Adaptive Hybrid Beamforming with Massive Phased Arrays in Macro-Cellular Networks

Shahram Shahsavari, S. Amir Hosseini, Chris Ng, Elza Erkip

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Hybrid beamforming via large antenna arrays has a great potential for increasing data rate in cellular networks by delivering multiple data streams simultaneously. In this paper, several beamforming design algorithms are proposed based on long-term channel information in macro-cellular environments where the base station is equipped with a massive phased array under per-antenna power constraint. Using an adaptive scheme, beamforming vectors are updated whenever the long-term channel information changes. First, the problem is studied when the base station has a single RF chain (single-beam scenario). Semi-definite relaxation (SDR) with randomization is used to solve the problem. As a second approach, a low-complexity heuristic beam composition algorithm is proposed which performs very close to the upper-bound obtained by SDR. Next, the problem is studied for a generic number of RF chains (multi-beam scenario) where the Gradient Projection method is used to obtain local solutions. Numerical results reveal that using massive antenna arrays with optimized beamforming vectors can lead to five-fold network throughput improvement over systems with conventional antennas.

Original languageEnglish (US)
Title of host publicationIEEE 5G World Forum, 5GWF 2018 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538649824
StatePublished - Oct 31 2018
Event1st IEEE 5G World Forum, 5GWF 2018 - Santa Clara, United States
Duration: Jul 9 2018Jul 11 2018

Publication series

NameIEEE 5G World Forum, 5GWF 2018 - Conference Proceedings


Other1st IEEE 5G World Forum, 5GWF 2018
Country/TerritoryUnited States
CitySanta Clara

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications
  • Control and Optimization


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