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Random Access Resource Selection in Mobile Networks with Machine Learning
- Author(s)
- BEKELE YARED ZERIHUN
- Advisor
- Young-June Choi
- Department
- 일반대학원 인공지능학과
- Publisher
- The Graduate School, Ajou University
- Publication Year
- 2022-02
- Language
- eng
- Keyword
- Machine learning; mobile networks
- Alternative Abstract
- In future generation network technologies, such as 3GPP’s Narrow- Band Internet of Things (NB-IoT), smart meters, 5G V2X, etc., there is a need to address the uRLLC use case (ultra-reliable and low latency communications). Estimates show that by the end of 2025, 50 billion devices will be connected across the globe. These applications dwell on the aforementioned technologies. Thus, the huge rollout of communicating devices causes congestion and latency. Extensive studies and research have to be conducted to cope up with reducing the latency and improving the reliability of the supporting technologies. For transmission, we focus on 5G’s millimeter-wave (mmWave) bands which 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 that comprises numerous small and macrocells, defined by transmission and reception points (TRxPs). For such a network, random access (RA) is the pivotal task for any mobile node to connect to TRxPs (AP), which experiences delay and congestion. The state of art RA resource selection does not fulfill the envisioned latency requirement of 5G and beyond 5G. RA is considered a challenging function in which users attempt to select an efficient TRxP 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 delivering this information back to users during random access. To solve this problem, we establish an optimization problem and employ a machine- learning-based scheme. Additionally, an important aspect of such estimation is that it can be approached based on machine learning architecture and hyperparameters search. Such a method in a mobile environment is an open research question for RA, in terms of increasing the key performance indicator (KPI) and rewards. Most studies in literature engage neural architecture search for supervised learning tasks, and there is limited work with regard to deep reinforcement learning (DRL). Therefore, we also propose an optimization framework to address the search problem of neural network architecture. The goal is to optimize the DRL algorithm so that it yields an additional performance improvement. This framework consists of a performance extrapolation algorithm. Through extensive simulations, we demonstrate that our proposed machine learning-based approach and revealed architectures to solving the problem discussed improve performance on random access. Specifically, experiments demonstrate a reduction of the RA delay and significant improvement of the access success probability. Furthermore, the proposed extrapolation algorithm based on LSTM shows better learning curve prediction as compared to notable regression techniques, such as linear interpolation.
- Fulltext
- Appears in Collections:
- Graduate School of Ajou University > Department of Artificial Intelligence > 4. Theses(Ph.D)
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