Data Recovery through Modulation Identification in Dense Wireless Networks
With rise in device complexity and transmission rates, reliability in data recovery has become another critical issue requiring costly and computationally demanding mechanism. The popularity of artificial intelligence (AI) and its ubiquitousness have established the usefulness of design of data reco...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2018-01-01
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| Series: | MATEC Web of Conferences |
| Subjects: | |
| Online Access: | https://doi.org/10.1051/matecconf/201821003009 |
| Summary: | With rise in device complexity and transmission rates, reliability in data recovery has become another critical issue requiring costly and computationally demanding mechanism. The popularity of artificial intelligence (AI) and its ubiquitousness have established the usefulness of design of data recovery schemes where device level complexity is less. Lower device complexity is being ensured by the use of AI driven data recovery. In this work, we focus on the design of such a mechanism where traditional process are replaced by a neuro-computing structure. The advantage is lower levels of device complexity but incorporation of a training latency. Experimental results have established the reliability of the proposed system. |
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| ISSN: | 2261-236X |