Deep Learning of Near Field Beam Focusing in Terahertz Wideband Massive MIMO Systems

Yu Zhang, Ahmed Alkhateeb

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Employing large antenna arrays and utilizing large bandwidth have the potential of bringing very high data rates to future wireless communication systems. However, this brings the system into the near-field regime and also makes the conventional transceiver architectures suffer from the wideband effects. To address these problems, in this letter, we propose a low-complexity frequency-aware beamforming solution that is designed for hybrid time-delay and phase-shifter based RF architectures. To reduce the complexity, the joint design problem of the time delays and phase shifts is decomposed into two subproblems, where a signal model inspired online learning framework is proposed to learn the shifts of the quantized analog phase shifters, and a low-complexity geometry-assisted method is leveraged to configure the delay settings of the time-delay units. Simulation results highlight the efficacy of the proposed solution in achieving robust performance across a wide frequency range for large antenna array systems.

Original languageEnglish (US)
Pages (from-to)535-539
Number of pages5
JournalIEEE Wireless Communications Letters
Issue number3
StatePublished - Mar 1 2023


  • Near field communication
  • Terahertz communications
  • deep learning
  • massive MIMO

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering


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