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Revised reinforcement learning based on anchor graph hashing for autonomous cell activation in cloud-RANs
یادگیری تقویتی اصلاح شده بر اساس هش کردن نمودار لنگر برای فعال سازی سلول مستقل در ابرهای-RAN -2020 Cloud radio access networks (C-RANs) have been regarded in recent times as a promising concept in
future 5G technologies where all DSP processors are moved into a central base band unit (BBU) pool
in the cloud, and distributed remote radio heads (RRHs) compress and forward received radio signals
from mobile users to the BBUs through radio links. In such dynamic environment, automatic decisionmaking
approaches, such as artificial intelligence based deep reinforcement learning (DRL), become
imperative in designing new solutions. In this paper, we propose a generic framework of autonomous
cell activation and customized physical resource allocation schemes to balance energy consumption
and QoS satisfaction in wireless networks. We formulate the cell activation problem as a Markov
decision process and set up a revised reinforcement learning model based on K-means clustering and
anchor-graph hashing to satisfy the QoS requirements of users and to achieve low energy consumption
with the minimum number of active RRHs under varying traffic demand and user mobility. Extensive
simulations are conducted to show the effectiveness of our proposed solution compared with existing
schemes. Keywords: Reinforcement learning | Anchor graph hashing | K-means clustering | Autonomous cell activation | Cloud radio access networks |
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