Reinforcement Learning Based Dynamic Power Control for UAV Mobility Management

التفاصيل البيبلوغرافية
العنوان: Reinforcement Learning Based Dynamic Power Control for UAV Mobility Management
المؤلفون: Meer, Irshad A., Besser, Karl-Ludwig, Ozger, Mustafa, Poor, H. Vincent, Cavdar, Cicek
المصدر: 2023 57th Asilomar Conference on Signals, Systems, and Computers, Oct. 2023, pp. 724-728
سنة النشر: 2023
المجموعة: Computer Science
Mathematics
مصطلحات موضوعية: Computer Science - Information Theory, Electrical Engineering and Systems Science - Signal Processing
الوصف: Modern communication systems need to fulfill multiple and often conflicting objectives at the same time. In particular, new applications require high reliability while operating at low transmit powers. Moreover, reliability constraints may vary over time depending on the current state of the system. One solution to address this problem is to use joint transmissions from a number of base stations (BSs) to meet the reliability requirements. However, this approach is inefficient when considering the overall total transmit power. In this work, we propose a reinforcement learning-based power allocation scheme for an unmanned aerial vehicle (UAV) communication system with varying communication reliability requirements. In particular, the proposed scheme aims to minimize the total transmit power of all BSs while achieving an outage probability that is less than a tolerated threshold. This threshold varies over time, e.g., when the UAV enters a critical zone with high-reliability requirements. Our results show that the proposed learning scheme uses dynamic power allocation to meet varying reliability requirements, thus effectively conserving energy.
Comment: 5 pages, 3 figures
نوع الوثيقة: Working Paper
DOI: 10.1109/IEEECONF59524.2023.10477032
URL الوصول: http://arxiv.org/abs/2312.04742
رقم الأكسشن: edsarx.2312.04742
قاعدة البيانات: arXiv
الوصف
DOI:10.1109/IEEECONF59524.2023.10477032