Antenna allocation and pricing invirtualized massive MIMO networks via stackelberg game

Ye Liu, Mahsa Derakhshani, Saeedeh Parsaeefard, Sangarapillai Lambotharan*, Kai Kit Wong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


We study a resource allocation problem for the uplink of a virtualized massive multiple-input multiple-output system, where the antennas at the base station are priced and virtualized among the service providers (SPs). The mobile network operator (MNO) who owns the infrastructure decides the price per antenna, and a Stackelberg game is formulated for the net profit maximization of the MNO, while the minimum rate requirements of SPs are satisfied. To solve the bi-level optimization problem of the MNO, we first derive the closed-form best responses of the SPs with respect to the pricing strategies of the MNO, such that the problem of the MNO can be reduced to a single-level optimization. Then, via transformations and approximations, we cast the MNO's problem with integer constraints into a signomial geometric program (SGP), and we propose an iterative algorithm based on the successive convex approximation (SCA) to solve the SGP. Simulation results show that the proposed algorithm has performance close to the global optimum. Moreover, the interactions between the MNO and SPs in different scenarios are explored via simulations.

Original languageEnglish
Article number8382174
Pages (from-to)5220-5234
Number of pages15
JournalIEEE Transactions on Communications
Issue number11
Publication statusPublished - Nov 2018
Externally publishedYes


  • Resource allocation
  • Stackelberg games
  • antenna allocation
  • convex approximation
  • massive MIMO

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