Energy and Spectrum Optimization for 5G Massive MIMO Cognitive Femtocell Based Mobile Network Using Auction Game Theory

  • Subha Ghosh
  • Debashis DeEmail author
  • Priti Deb


Energy and spectrum optimization for massive multiple input multiple output (MIMO) cognitive femtocell based fifth generation (5G) mobile network is developed using auction game. In 5G massive MIMO, multiple numbers of antennas are used to transmit the signal with same time frequency to maximize the number of users, who can communicate with less number of channels. Cognitive radio network (CRN) also increases spectrum efficiency by sharing primary users channel to transmit data for secondary users. In this article, cognitive femtocell base stations are treated as secondary base stations to win a channel by using auction game with utility function. Femtocell base stations bid for a channel with pricing value to the MIMO base station spectrum manager and the spectrum manager allocates spectrum to the femtocell base station based on maximum pricing value. Opportunistic spectrum access by femtocell using cognitive approach decreases number of active antennas in massive MIMO based network which reduces energy consumption. Simulation results show that the proposed network reduces ~ 70% power consumptions than the existing CRN and only MIMO CRN based strategies. Simulation results also presents that the massive MIMO cognitive femtocell network increases signal to interference plus noise ratio and spectral efficiency ~ 13% and ~ 20% respectively than the existing CRN and only MIMO CRN based approaches.


Massive MIMO Cognitive radio network Auction game Energy efficiency Spectral efficiency Signal to interference plus noise ratio 



Authors are grateful to Department of Science and Technology (DST) for sanctioning a research Project entitled “Dynamic Optimization of Green Mobile Networks: Algorithm, Architecture and Applications” under Fast Track Young Scientist Scheme Reference No.: SERB/F/5044/2012-2013, DST FIST Reference No.: SR/FST/ETI-296/2011 and TEQIP III (Grant No. 2018/makaut,wb) of MHRD a world bank project under which this paper has been completed.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Centre of Mobile Cloud ComputingMaulana Abul Kalam Azad University of Technology, West BengalKolkataIndia
  2. 2.Department of PhysicsUniversity of Western AustraliaCrawleyAustralia

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