TY - GEN
T1 - Approximated Whittle index for femtocell scheduling
AU - Zhang, Yiying
AU - Liu, Keqin
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025/7/2
Y1 - 2025/7/2
N2 - This study focuses on the channel selection problem for femto base stations (FBSs) that share channels with macro base stations (MBSs). We formulate the femtocell scheduling problem within the framework of restless multi-armed bandits (RMAB). Our objective is to select channels that maximize the expected discounted return over an infinite horizon while minimizing interference to the macrocell caused by the shared channels with femtocells. Since the true channel state is not directly observable, we utilize the available feedback from users known as the channel quality indicator (CQI). Generally, the RMAB problem is recognized as PSPACE-hard. To tackle this challenge, we derive a closed-form approximation for the channel index by employing an iterative method. Based on this approximated index, we improve an existing policy called the approximated Whittle index policy, which provides a low-complexity solution for ranking the available channels for FBSs. Furthermore, we demonstrate the superior performance of the proposed algorithm compared to the existing approach.
AB - This study focuses on the channel selection problem for femto base stations (FBSs) that share channels with macro base stations (MBSs). We formulate the femtocell scheduling problem within the framework of restless multi-armed bandits (RMAB). Our objective is to select channels that maximize the expected discounted return over an infinite horizon while minimizing interference to the macrocell caused by the shared channels with femtocells. Since the true channel state is not directly observable, we utilize the available feedback from users known as the channel quality indicator (CQI). Generally, the RMAB problem is recognized as PSPACE-hard. To tackle this challenge, we derive a closed-form approximation for the channel index by employing an iterative method. Based on this approximated index, we improve an existing policy called the approximated Whittle index policy, which provides a low-complexity solution for ranking the available channels for FBSs. Furthermore, we demonstrate the superior performance of the proposed algorithm compared to the existing approach.
KW - Femtocell
KW - Reinforcement learning
KW - Resource allocation
KW - Restless multi-armed bandit
KW - Whittle index
UR - https://www.scopus.com/pages/publications/105017378020
U2 - 10.1007/978-981-96-2379-2_29
DO - 10.1007/978-981-96-2379-2_29
M3 - Conference Proceeding
AN - SCOPUS:105017378020
SN - 9789819623785
T3 - Springer Proceedings in Mathematics and Statistics
SP - 337
EP - 346
BT - Computational Mathematics and Numerical Analysis, CSAMCS 2023
A2 - Dai, Wanyang
A2 - Li, Jichun
PB - Springer
T2 - 3rd International Conference on Statistics, Applied Mathematics and Computing Science, CSAMCS 2023
Y2 - 10 November 2023 through 12 November 2023
ER -