Electronic Resource
Multi-agent Reinforcement Learning for Energy Saving in Multi-Cell Massive MIMO Systems
| العنوان: | Multi-agent Reinforcement Learning for Energy Saving in Multi-Cell Massive MIMO Systems |
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| المؤلفون: | Cai, Tianzhang, Wang, Qichen, Zhang, Shuai, Demir, Ozlem Tugfe, Cavdar, Cicek |
| بيانات النشر: | KTH, Kommunikationssystem, CoS KTH, Radio Systems Laboratory (RS Lab) TOBB ETU, Department of Electrical-Electronics Engineering, Ankara, Türkiye Institute of Electrical and Electronics Engineers Inc. 2024 |
| نوع الوثيقة: | Electronic Resource |
| مستخلص: | We develop a multi-agent reinforcement learning (MARL) algorithm to minimize the total energy consumption of multiple massive MIMO (multiple-input multiple-output) base stations (BSs) in a multi-cell network while preserving the overall quality-of-service (QoS) by making decisions on the multi-level advanced sleep modes (ASMs) and antenna switching of these BSs. The problem is modeled as a decentralized partially observable Markov decision process (DEC-POMDP) to enable collaboration between individual BSs, which is necessary to tackle inter-cell interference. A multi-agent proximal policy optimization (MAPPO) algorithm is designed to learn a collaborative BS control policy. To enhance its scalability, a modified version called MAPPO-neighbor policy is further proposed. Simulation results demonstrate that the trained MAPPO agent achieves better performance compared to baseline policies. Specifically, compared to the auto sleep mode 1 (symbol-level sleeping) algorithm, the MAPPO-neighbor policy reduces power consumption by approximately 8.7% during low-traffic hours and improves energy efficiency by approximately 19% during high-traffic hours, respectively. QC 20240927Part of ISBN 9798350343199 |
| مصطلحات الفهرس: | antenna switching, BS control for energy saving, massive MIMO, multi-agent reinforcement learning, Communication Systems, Kommunikationssystem, Computer Sciences, Datavetenskap (datalogi), Telecommunications, Telekommunikation, Conference paper, info:eu-repo/semantics/conferenceObject, text |
| DOI: | 10.1109.ICMLCN59089.2024.10624787 |
| URL: | 2024 IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2024, p. 480-485 |
| الاتاحة: | Open access content. Open access content info:eu-repo/semantics/restrictedAccess |
| ملاحظة: | English |
| Other Numbers: | UPE oai:DiVA.org:kth-353562 0009-0004-0894-0360 0000-0003-0525-4491 doi:10.1109/ICMLCN59089.2024.10624787 Scopus 2-s2.0-85202434656 1457579865 |
| المصدر المساهم: | UPPSALA UNIV LIBR From OAIster®, provided by the OCLC Cooperative. |
| رقم الانضمام: | edsoai.on1457579865 |
| قاعدة البيانات: | OAIster |
| DOI: | 10.1109.ICMLCN59089.2024.10624787 |
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