Two-Stage Resource Allocation in Reconfigurable Intelligent Surface Assisted Hybrid Networks via Multi-Player Bandits

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 $\epsilon$-greedy algorithm, Thompson sampling (TS) algorithm, and non-cooperation game method. We derive an upper regret bound for the proposed algorithm, i.e., $\mathcal{O}(\log^{1+\delta}_2 T)$, increasing logarithmically with the time horizon $T$. Numerical results show that the E2Boost algorithm has the best performance among the existing methods and exhibits a fast convergence rate. More importantly, the proposed algorithm is not sensitive to the number of combinations of the RISs and SFs thanks to the two-stage allocation mechanism, which can benefit high-density networks.