Abstract
In this work, a multitask diffusion least mean square (MDLMS) algorithm is developed via orthonormal codes in ad-hoc networks. Unlike the existing MDLMS approaches, where the adaptive combiner matrix is altered and becomes a disconnected graph, the newly proposed MDLMS approach preserves the combining matrix as a connected graph based on the orthonormal codes. The connected graph property allows nodes located in a similar cluster to exchange their knowledge by node cooperation to nearby and faraway nodes. In the simulations, the performance of the newly proposed MDLMS and the existing adaptive combiner methods are similar; however, the newly proposed MDLMS method posses a unique feature which doesn't alter the connected graph property over ad-hoc networks.
| Original language | English |
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| Title of host publication | 2024 7th International Conference on Signal Processing and Information Security, ICSPIS 2024 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9798350368673 |
| DOIs | |
| State | Published - 2024 |
| Event | 7th International Conference on Signal Processing and Information Security, ICSPIS 2024 - Dubai, United Arab Emirates Duration: 12 Nov 2024 → 14 Nov 2024 |
Publication series
| Name | 2024 7th International Conference on Signal Processing and Information Security, ICSPIS 2024 |
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Conference
| Conference | 7th International Conference on Signal Processing and Information Security, ICSPIS 2024 |
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| Country/Territory | United Arab Emirates |
| City | Dubai |
| Period | 12/11/24 → 14/11/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
Keywords
- connected graph
- diffusion least mean square
- mean squared deviation
- multitask networks
- orthonormal codes
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Information Systems
- Signal Processing
- Safety, Risk, Reliability and Quality