Energy Optimization in Ultra-Dense Radio Access Networks via Traffic-Aware Cell Switching

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ÖZTÜRK M., Abubakar A. I., Nadas J. P. B., Rais R. N. B., Hussain S., Imran M. A.

IEEE Transactions on Green Communications and Networking, vol.5, no.2, pp.832-845, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 5 Issue: 2
  • Publication Date: 2021
  • Doi Number: 10.1109/tgcn.2021.3056235
  • Journal Name: IEEE Transactions on Green Communications and Networking
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.832-845
  • Keywords: Switches, Computer architecture, Microprocessors, Heuristic algorithms, Quality of service, Energy consumption, Optimization, 5G, reinforcement learning, cell switching, energy consumption, cellular networks
  • Ankara Yıldırım Beyazıt University Affiliated: Yes


© 2017 IEEE.We propose a reinforcement learning-based cell switching algorithm to minimize the energy consumption in ultra-dense deployments without compromising the quality of service (QoS) experienced by the users. In this regard, the proposed method can intelligently learn which small cells (SCs) to turn off at any given time based on the traffic load of the SCs and the macro cell. To validate the idea, we used the open call detail record (CDR) data set from the city of Milan, Italy, and tested our algorithm against typical operational benchmark solutions. With the obtained results, we demonstrate exactly when and how the proposed method can provide energy savings, and moreover how this happens without reducing QoS of users. Most importantly, we show that our solution has a very similar performance to the exhaustive search, with the advantage of being scalable and less complex.