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Throughput Maximization Using Deep Complex Networks for Industrial Internet of Things.

Danfeng Sun1, Yanlong Xi1, Abdullah Yaqot2

  • 1Key Laboratory of Discrete Industrial Internet of Things of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China.

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|January 21, 2023
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Summary

This study introduces a novel complex-valued deep learning network for dynamic power allocation in Industrial Internet of Things systems. The proposed method enhances spectral efficiency by effectively utilizing complex-valued channel data, outperforming existing techniques.

Keywords:
IIoTdeep complex networksspectral efficiency optimization

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Area of Science:

  • Wireless Communication
  • Deep Learning
  • Resource Allocation

Background:

  • High-density Industrial Internet of Things (IIoT) requires robust support for numerous devices and massive data transmission.
  • Multiple-Input Multiple-Output (MIMO) cognitive systems are crucial for maintaining high throughput in IIoT networks.
  • Spectral efficiency (SE) optimization via dynamic power allocation is key to enhancing network throughput, especially with varying channel quality.

Purpose of the Study:

  • To address the limitations of real-valued deep learning methods in power allocation by leveraging the full capacity of complex-valued channel data.
  • To propose a novel complex-valued power allocation network (AttCVNN) incorporating attention mechanisms for improved performance in IIoT cognitive systems.
  • To enhance spectral efficiency and network throughput in high-density IIoT environments.

Main Methods:

  • Development of a complex-valued power allocation network (AttCVNN) utilizing both cross-channel and in-channel attention mechanisms.
  • The cross-channel attention mechanism models interactions between cognitive and primary users (inter-network).
  • The in-channel attention mechanism models interactions among cognitive users (intra-network).

Main Results:

  • The proposed AttCVNN significantly outperforms traditional Equal Power Allocation Method (EPM) and real-valued/complex-valued Fully Connected Networks (FNN, CVFNN).
  • AttCVNN demonstrates superior performance in spectral efficiency optimization for high-density IIoT cognitive systems.
  • The network exhibits a faster convergence rate during training compared to real-valued Convolutional Neural Networks (AttCNN).

Conclusions:

  • Complex-valued deep learning, particularly with attention mechanisms, offers a more effective approach to power allocation in cognitive radio systems.
  • The AttCVNN architecture successfully addresses the non-convexity issues in resource allocation caused by channel multi-path and inter-user interference.
  • This research provides a promising direction for optimizing spectral efficiency in future high-density IIoT networks.