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Deep Learning Based Antenna Selection for MIMO SDR System.

Shida Zhong1, Haogang Feng1, Peichang Zhang1

  • 1College of Electronics and Information Engineering, Shenzhen University, Nanhai Avenue 3688, Shenzhen 518060, China.

Sensors (Basel, Switzerland)
|December 10, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning-based antenna selection (DLBAS) framework for multiple-input-multiple-output (MIMO) software-defined radio (SDR) systems. The DLBAS-aided MIMO SDR system achieves comparable real-time performance to existing methods while significantly improving channel capacity.

Keywords:
antenna selectiondeep learningdeep neural network (DNN)multiple-input multiple-output (MIMO)software defined radio (SDR)

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Area of Science:

  • Wireless Communication
  • Artificial Intelligence
  • Signal Processing

Background:

  • Multiple-Input-Multiple-Output (MIMO) systems enhance wireless communication capacity.
  • Software-Defined Radio (SDR) offers flexible and reconfigurable communication platforms.
  • Traditional antenna selection methods can be computationally intensive.

Purpose of the Study:

  • To propose and implement a novel Deep Learning Based Antenna Selection (DLBAS) framework for MIMO SDR systems.
  • To transform antenna selection from an optimization-driven to a data-driven decision-making process.
  • To enhance resource utilization in MIMO SDR platforms.

Main Methods:

  • Construction of a MIMO SDR communication platform with Time Division Duplex (TDD) uplink.
  • Development of a deep neural network (DNN) based decision server for intelligent antenna selection.
  • Implementation of a multithreading server architecture for improved resource utilization.

Main Results:

  • The DLBAS-aided MIMO SDR system demonstrated real-time performance comparable to the norm-based antenna selection (NBAS) scheme.
  • The proposed DLBAS scheme achieved up to 53% average channel capacity gain compared to MIMO systems without antenna selection.
  • The data-driven approach via DNN effectively assisted antenna selection decisions.

Conclusions:

  • The DLBAS framework provides an efficient and intelligent solution for antenna selection in MIMO SDR systems.
  • Deep learning integration significantly boosts channel capacity and system performance.
  • The multithreading server design enhances the overall efficiency of the communication platform.