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Throughput enhancement in a cognitive radio network using a reinforcement learning method

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Abstract

As the demand for higher data rate is exponentially growing, spectral efficiency improving methods can be adopted in recent day’s wireless communication systems. If the cognitive radio network can forecast the channel to be sensed, instead of sensing all channels sequentially, then reasonable increase in throughput can be achieved. In this research, we forecast not only the channel that can be sensed, but we also predict how long the channel remains usable for secondary users. This process can reduce the sensing time. We use a deep deterministic policy gradient method to optimally select the channel and also the duration for sensing. Doing this way, we can minimise the energy spent on sensing and make the cognitive radio energy efficient. Through simulation, we show that the number of operations invested on sensing is minimised. We also show in our result that the higher throughput is achieved.

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Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study

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Authors and Affiliations

  1. Vellore Institute of Technology, Vellore, India

    J. Christopher Clement, K. C. Sriharipriya, P. Prakasam & Chandra Sekaran D S

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  1. J. Christopher Clement
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  2. K. C. Sriharipriya
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  3. P. Prakasam
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  4. Chandra Sekaran D S
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Correspondence to J. Christopher Clement.

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    The authors did not receive support from any organization for the submitted work.

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    No funding was received to assist with the preparation of this manuscript.

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    No funding was received for conducting this study.

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    No funds, grants, or other support was received.

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Clement, J.C., Sriharipriya, K.C., Prakasam, P. et al. Throughput enhancement in a cognitive radio network using a reinforcement learning method. Multimed Tools Appl 83, 1165–1187 (2024). https://doi.org/10.1007/s11042-023-15432-8

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  • DOI: https://doi.org/10.1007/s11042-023-15432-8

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