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Analysis of user pairing non-orthogonal multiple access network using deep Q-network algorithm for defense applications

Author

Listed:
  • Shankar Ravi
  • Gopal Ramchandra Kulkarni
  • Samrat Ray
  • Malladi Ravisankar
  • V Gokula krishnan
  • D S K Chakravarthy

Abstract

Non-orthogonal multiple access (NOMA) networks play an important role in defense communication scenarios. Deep learning (DL)-based solutions are being considered as viable ways to solve the issues in fifth-generation (5G) and beyond 5G (B5G) wireless networks, since they can provide a more realistic solution in the real-world wireless environment. In this work, we consider the deep Q-Network (DQN) algorithm-based user pairing downlink (D/L) NOMA network. We have applied the convex optimization (CO) technique and optimized the sum rate of all the wireless users. First, the near-far (N-F) pairing and near-near and far-far (N-N and F-F) pairing strategies are investigated for the multiple numbers of users, and a closed-form (CF) expression of the achievable rate is derived. After that, the optimal power allocation (OPA) factors are derived using the CO technique. Through simulations, it has been demonstrated that the DQN algorithms perform much better than the deep reinforcement learning (DRL) and conventional orthogonal frequency-division multiple access (OFDMA) schemes. The sum-rate performance significantly increases with OPA factors. The simulation results are in close agreement with the analytical results.

Suggested Citation

  • Shankar Ravi & Gopal Ramchandra Kulkarni & Samrat Ray & Malladi Ravisankar & V Gokula krishnan & D S K Chakravarthy, 2023. "Analysis of user pairing non-orthogonal multiple access network using deep Q-network algorithm for defense applications," The Journal of Defense Modeling and Simulation, , vol. 20(3), pages 303-316, July.
    • Handle: RePEc:sae:joudef:v:20:y:2023:i:3:p:303-316
    DOI: 10.1177/15485129211072548
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    References listed on IDEAS

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    1. Xu Gao & Wenru Zeng & Yu Shen & Zhiwei Guo & Jinhui Yang & Xuhong Cheng & Qiaozhi Hua & Keping Yu, 2020. "Integrated Deep Neural Networks-Based Complex System for Urban Water Management," Complexity, Hindawi, vol. 2020, pages 1-12, November.
    2. Ming Li & Hui Li, 2020. "Application of deep neural network and deep reinforcement learning in wireless communication," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-15, July.
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