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Efficient Massive MIMO Detection for M-QAM Symbols.

Zhi Quan1, Jiyu Luo1, Hailong Zhang1

  • 1School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China.

Entropy (Basel, Switzerland)
|March 29, 2023
PubMed
Summary

This study introduces an efficient detector for massive MIMO systems, improving data detection accuracy for next-generation wireless technologies. The new method enhances performance, especially in large systems with high signal-to-noise ratios.

Keywords:
box-constrained dichotomous coordinate descentmassive MIMOnegative diagonal loading regularizationquadrature amplitude modulationsignal detection

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Area of Science:

  • Wireless communication systems
  • Signal processing
  • Information theory

Background:

  • Massive MIMO systems offer superior data rates compared to small-scale MIMO, crucial for future wireless networks.
  • Increased antenna counts in massive MIMO systems pose significant computational challenges for data detection.
  • Efficient detection algorithms are vital to harness the full potential of massive MIMO technology.

Purpose of the Study:

  • To develop a novel and computationally efficient data detection algorithm for massive MIMO systems.
  • To address the computational complexity associated with high-dimensional data detection in massive MIMO.
  • To improve the performance of M-QAM symbol detection in next-generation wireless systems.

Main Methods:

  • A novel detector is proposed, utilizing joint deregularized and box-constrained dichotomous coordinate descent (BOXDCD) with iterations.
  • Deregularization is employed to maximize solution energy.
  • Box-constraints are integrated to guide the solution towards the rectangular boundary set for M-QAM symbols.

Main Results:

  • The proposed BOXDCD detector demonstrates significant performance gains over existing detection algorithms.
  • The performance advantage of the proposed detector increases with system size (number of antennas).
  • Improved performance is also observed with higher signal-to-noise ratios.

Conclusions:

  • The developed BOXDCD detector offers a substantial improvement in performance for massive MIMO systems.
  • The algorithm effectively manages the computational complexity of data detection in large-scale wireless systems.
  • This technique represents a promising advancement for enabling next-generation wireless communication capabilities.