Random Access Using Deep Reinforcement Learning in Dense Mobile Networks

Sensors

YaredZerihun Bekele¹  Young-June Choi² 

[1] Department of Artificial Intelligence, Ajou University, Suwon 16499, Korea;Department of Software and Computer Engineering, Ajou University, Suwon 16499, Korea;

关键词: machine learning;    optimization;    random access;   

DOI  :  10.3390/s21093210

来源: DOAJ

【 摘 要 】

5G and Beyond 5G mobile networks use several high-frequency spectrum bands such as the millimeter-wave (mmWave) bands to alleviate the problem of bandwidth scarcity. However high-frequency bands do not cover larger distances. The coverage problem is addressed by using a heterogeneous network which comprises numerous small and macrocells, defined by transmission and reception points (TRxPs). For such a network, random access is considered a challenging function in which users attempt to select an efficient TRxP by random access within a given time. Ideally, an efficient TRxP is less congested, minimizing delays in users’ random access. However, owing to the nature of random access, it is not feasible to deploy a centralized controller estimating the congestion level of each cell and deliver this information back to users during random access. To solve this problem, we establish an optimization problem and employ a reinforcement-learning-based scheme. The proposed scheme estimates congestion of TRxPs in service and selects the optimal access point. Mathematically, this approach is beneficial in approximating and minimizing a random access delay function. Through simulation, we demonstrate that our proposed deep learning-based algorithm improves performance on random access. Notably, the average access delay is improved by 58.89% from the original 3GPP algorithm, and the probability of successful access also improved.

【 授权许可】

Unknown