TY - JOUR
T1 - A Large-Scale Energy Efficient Time-Domain Pilot Muting Mechanism for Non-Stationary Channels
T2 - A Deep Reinforcement Learning Approach
AU - Sun, Yanzan
AU - Ye, Xinrui
AU - Tan, Hongchang
AU - Zhang, Shunqing
AU - Chen, Xiaojing
AU - Xu, Shugong
N1 - Publisher Copyright:
© 2012 IEEE.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - To address rapid fluctuations in non-stationary channel environments, adaptive pilot patterns are commonly used. However, frequent pilot pattern changes require an interactive mechanism for terminal communication. In this letter, we introduce a time-domain pilot muting mechanism (TDPMM) based on the densest pilot pattern, combined with power optimization using deep reinforcement learning. This approach aims to mitigate issues associated with frequent pilot pattern adjustments in non-stationary channel fading environments. We first formulate an energy efficiency (EE) optimization model that balances normalized mean square error (NMSE) and energy consumption (EC) for large-scale continuous resource blocks (RBs) using TDPMM. Then, we propose a deep Q-network (DQN) based learning strategy tailored to optimize TDPMM selection and the power ratio between pilot and data signals. Simulation experiments confirm the superior EE performance of the proposed TDPMM. Furthermore, our DQN-based approach demonstrates lower complexity and only slightly inferior performance compared to the exhaustive search strategy.
AB - To address rapid fluctuations in non-stationary channel environments, adaptive pilot patterns are commonly used. However, frequent pilot pattern changes require an interactive mechanism for terminal communication. In this letter, we introduce a time-domain pilot muting mechanism (TDPMM) based on the densest pilot pattern, combined with power optimization using deep reinforcement learning. This approach aims to mitigate issues associated with frequent pilot pattern adjustments in non-stationary channel fading environments. We first formulate an energy efficiency (EE) optimization model that balances normalized mean square error (NMSE) and energy consumption (EC) for large-scale continuous resource blocks (RBs) using TDPMM. Then, we propose a deep Q-network (DQN) based learning strategy tailored to optimize TDPMM selection and the power ratio between pilot and data signals. Simulation experiments confirm the superior EE performance of the proposed TDPMM. Furthermore, our DQN-based approach demonstrates lower complexity and only slightly inferior performance compared to the exhaustive search strategy.
KW - deep reinforcement learning
KW - energy efficiency
KW - Non-stationary channel
KW - TDPMM
UR - http://www.scopus.com/inward/record.url?scp=85174829785&partnerID=8YFLogxK
U2 - 10.1109/LWC.2023.3321763
DO - 10.1109/LWC.2023.3321763
M3 - Article
AN - SCOPUS:85174829785
SN - 2162-2337
VL - 13
SP - 93
EP - 97
JO - IEEE Wireless Communications Letters
JF - IEEE Wireless Communications Letters
IS - 1
ER -