Deep Reinforcement Learning Aided Cell Outage Compensation Framework in 5G Cloud Radio Access Networks
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Deep Reinforcement Learning Aided Cell Outage Compensation Framework in 5G Cloud Radio Access Networks Peng Yu 1
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Xiao Yang 1 & Fanqin Zhou 1 & Hao Li 1 & Lei Feng 1 & Wenjing Li 1 & Xuesong Qiu 1
# Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract As one of the key technologies of 5G, Cloud Radio Access Networks (C-RAN) with cloud BBUs (Base Band Units) pool architecture and distributed RRHs (Remote Radio Heads) can provide the ubiquitous services. When failure occurs at RRH, it can’t be alleviated in time and will lead to a significant drop in network performance. Therefore, the cell outage compensation (COC) problem for RRH in 5G C-RAN is very important. Although deep reinforcement learning (DRL) has been applied to many scenarios related to the self-organizing network (SON), there are fewer applications for cell outage compensation. And most intelligent algorithms are hard to obtain globally optimized solutions. In this paper, aiming at the cell outage scenario in C-RAN with the goal of maximizing the energy efficiency, connectivity of RRH while meeting service quality demands of each compensation user, a framework based on DRL is presented to solve it. Firstly, compensation users are allocated to adjacent RRHs by using the K-means clustering algorithm. Secondly, DQN is used to find the antenna downtilt and the power allocated to compensation users. Comparing to different genetic algorithms, simulation result shows that the proposed framework converges quickly and tends to be stable, and reaches 95% of the maximum target value. It verifies the efficiency of the DRL-based framework and its effectiveness in meeting user requirements and handling cell outage compensation. Keywords 5G C-RAN . Deep reinforcement learning . Cell outage compensation
1 Introduction With the continuous development of wireless communication technology, 5G will be a combination of centralized performance processing and distributed demodulation with virtualized and software defined network functions [1]. As one of the key technologies for 5G wireless communications, C-RAN has become a new type of network architecture because of its advantages of cloud computing and distributed processing, which can realize the efficient usage and share sharing of computing resources [2]. In C-RAN BBU pools are centralized into cloud computing nodes, and they can communicate with each other and control regional access nodes such as RRHs and existing legacy communication entities.
* Peng Yu [email protected] 1
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
Besides, it can reduce the delay and meet the requirements of high-traffic, high-density wireless terminal access. Under 5G C-RAN scenarios, massive devices are connected to the RRHs in the network to support eMBB, URLLC or mMTC services [3]. As C-RAN’s structure is more complex, its management is very complicated, which means it faces many challenges such as potential hidden failure
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