Scalable User Rate and Energy-Efficiency Optimization in Cell-Free Massive MIMO

H. D. Tuan, A. A. Nasir, H. Q. Ngo, E. Dutkiewicz, H. V. Poor

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


This paper considers a cell-free massive multiple-input multiple-output network (cfm-MIMO) with a massive number of access points (APs) distributed across an area to deliver information to multiple users. Based on only local channel state information, conjugate beamforming is used under both proper and improper Gaussian signalings. To accomplish the mission of cfm-MIMO in providing fair service to all users, the problem of power allocation to maximize the geometric mean (GM) of users' rates (GM-rate) is considered. A new scalable algorithm, which iterates linear-complex closed-form expressions and thus is practical regardless of the scale of the network, is developed for its solution. The problem of quality-of-service (QoS) aware network energy-efficiency is also addressed via maximizing the ratio of the GM-rate and the total power consumption, which is also addressed by iterating linear-complex closed-form expressions. Intensive simulations are provided to demonstrate the ability of the GM-rate based optimization to achieve multiple targets such as a uniform QoS, a good sum rate, and a fair power allocation to the APs.

Original languageEnglish (US)
Pages (from-to)6050-6065
Number of pages16
JournalIEEE Transactions on Communications
Issue number9
StatePublished - Sep 1 2022

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


  • Cell-free massive MIMO (cfm-MIMO)
  • conjugate beamforming (CB)
  • energy efficiency
  • geometric mean
  • nonconvex optimization
  • scalable algorithms


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