A Stochastic Optimization Approach to Hybrid Processing in Massive MIMO Systems

Georgios K. Papageorgiou, Mathini Sellathurai, Konstantinos Ntougias, Constantinos B. Papadias

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
47 Downloads (Pure)


The high cost and energy consumption of fully digital massive multiple-input multiple-output (MIMO) systems has led to hybrid designs with fewer radio frequency (RF) chains than antennas. In this letter, we propose an efficient hybrid processing algorithm for point-to-point (P2P) massive MIMO systems that operate in either rich or poor scattering environments. The proposed scheme, i.e., hybrid processing via stochastic approximation with Gaussian smoothing (HPSAGS), alternates between a digital baseband and an analog RF precoder/combiner computation step. The method achieves state-of-the-art performance with low computational cost, which is essential for large MIMO systems.

Original languageEnglish
Pages (from-to)770-773
Number of pages4
JournalIEEE Wireless Communications Letters
Issue number6
Early online date24 Jan 2020
Publication statusPublished - Jun 2020


  • Gaussian smoothing
  • Hybrid processing
  • massive MIMO
  • millimeter waves
  • stochastic approximation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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