High-Priority Minimum-Interference Channel Assignment in Multi-Radio Multi-Channel Wireless Networks

Li, Gang; Hu, Chunjing; Peng, Tao; Zhou, Xiaohui; Xu, Yueqing

doi:10.1145/3291842.3291880

Proceedings of the 2nd International Conference on Telecommunications and Communication Engineering 2018

DOI: 10.1145/3291842.3291880

|View full text |Cite

High-Priority Minimum-Interference Channel Assignment in Multi-Radio Multi-Channel Wireless Networks

Gang Li

Chunjing Hu

Tao Peng

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Other1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Deep Index Policy for Multi-Resource Restless Matching Bandit and Its Application in Multi-Channel Scheduling

Zamir,

Hou

2024

Proceedings of the Twenty-Fifth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Netw

View full text Add to dashboard Cite

Scheduling in multi-channel wireless communication system presents formidable challenges in effectively allocating resources. To address these challenges, we investigate a multi-resource restless matching bandit (MR-RMB) model for heterogeneous resource systems with an objective of maximizing long-term discounted total rewards while respecting resource constraints. We have also generalized to applications beyond multi-channel wireless. We discuss the Max-Weight Index Matching algorithm, which optimizes resource allocation based on learned partial indexes. We have derived the policy gradient theorem for index learning. Our main contribution is the introduction of a new Deep Index Policy (DIP), an online learning algorithm tailored for MR-RMB. DIP learns the partial index by leveraging the policy gradient theorem for restless arms with convoluted and unknown transition kernels of heterogeneous resources. We demonstrate the utility of DIP by evaluating its performance for three different MR-RMB problems. Our simulation results show that DIP indeed learns the partial indexes efficiently. CCS CONCEPTS• Theory of computation → Sequential decision making.

show abstract

Deep Index Policy for Multi-Resource Restless Matching Bandit and Its Application in Multi-Channel Scheduling

Zamir,

Hou

2024

Proceedings of the Twenty-Fifth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Netw

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

High-Priority Minimum-Interference Channel Assignment in Multi-Radio Multi-Channel Wireless Networks

Cited by 1 publication

References 10 publications

Deep Index Policy for Multi-Resource Restless Matching Bandit and Its Application in Multi-Channel Scheduling

Deep Index Policy for Multi-Resource Restless Matching Bandit and Its Application in Multi-Channel Scheduling

Contact Info

Product

Resources

About