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Neural network based decision fusion for abnormality detection via molecular communications
conference contribution
posted on 2023-06-09, 21:58 authored by Sinem Nimet Solak, Menguc OnerMenguc OnerAbnormality detection is one of the most highly anticipated application areas of Molecular Communication (MC) based nanonetworks. This task entails sensing, detection, and reporting of abnormal changes in a fluid medium that may characterize a disease or disorder using a network of collaborating nanoscale sensors. Existing strategies for such distributed collaborative detection problems require a complete statistical characterization of the underlying communication channel between the sensors and the fusion centre (FC), with the assumption of perfectly-known or accurately estimated channel parameters. This assumption is usually impractical both due to mathematical intractability of the analytical channel models for MC except in a few ideal cases, and the slow and dispersive signal propagation characteristics that make the channel estimation a difficult task even in these ideal cases. This work, for the first time in the literature, proposes to employ a machine learning approach to this task and shows that this approach provides the robustness and flexibility required for practical implementation. We focus on detection based on deep learning, specifically on a feed-forward neural network and a recurrent neural network structure that learn the underlying model from data. This study shows that the proposed decision fusion strategy can perform well without any knowledge of the communication channel.
History
Publication status
- Published
File Version
- Accepted version
Journal
2020 IEEE Workshop on Signal Processing Systems (SiPS)ISSN
1520-6130Publisher
IEEEExternal DOI
Event name
IEEE International Workshop on Signal Processing Systems 2020Event location
Virtual conferenceEvent type
conferenceEvent date
20-22 October 2020ISBN
9781728181004Department affiliated with
- Engineering and Design Publications
Notes
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksFull text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2020-10-22First Open Access (FOA) Date
2020-10-27First Compliant Deposit (FCD) Date
2020-10-22Usage metrics
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