Optimal Coding Scheme Selection for LoRa-Based Satellite IoT Transmission
Considering the extremely low signal-to-noise ratio (SNR) environment in future satellite internet of Things (IoT) transmission in which the traditional Hamming coded LoRa (Long Range) system in the ground IoT communication is of difficulty to guarantee high reliability, optimal coding scheme selection for the LoRa-based satellite IoT is studied for the first time. In specific, the corresponding system model is described in detail, followed by derivations of two noncoherent detection methods of coded LoRa signals. Subsequently, the channel capacity of the coded LoRa system is provided and used to develop the corresponding relationship between the signal-to-noise ratio (SNR) and the code rate. Based on the obtained relationship and the Monte-Carlo method, it is found that the coded LoRa system with different spreading factors (SFs) can achieve the lowest SNR when the code rate is close to 1/2. Finally, the performance of several coded LoRa systems (including Hamming code, BCH code, RS code, Turbo code, LDPC code and LDGM code with code rates of about 1/2) is compared by means of the Monte-Carlo method. Simulation results show that Turbo or LDPC coded LoRa systems exhibit more than 4.5 dB performance advantages over the Hamming coded LoRa system.
Authors
- Zhongyang Yu (ORCID: https://orcid.org/0000-0002-4220-7777)
- Lingxi Guo (ORCID: https://orcid.org/0000-0003-1191-8397)
- Hengzhou Xu (ORCID: https://orcid.org/0000-0001-9403-4460)
- Wei Wang (ORCID: https://orcid.org/0000-0002-6104-7908)
- Mengmeng Xu (ORCID: https://orcid.org/0000-0001-6110-6093)
- Yuqian Yan
- Hai Zhu
Institutions
- North China University of Water Resources and Electric Power (CN)
- Henan University of Technology (CN)
- Henan University of Engineering (CN)
Publication Details
- Published
- 2024-11-05
- DOI
- https://doi.org/10.1109/apcc62576.2024.10767970
- Citations
- 1
- Primary Topic
- Satellite Communication Systems
- Type
- article
- Field-Weighted Citation Impact
- 1.30
Funders
- National Natural Science Foundation of China