Robust directional-diffusive hybrid molecular communication with parity-check erasure coding

Hiroaki Egashira, Junichi Suzuki, Jonathan S. Mitzman, Tadashi Nakano, Hiroaki Fukuda

研究成果: Conference contribution

6 被引用数 (Scopus)

抄録

This paper investigates a robustness enhancement protocols for biologically-enabled machines (or bio-nanomachines) to reliably exchange information by means of molecules in the aqueous, intrabody environment. The proposed protocol performs forward error correction (FEC) for directional-diffusive hybrid transports, in which molecules travel through two different transports: directional and diffusive transports. It allows the transmitter bio-nanomachine to encode molecules in a redundant manner with parity-check erasure codes. The receiver bio-nanomachine can recover the information embedded in lost molecules. Simulation results show that the proposed protocol enhances robustness against molecule losses and in turn improves communication performance. They also reveal the impacts of FEC overhead and molecule redundancy on the communication performance.

本文言語English
ホスト出版物のタイトルIFSA-SCIS 2017 - Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781509049172
DOI
出版ステータスPublished - 2017 8月 30
イベント17th Joint World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems, IFSA-SCIS 2017 - Otsu, Japan
継続期間: 2017 6月 272017 6月 30

出版物シリーズ

名前IFSA-SCIS 2017 - Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems

Other

Other17th Joint World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems, IFSA-SCIS 2017
国/地域Japan
CityOtsu
Period17/6/2717/6/30

ASJC Scopus subject areas

  • 論理
  • 計算理論と計算数学
  • コンピュータ サイエンスの応用
  • 制御と最適化
  • 人工知能

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