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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Machine Learning-Inspired Hybrid Precoding for mmWave MU-MIMO Systems with Domestic Switch Network.

Xiang Li1, Yang Huang1, Wei Heng1

  • 1National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China.

Sensors (Basel, Switzerland)
|April 30, 2021
PubMed
Summary

This study introduces a novel hybrid precoding structure for MU-MIMO systems, reducing hardware costs and complexity using domestic connections. The new design enhances energy efficiency and offers significant advantages in various configurations.

Keywords:
MU-MIMOblock diagonalizationcross-entropyhybrid precodingmmWave

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Area of Science:

  • Electrical Engineering
  • Wireless Communications
  • Signal Processing

Background:

  • Hybrid precoding in MU-MIMO systems offers reduced hardware costs but requires complex analog networks.
  • Existing hybrid precoding structures face challenges with intricate RF chain and antenna interconnections.

Purpose of the Study:

  • To develop a novel hybrid precoding structure for downlink transmission in MU-MIMO systems.
  • To reduce hardware complexity and enhance energy efficiency through a compact RF structure.
  • To optimize RF precoder design and active antenna count using advanced algorithms.

Main Methods:

  • A new hybrid precoding structure utilizing domestic connections between RF chains and antennas.
  • Implementation of fixed-degree phase shifters and on-off switches, eliminating the need for RF adders.
  • Application of baseband zero forcing and block diagonalization for interference cancellation.
  • RF precoder design via cross-entropy minimization and energy efficiency optimization using fractional programming and the Dinkelbach method.

Main Results:

  • The proposed structure significantly reduces hardware complexity compared to traditional global connection methods.
  • The algorithms effectively cancel interference for both single-antenna and multiple-antenna users.
  • Optimized energy efficiency and active antenna configurations were achieved.
  • Simulation results demonstrate substantial performance advantages across different system configurations.

Conclusions:

  • The novel hybrid precoding structure offers a cost-effective and efficient solution for MU-MIMO systems.
  • The proposed design simplifies the analog network while maintaining high performance.
  • The integration of machine learning and optimization techniques provides a robust framework for precoder design and energy efficiency.