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A Fairness of Data Combination in Wireless Packet Scheduling
Sovit Bhandari1, Navin Ranjan1, Yeong-Chan Kim1,2
1IoT and Big-Data Research Center, Incheon National University, Yeonsu-gu, Incheon 22012, Korea.
Sensors (Basel, Switzerland)
|February 26, 2022
Summary
This study addresses AI ethics in 6G wireless networks by proposing novel data categorization and combination methods. These techniques aim to improve data fairness, crucial for ethical AI deployment in future networks.
Area of Science:
- Artificial Intelligence
- Wireless Communications
- Data Science
Background:
- The widespread adoption of artificial intelligence (AI) necessitates its integration into sixth-generation (6G) wireless networks.
- Ethical considerations surrounding AI, particularly data fairness, are increasingly critical with AI's rapid advancement.
Purpose of the Study:
- To investigate the ethical concerns of AI in wireless networks, focusing on data fairness.
- To propose novel methods for enhancing data fairness in AI datasets used for wireless networks.
Main Methods:
- A deep-learning-based dataset categorization (DLDC) model was developed to classify datasets.
- Datasets were categorized by group index and combined using various schemes.
- Simulations were conducted to compare different dataset combination methods and their performance.
Main Results:
- The study analyzed the performance and fairness trade-offs associated with different dataset configurations.
- The proposed DLDC model and combination schemes demonstrated potential for improving data fairness.
Conclusions:
- Novel dataset categorization and combination schemes can mitigate ethical concerns related to data fairness in AI for 6G wireless networks.
- The findings provide insights into optimizing dataset configurations for balanced fairness and performance.
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