Selective Jamming of LoRaWAN Using Commodity Hardware. Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems, Melbourne, Australia.","DOI":"10.4108\/eai.7-11-2017.2273515"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Perkovi\u0107, T., and Siri\u0161\u010devi\u0107, D. (2020, January 23\u201326). Low-Cost LoRaWAN Jammer. Proceedings of the 5th International Conference on Smart and Sustainable Technologies (SpliTech), Split, Croatia.","DOI":"10.23919\/SpliTech49282.2020.9243739"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kalokidou, V., Nair, M., and Beach, M.A. (2022, January 13\u201314). LoRaWAN Performance Evaluation and Resilience under Jamming Attacks. Proceedings of the Sensor Signal Processing for Defence Conference (SSPD), London, UK.","DOI":"10.1109\/SSPD54131.2022.9896225"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1292","DOI":"10.1109\/TWC.2019.2952584","article-title":"On the Error Rate of the LoRa Modulation with Interference","volume":"19","author":"Afisiadis","year":"2020","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6830","DOI":"10.1109\/JIOT.2021.3114439","article-title":"Theoretical Performance of LoRa System in Multipath and Interference Channels","volume":"9","author":"Demeslay","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1109\/JPROC.2021.3052449","article-title":"A Unifying Review of Deep and Shallow Anomaly Detection","volume":"109","author":"Ruff","year":"2021","journal-title":"Proc. IEEE"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chalapathy, R., and Chawla, S. (2019). Deep Learning for Anomaly Detection: A Survey. arXiv.","DOI":"10.1145\/3394486.3406704"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1145\/3583137","article-title":"Jamming of LoRa PHY and Countermeasure","volume":"19","author":"Hou","year":"2023","journal-title":"ACM Trans. Sens. Netw."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Haque, M.A., and Saifullah, A. (2024, January 13\u201316). Handling Jamming Attacks in a LoRa Network. Proceedings of the 2024 IEEE\/ACM Ninth International Conference on Internet-of-Things Design and Implementation (IoTDI), Hong Kong, China.","DOI":"10.1109\/IoTDI61053.2024.00017"},{"key":"ref_12","first-page":"70","article-title":"LoRaWAN Security: An Evolvable Survey on Vulnerabilities, Attacks and Their Systematic Mitigation","volume":"18","author":"Hessel","year":"2023","journal-title":"ACM Trans. Sens. Netw."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xu, W., Trappe, W., Zhang, Y., and Wood, T. (2005, January 25\u201327). The Feasibility of Launching and Detecting Jamming Attacks in Wireless Networks. Proceedings of the 6th ACM International Symposium on Mobile Ad Hoc Networking and Computing, Urbana-Champaign, IL, USA.","DOI":"10.1145\/1062689.1062697"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1504\/IJAHUC.2014.066419","article-title":"Jamming and Anti-Jamming Techniques in Wireless Networks: A Survey","volume":"17","author":"Grover","year":"2014","journal-title":"Int. J. Ad. Hoc Ubiquitous Comput."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ruotsalainen, H. (2022, January 23\u201326). Reactive Jamming Detection for LoRaWAN Based on Meta-Data Differencing. Proceedings of the 17th International Conference on Availability, Reliability and Security (ARES \u201922), Vienna, Austria.","DOI":"10.1145\/3538969.3543805"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Alhazbi, S., Sciancalepore, S., and Oligeri, G. (2023, January 8\u201311). BloodHound: Early Detection and Identification of Jamming at the PHY-Layer. Proceedings of the 2023 IEEE 20th Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA.","DOI":"10.1109\/CCNC51644.2023.10059878"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"14682","DOI":"10.1109\/JIOT.2023.3343611","article-title":"Jamming Detection in Low-BER Mobile Indoor Scenarios via Deep Learning","volume":"11","author":"Sciancalepore","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_18","unstructured":"Semtech Corporation (2015). LoRa Modulation Basics, Semtech Corporation. Application Note AN1200.22, Revision 2."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Robyns, P., Quax, P., Lamotte, W., and Thenaers, W. (2018, January 19\u201321). A Multi-Channel Software Decoder for the LoRa Modulation Scheme. Proceedings of the 3rd International Conference on Internet of Things, Big Data and Security (IoTBDS), Funchal, Portugal.","DOI":"10.5220\/0006668400410051"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Marquet, A., Montavont, N., and Papadopoulos, G.Z. (2019, January 10\u201312). Investigating Theoretical Performance and Demodulation Techniques for LoRa. Proceedings of the IEEE 20th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), Washington, DC, USA.","DOI":"10.1109\/WoWMoM.2019.8793014"},{"key":"ref_21","unstructured":"Tapparel, J., and Burg, A. (2024, January 16\u201320). Design and Implementation of LoRa Physical Layer in GNU Radio. Proceedings of the 14th GNU Radio Conference, Knoxville, TN, USA."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3756","DOI":"10.1109\/JIOT.2021.3101002","article-title":"A Low-Complexity LoRa Synchronization Algorithm Robust to Sampling Time Offsets","volume":"9","author":"Xhonneux","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tapparel, J., Balatsoukas-Stimming, A., and Burg, A. (2023, January 25\u201328). LoRa Preamble Detection Robust to Inter-Channel Interference. Proceedings of the IEEE 24th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Shanghai, China.","DOI":"10.1109\/SPAWC53906.2023.10304493"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tapparel, J., and Burg, A. (2025). LoRa Fine Synchronization with Two-Pass Time and Frequency Offset Estimation. arXiv.","DOI":"10.1109\/IEEECONF60004.2024.10942846"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ferreira Dias, C., Rodrigues de Lima, E., and Fraidenraich, G. (2019). Bit Error Rate Closed-Form Expressions for LoRa Systems under Nakagami and Rice Fading Channels. Sensors, 19.","DOI":"10.3390\/s19204412"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/LCOMM.2018.2849718","article-title":"Closed-Form Approximation of LoRa Modulation BER Performance","volume":"22","author":"Elshabrawy","year":"2018","journal-title":"IEEE Commun. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1109\/LCOMM.2018.2797057","article-title":"Impact of LoRa Imperfect Orthogonality: Analysis of Link-Level Performance","volume":"22","author":"Croce","year":"2018","journal-title":"IEEE Commun. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1109\/LWC.2016.2647247","article-title":"Low Power Wide Area Network Analysis: Can LoRa Scale?","volume":"6","author":"Georgiou","year":"2017","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Azevedo, J.A., and Mendon\u00e7a, F. (2024). A Critical Review of the Propagation Models Employed in LoRa Systems. Sensors, 24.","DOI":"10.3390\/s24123877"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1162\/089976601750264965","article-title":"Estimating the Support of a High-Dimensional Distribution","volume":"13","author":"Platt","year":"2001","journal-title":"Neural Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1109\/TCCN.2017.2758370","article-title":"An Introduction to Deep Learning for the Physical Layer","volume":"3","author":"Hoydis","year":"2017","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An Introduction to ROC Analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","article-title":"The Meaning and Use of the Area under a Receiver Operating Characteristic (ROC) Curve","volume":"143","author":"Hanley","year":"1982","journal-title":"Radiology"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"837","DOI":"10.2307\/2531595","article-title":"Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach","volume":"44","author":"DeLong","year":"1988","journal-title":"Biometrics"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Efron, B., and Tibshirani, R.J. (1993). An Introduction to the Bootstrap, Chapman & Hall.","DOI":"10.1007\/978-1-4899-4541-9"},{"key":"ref_37","unstructured":"Kay, S.M. (1998). Fundamentals of Statistical Signal Processing, Volume II: Detection Theory, Prentice Hall."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5105612","DOI":"10.1109\/TGRS.2023.3280322","article-title":"Few-Shot Radar Jamming Recognition Network via Time-Frequency Self-Attention and Global Knowledge Distillation","volume":"61","author":"Luo","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"17494","DOI":"10.1109\/TVT.2026.3678290","article-title":"Few-Shot Recognition and Classification Framework for Jamming Signal: A CGAN-Based Fusion CNN Approach","volume":"75","author":"Ding","year":"2026","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhao, Z., and Bu, Y. (2023). Radar Active Jamming Recognition under Open World Setting. Remote Sens., 15.","DOI":"10.3390\/rs15164107"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"12525","DOI":"10.1109\/TAES.2025.3573297","article-title":"A Sustainable Learning Method for Radar Jamming Recognition Based on Open Set","volume":"61","author":"Zhou","year":"2025","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"6508","DOI":"10.1109\/TCOMM.2026.3675484","article-title":"Open-Set Recognition of Communication Jamming Using Raw I\/Q Data With Domain Adaptation","volume":"74","author":"Li","year":"2026","journal-title":"IEEE Trans. Commun."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Goldsmith, A. (2005). Wireless Communications, Cambridge University Press.","DOI":"10.1017\/CBO9780511841224"},{"key":"ref_44","unstructured":"LoRa Alliance (2026, September 12). RP002-1.0.5 LoRaWAN Regional Parameters. LoRa Alliance. Available online: https:\/\/resources.lora-alliance.org\/technical-specifications\/rp002-1-0-5-lorawan-regional-parameters."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6123\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T12:18:29Z","timestamp":1790684309000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6123"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,27]]},"references-count":44,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["s26196123"],"URL":"https:\/\/doi.org\/10.3390\/s26196123","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,27]]}}}