Halim, Dareen Kusuma and Ming, Tang Chong and Song Ng, Mow and Hartono, Dicky (2018) Software-based turbo decoder implementation on low power multi-processor system-on-chip for Internet of Things. In: 4th International Conference on New Media Studies, 8-10 Nov. 2017, Yogyakarta, Indonesia.
Full text not available from this repository.Abstract
Internet of Things technology is highly reliant on wireless radio link, which must be of low error rate to avoid retransmission. Forward error correction (FEC) provides lower error rate by introducing data overhead. A particular FEC, Turbo code has found vast application over various platforms, trading off between flexibility and power-efficiency during implementation. This work proposes a low-power yet flexible Turbo decoder implementation on a low power programmable MPSoC in expense of data throughput, aimed for low-data rate IoT applications. A detailed analysis for software-based implementation and optimization of the decoder targeted for the specific MPSoC is given. The implemented decoder is tested on a real SDR-based wireless transceiver system and compared against its non-coded version. Test result shows significantly lower error rate for the coded system compared to the non-coded version, that is around 50-60 percent on the 3 rd iteration of Turbo decoding.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Keywords: | Turbo code , Internet of Things (IoT) , Software defined radio (SDR) , Multi-Processor System-on-Chip (MPSoC) , Low power radio |
| Subjects: | 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 005 Computer Programming 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 005 Computer Programming > 005.5 Application / Software 600 Technology (Applied Sciences) > 600 Technology |
| Divisions: | Faculty of Engineering & Informatics > Computer Engineering |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 02 Dec 2021 14:55 |
| Last Modified: | 24 Aug 2023 05:51 |
| URI: | https://kc.umn.ac.id/id/eprint/19321 |
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