Energy-Efficient Resource Allocation for Ultra-Dense Licensed and Unlicensed Dual-Access Small Cell Networks

Adnan Shahid, Vasilis Maglogiannis, Irfan Ahmed, Kwang Soon Kim, Eli De Poorter, Ingrid Moerman

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


In this study, an energy-efficient self-organized framework for sub-channel allocation and power allocation is presented for ultra-dense small cell networks, which can operate in both licensed and unlicensed bands. In order to protect legacy WiFi devices (operating in unlicensed bands), we consider the Long-Term Evolution (LTE) operation in unlicensed bands based on Carrier Sense Adaptive Transmission (CSAT), in which 'ON' and 'OFF' duty cycle approach is utilized. On the other hand, there are severe interference management problems among small cells (operating in licensed and unlicensed bands) and between macro cells and small cells (operating in licensed bands) due to co-channel and ultra-dense deployment of small cells. This article proposes a self-organized optimization framework for the allocation of sub-channels and power levels by exploiting a non-cooperative game with the objective to maximize the energy efficiency of dual-access small cells without creating harmful impact on coexisting network entities including macro cell users, small cell users, and legacy WiFi devices. Simulation results show that the proposed scheme outperforms (6 and 11 percent) and (8 and 18 percent) the round-robin and the spectrum-efficient schemes, respectively, for two different small cell scenarios. In addition, it is shown that for less channel state information (CSI) estimation errors varsigma = 0.02ς=0.02, the maximum performance degradation of the proposed scheme is reasonably small (5.5 percent) as compared to the perfect CSI.

Original languageEnglish
Article number8903493
Pages (from-to)983-1000
Number of pages18
JournalIEEE Transactions on Mobile Computing
Issue number3
Publication statusPublished - 2021 Mar 1

Bibliographical note

Funding Information:
The research leading to these results has received the funding from the Agency for Innovation by Science and Technology, Flanders (IWT-V) (under Grant agreement No. IWT140048 SAMURAI Project []) and from the European Horizon 2020 Programme (under Grant agreement No. 688116 eWINE Project []).

Publisher Copyright:
© 2002-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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