Long Range (LoRa) has become a key enabler technology for low power wide area networks. However, due to its ALOHA-based medium access scheme, LoRa has to cope with collisions that limit the capacity and network scalability. Collisions between randomly overlapped signals modulated with different spreading factors (SFs) result in inter-SF interference, which increases the packet loss likelihood when signal-to-interference ratio (SIR) is low. This issue cannot be resolved by channel coding since the probability of error distance is not concentrated around the adjacent symbol. In this paper, we analytically model this interference, and propose an interference cancellation method based on the idea of segmentation of the received signal. This scheme has three steps. First, the SF of the interference signal is identified, then the equivalent data symbol and complex amplitude of the interference are estimated. Finally, the estimated interference signal is subtracted from the received signal before demodulation. Unlike conventional serial interference cancellation (SIC), this scheme can directly estimate and reconstruct the non-aligned inter-SF interference without synchronization. Simulation results show that the proposed method can significantly reduce the symbol error rate (SER) under low SIR compared with the conventional demodulation. Moreover, it also shows high robustness to fractional sample timing offset (STO) and carrier frequency offset (CFO) of interference. The presented results clearly show the effectiveness of the proposed method in terms of the SER performance.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:89419 |
| Date | 22 April 2024 |
| Creators | Zhang, Qiaohan, Bizon, Ivo, Kumar, Atul, Martinez, Ana Belen, Chafii, Marwa, Fettweis, Gerhard |
| Publisher | IEEE - Institute of Electrical and Electronics Engineers |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:article, info:eu-repo/semantics/article, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 2644-125X, 10.1109/OJCOMS.2022.3166596, info:eu-repo/grantAgreement/European Commission/H2020 | RIA/957216//Next-GENeration IoT sOlutions for the Universal Supply chain/iNGENIOUS, info:eu-repo/grantAgreement/Deutsche Forschungsgemeinschaft/Exzellenzcluster/390696704//EXC 2050: Centre for Tactile Internet with Human-in-the-Loop /CeTI, info:eu-repo/grantAgreement/Bundesministerium für Bildung und Forschung/6G-Life/16KISK001K/ |
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