In this paper, we propose a novel nano-molecular communication system, including a nano receiver design and detection strategies. We show how machine intelligence can be incorporated into the practical implementation of nano communications. We introduce a testbed employing a biosensor chip as a receiver. The chip is made to be sufficiently small to be implanted under the human skin with no harm while detecting concentrations of glucose molecules over time. Molecules are released by a transmitter, to convey information through a thin pipe. For this configuration, the channel model is unknown, and the sensor dynamics can differ with according to the manufacturing process. Therefore, it is more desirable to find a universal strategy than using closed-form channel expressions so that it can be less sensitive to the channel and sensor variation. Learning-based approaches are likely to solve the problem. Therefore, in this paper, we suggest detection strategies with and without machine learning. We first describe our intuitions of nanomachine design from observations, and we show how the learning-based techniques can benefit the system by reducing the design burden and enhancing the accuracy of data detection. The study concludes by showing sample results of real data transmission.
|Title of host publication||2020 IEEE International Conference on Communications, ICC 2020 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2020 Jun|
|Event||2020 IEEE International Conference on Communications, ICC 2020 - Dublin, Ireland|
Duration: 2020 Jun 7 → 2020 Jun 11
|Name||IEEE International Conference on Communications|
|Conference||2020 IEEE International Conference on Communications, ICC 2020|
|Period||20/6/7 → 20/6/11|
Bibliographical noteFunding Information:
This research was supported in part by the Basic Science Research Program (NRF-2017R1A1A1A05001439), funded by the MSIT(Ministry of Science and ICT), through the National Research Foundation of Korea, and in part by the MSIT, under the “ICT Consilience Creative Program” (IITP-2019-2017-0-01015) supervised by the IITP.
© 2020 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Electrical and Electronic Engineering