Base Station Power Optimization for Green Networks Using Reinforcement Learning

2021-08-31
Aktaş, Semih
Alemdar, Hande
The next generation mobile networks have to provide high data rates, extremely low latency, and support high connection density. To meet these requirements, the number of base stations will have to increase and this increase will lead to an energy consumption issue. Therefore “green” approaches to the network operation will gain importance. Reducing the energy consumption of base stations is essential for going green and also it helps service providers to reduce operational expenses. However, achieving energy savings without degrading the quality of service is a huge challenge. In order to address this issue, we propose a machine learning based intelligent solution that also incorporates a network simulator. We develop a reinforcement-based learning model by using deep deterministic policy gradient algorithm. Our model update frequently the policy of network switches in a way that, packet be forwarded to base stations with an optimized power level. The policies taken by the network controller are evaluated with a network simulator to ensure the energy consumption reduction and quality of service balance. The reinforcement learning model allows us to constantly learn and adapt to the changing situations in the dynamic network environment, hence having a more robust and realistic intelligent network management policy set. Our results demonstrate that energy efficiency can be enhanced by 32% and 67% in dense and sparse scenarios, respectively.
Sakarya University Journal of Computer and Information Sciences

Suggestions

Base station power optimization for green networks using reinforcement learning
Aktaş, Semih; Alemdar, Hande; Department of Computer Engineering (2019)
The next generation mobile networks have to provide high data rates, extremely low latency, and support high connection density. To meet these requirements, the number of base stations will have to increase and this increase will lead to an energy consumption issue. Therefore ``green'' approaches to the network operation will gain importance. Reducing the energy consumption of base stations is essential for going green and also it helps service providers to reduce operational expenses. However, achieving en...
Content Placement Problem in a Hierarchical Collaborative Caching method for 5G networks (CPP-HCC)
Hassanzadeh, Farnaz; Onur, Ertan (2020-01-01)
The increasing demand for video streaming has imposed tremendous data rates and minimal end-to-end latency requirements on 5G mobile networks. Caching content close to the users is one of the conventional ways to meet these requirements. Subsequent requests for the same content can be supplied from the cache with minimal delay. In this paper, we present a content placement problem in a hierarchical collaborative caching (CPP-HCC) in 5G networks that can determine the location of the replica contents by solv...
Optimal dynamic resource allocation for heterogenous cloud data centers
Ekici, Nazım Umut; Güran Schmidt, Şenan.; Department of Electrical and Electronics Engineering (2019)
Today's data centers are mostly cloud-based with virtualized servers to provide on-demand scalability and flexibility of the available resources such as CPU, memory, data storage and network bandwidth. Heterogeneous cloud data centers (CDCs) offer hardware accelerators in addition to these standard cloud server resources. A cloud data center provider may provide Infrastructure as a Service and Platform as a Service (IPaaS), where the user gets a virtual machine (VM) with processing, memory, storage and netw...
Density estimation in large-scale wireless sensor networks
Eroğlu, Alperen; Onur, Ertan; Oğuztüzün, Mehmet Halit S.; Department of Computer Engineering (2015)
Density estimation is a significant problem in large-scale wireless ad-hoc networks since the density drastically impacts the network performance. It is crucial to make the network adaptive in the run-time to the density changes that may not be predictable in advance. Local density estimators are required while taking run-time control decisions to improve the network performance. A wireless node may estimate the density locally by measuring the received signal strength (RSS) of packets sent by its neighbour...
GreenSlice: An Energy-Efficient Secure Network Slicing Framework
Akin, Ozan; Gulmez, Umut Can; Sazak, Ozan; Yagmur, Osman Ufuk; Angın, Pelin (2022-02-01)
The fifth generation of telecommunication networks comes with various use cases such as Enhanced Mobile Broadband, Ultra-Reliable and Low Latency Communications and Massive Machine Type Communications. These different types of communications have diverse requirements that need to be satisfied while they utilize the same physical infrastructure. By leveraging Software Defined Network (SDN) and Virtual Network Function (VNF) technologies, the 5G network slicing concept can provide end-to-end logical networks ...
Citation Formats
S. Aktaş and H. Alemdar, “Base Station Power Optimization for Green Networks Using Reinforcement Learning,” Sakarya University Journal of Computer and Information Sciences, vol. 4, no. 2, pp. 244–265, 2021, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/92914.