Two-Stage Resource Allocation in Reconfigurable Intelligent Surface Assisted Hybrid Networks via Multi-Player Bandits
Jingwen Tong, Hongliang Zhang, Liqun Fu, Amir Leshem, Zhu Han

TL;DR
This paper introduces a low-complexity, learning-based resource allocation framework for RIS-assisted IoT networks, optimizing sum rate via a novel two-stage multi-player bandit approach with proven regret bounds.
Contribution
It proposes a two-stage multi-player bandit framework with an E2Boost algorithm for distributed RIS and SF allocation, addressing the lack of prior CSI in IoT networks.
Findings
E2Boost outperforms existing methods in simulations.
The algorithm converges rapidly and is robust to high-density network configurations.
Regret grows logarithmically with time, ensuring efficiency.
Abstract
This paper considers a resource allocation problem where several Internet-of-Things (IoT) devices send data to a base station (BS) with or without the help of the reconfigurable intelligent surface (RIS) assisted cellular network. The objective is to maximize the sum rate of all IoT devices by finding the optimal RIS and spreading factor (SF) for each device. Since these IoT devices lack prior information on the RISs or the channel state information (CSI), a distributed resource allocation framework with low complexity and learning features is required to achieve this goal. Therefore, we model this problem as a two-stage multi-player multi-armed bandit (MPMAB) framework to learn the optimal RIS and SF sequentially. Then, we put forth an exploration and exploitation boosting (E2Boost) algorithm to solve this two-stage MPMAB problem by combining the -greedy algorithm, Thompson…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsIoT and Edge/Fog Computing
MethodsBalanced Selection
