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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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A skipping spectrum sensing scheme based on deep reinforcement learning for transform domain communication systems.

Ce Li1, Yanhua Wu2, Rangang Zhu3

  • 1College of Electronic Engineering, National University of Defense Technology, Hefei, 230000, China. lice22@nudt.edu.cn.

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|December 29, 2024
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Summary

This study introduces a new method for Transform Domain Communication Systems (TDCS) to improve spectrum sensing efficiency. The approach enhances adaptability and performance by balancing sensing costs and spectrum utilization.

Keywords:
Double Deep Recurrent Q-NetworkDynamic spectrum accessPartially observable Markov decision processSpectrum sensingTransform domain communication system

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Area of Science:

  • Wireless Communication
  • Signal Processing
  • Machine Learning

Background:

  • Spectrum sensing is crucial for Transform Domain Communication Systems (TDCS).
  • Traditional fixed sub-band selection and periodic sensing in TDCS lead to performance degradation and inefficiency.
  • Existing models often oversimplify the dynamic spectrum environment.

Purpose of the Study:

  • To develop an adaptive spectrum sensing strategy for TDCS that addresses limitations of traditional methods.
  • To model the spectrum sensing problem as a Partially Observable Markov Decision Process (POMDP) considering incomplete spectrum state information.
  • To enhance TDCS transmission efficiency and reduce sensing overhead.

Main Methods:

  • Modeling spectrum sensing as a Partially Observable Markov Decision Process (POMDP).
  • Developing a DDRQN-BandShift strategy combining Double Deep Q-Network (DDQN) and Deep Recurrent Q-Network (DRQN) to address Q-value overestimation.
  • Incorporating distinct termination conditions for sub-band selection and skipped time slots.
  • Assigning weights to balance sensing overhead and spectrum utilization.

Main Results:

  • The proposed POMDP model accurately reflects the TDCS device's limited observation capabilities.
  • The DDRQN-BandShift strategy effectively mitigates Q-value overestimation issues.
  • Significant improvements in TDCS transmission efficiency were observed.
  • Substantial reductions in spectrum sensing costs were achieved.

Conclusions:

  • The novel POMDP-based approach and DDRQN-BandShift strategy offer a more realistic and effective solution for spectrum sensing in TDCS.
  • The proposed method enhances system adaptability and performance by optimizing the trade-off between sensing overhead and spectrum utilization.
  • This research contributes to more efficient and reliable wireless communication systems.