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Related Experiment Video

Updated: Feb 17, 2026

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
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Hybrid precoding based on matrix-adaptive method for multiuser large-scale antenna arrays.

Yongpan Feng1, Suk Chan Kim1

  • 1Electronics Engineering Department, Pusan National University, Busan, Korea.

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|December 6, 2017
PubMed
Summary
This summary is machine-generated.

This study proposes an iterative hybrid precoding algorithm for massive multiuser multiple-input single-output (MU-MISO) systems. The method approaches full digital precoding performance with reduced radio frequency chain costs and power consumption.

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Area of Science:

  • Wireless communication systems
  • Signal processing for telecommunications
  • Antenna array design

Background:

  • Massive MIMO offers high capacity but suffers from high cost and power consumption due to numerous RF chains.
  • Hybrid precoding reduces RF chains, balancing performance and efficiency.
  • Existing hybrid schemes may not fully exploit system capacity.

Purpose of the Study:

  • To develop an iterative hybrid precoding algorithm for massive MU-MISO systems.
  • To approach the capacity performance of full digital precoding.
  • To reduce the cost and power consumption associated with RF chains.

Main Methods:

  • Proposing an iterative hybrid precoding algorithm for downlink MU-MISO.
  • Utilizing zero-forcing (ZF) digital precoding on the equivalent channel for baseband gain.
  • Minimizing total power to determine the optimal RF precoder.

Main Results:

  • The proposed iterative hybrid precoding algorithm approaches the performance of conventional fully digital precoding.
  • The method achieves significant reductions in radio frequency chain requirements.
  • Low computational complexity is demonstrated compared to traditional methods.

Conclusions:

  • The proposed iterative hybrid precoding algorithm offers a viable solution for massive MU-MISO systems.
  • This approach effectively balances system capacity with reduced hardware cost and power consumption.
  • The algorithm provides near-optimal performance with practical implementation advantages.