| 1. |
- Alay, Özgü, et al.
(författare)
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Monitoring and Analytics (Release B)
- 2021
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Rapport (refereegranskat)abstract
- This document describes the design and implementation of the 5GENESIS Monitoring & Analytics (M&A) framework in its Release B, developed within Task T3.3 of the project work plan. M&A Release B leverages and extends M&A Release A, which has been documented in the previous Deliverable D3.5 [1]. In particular, we present new features and enhancements introduced in this new Release compared to the Release A. We also report some examples of usage of the M&A framework, in order to showcase its integrated in the 5GENESIS Reference Architecture.
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| 2. |
- Aumayr, Erik, et al.
(författare)
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A Safe Reinforcement Learning Architecture for Antenna Tilt Optimisation
- 2021
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Ingår i: 2021 Ieee 32Nd Annual International Symposium On Personal, Indoor And Mobile Radio Communications (PIMRC). - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- Safe interaction with the environment is one of the most challenging aspects of Reinforcement Learning (RL) when applied to real-world problems. This is particularly important when unsafe actions have a high or irreversible negative impact on the environment. In the context of network management operations, Remote Electrical Tilt (RET) optimisation is a safety-critical application in which exploratory modifications of antenna tilt angles of base stations can cause significant performance degradation in the network. In this paper, we propose a modular Safe Reinforcement Learning (SRL) architecture which is then used to address the RET optimisation in cellular networks. In this approach, a safety shield continuously benchmarks the performance of RL agents against safe baselines, and determines safe antenna tilt updates to be performed on the network. Our results demonstrate improved performance of the SRL agent over the baseline while ensuring the safety of the performed actions.
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| 3. |
- Caso, Giuseppe, et al.
(författare)
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Monitoring and Analytics (Release A)
- 2019
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Rapport (refereegranskat)abstract
- This document describes the design and implementation of the 5GENESIS Monitoring & Analytics (M&A) framework (Release A), developed within Task T3.3 of the Project work plan.
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| 4. |
- Vannella, Filippo, et al.
(författare)
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Remote Electrical Tilt Optimization via Safe Reinforcement Learning
- 2021
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Ingår i: 2021 IEEE wireless communications and networking conference (WCNC). - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- Remote Electrical Tilt (RET) optimization is an efficient method for adjusting the vertical tilt angle of Base Stations (BSs) antennas in order to optimize Key Performance Indicators (KPIs) of the network. Reinforcement Learning (RL) provides a powerful framework for RET optimization because of its self-learning capabilities and adaptivity to environmental changes. However, an RL agent may execute unsafe actions during the course of its interaction, i.e., actions resulting in undesired network performance degradation. Since the reliability of services is critical for Mobile Network Operators (MNOs), the prospect of performance degradation has prohibited the real-world deployment of RL methods for RET optimization. In this work, we model the RET optimization problem in the Safe Reinforcement Learning (SRL) framework with the goal of learning a tilt control strategy providing performance improvement guarantees with respect to a safe baseline. We leverage a recent SRL method, namely Safe Policy Improvement through Baseline Bootstrapping (SPIBB), to learn an improved policy from an offline dataset of interactions collected by the safe baseline. Our experiments show that the proposed approach is able to learn a safe and improved tilt update policy, providing a higher degree of reliability and potential for real-world network deployment.
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