On the convergence of max-min fairness power allocation in massive MIMO systems

Rafael S. Chaves*, Ediz Cetin, Markus V. S. Lima, Wallace A. Martins

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Power allocation techniques, among which the max-min fairness power allocation (MMFPA) is one of the most widely used, are essential to guarantee good data throughput for all users in a cell. Recently, an efficient MMFPA algorithm for massive multiple-input multiple-output (MIMO) systems has been proposed. However, this algorithm is susceptible to the initial search interval employed by the underlying bisection search. Even if the optimal point belongs to the initial search interval, this algorithm may fail to converge to such a point. In this letter, we use the Perron-Frobenius theory to explain this issue and provide search intervals that guarantee convergence to the optimal point. Furthermore, we propose the bound test procedure as an efficient way of initializing the search interval. Simulation results corroborate our findings.

Original languageEnglish
Pages (from-to)2873-2877
Number of pages5
JournalIEEE Communications Letters
Issue number12
Early online date7 Aug 2020
Publication statusPublished - Dec 2020


  • MIMO communication
  • Signal to noise ratio
  • Convergence
  • Eigenvalues and eigenfunctions
  • Resource management
  • Interference
  • Downlink
  • Massive MIMO
  • power control
  • power allocation
  • max-min fairness


Dive into the research topics of 'On the convergence of max-min fairness power allocation in massive MIMO systems'. Together they form a unique fingerprint.

Cite this