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A Deep Learning Approach for Maximum Activity Links in D2D Communications.

Bocheng Yu1, Xingjun Zhang1, Francesco Palmieri2

  • 1School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China.

Sensors (Basel, Switzerland)
|July 7, 2019
PubMed
Summary
This summary is machine-generated.

Device-to-device (D2D) communication reduces mobile network load by enabling direct content sharing. A deep neural network (DNN) optimizes D2D link scheduling, significantly cutting computation time while maintaining accuracy.

Keywords:
D2D communicationsdeep learninginteger programminglink activationwireless networks

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Area of Science:

  • Wireless communication networks
  • Mobile computing
  • Network optimization

Background:

  • Mobile cellular networks face exponential traffic growth, straining Long Term Evolution (LTE) eNode B (eNB) components.
  • Device-to-device (D2D) communication offers a solution by enabling direct content sharing among nearby users, reducing redundant downloads and eNB load.

Purpose of the Study:

  • To maximize simultaneous D2D transmissions for improved spectrum and energy efficiency and reduced transmission delay.
  • To address challenges of interference between D2D and cellular communications and guarantee Quality of Service (QoS).

Main Methods:

  • Formulating the D2D link scheduling problem as an integer linear programming problem (NP-hard).
  • Proposing a deep learning approach using a deep neural network (DNN) to solve the link scheduling problem.
  • Utilizing network features extracted via deep learning for optimizing D2D link schedules.

Main Results:

  • The DNN-based algorithm effectively solves the D2D link scheduling problem.
  • Achieved significant reductions in computation time, up to 90%, for delay-sensitive operations.
  • The method found satisfactory D2D link scheduling solutions with minimal impact on accuracy.

Conclusions:

  • Deep learning offers an efficient solution for optimizing D2D link scheduling in mobile cellular networks.
  • The DNN approach significantly reduces computational overhead, making it suitable for real-time applications.
  • This method enhances network efficiency and performance by maximizing D2D transmissions while managing interference and QoS.