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Adaptive Cooperation for Energy Harvesting Systems

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Abstract

In this paper, we propose an Adaptive Cooperation (AC) protocol for energy harvesting systems. AC chooses between Cooperative Communications (CC) and Non Cooperative Communications (NCC), the protocol that maximizes the instantaneous or average throughput. Both the source and relay harvest energy from Radio Frequency (RF) signal received from a Base Station (BS). The harvested energy is used to transmit data by the source and relays. The proposed AC protocol has been extended to include Adaptive Modulation and Coding (AMC) that chooses the best Modulation and Coding Scheme (MCS). We also optimize harvesting duration in order to maximize the average throughput.

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References

  1. Jun, Z., Yong, L., Xiaohu, T., & Qingchun, C. (2018). Relaying protocols for buffer-aided energy harvesting wireless cooperative networks. IET Networks Year, 7(3), 109–118.

    Article  Google Scholar 

  2. Xiuping, W., Feng, Y., & Tian, Z. (2018). The DF-AF selection relay transmission based on energy harvesting. In 2018 10th International conference on measuring technology and mechatronics automation (ICMTMA) (pp. 174–177).

  3. Nguyen, H. T., Nguyen, S. Q., & Hwang, W.-J. (2018). Outage probability of energy harvesting relay systems under unreliable backhaul connections. In 2018 2nd International conference on recent advances in signal processing, telecommunications and computing (SigTelCom) (pp. 19–23).

  4. Qiu, C., Hu, Y., & Chen, Y. (2018). Lyapunov optimized cooperative communications with stochastic energy harvesting relay. IEEE Internet of Things Journal Year, 5(2), 1323–1333.

    Article  Google Scholar 

  5. Sui, D., Hu, F., Zhou, W., Shao, M., & Chen, M. (2018). Relay selection for radio frequency energy-harvesting wireless body area network with buffer. IEEE Internet of Things Journal Year, 5(2), 1100–1107.

    Article  Google Scholar 

  6. Dung, L. T., Hoang, T. M., Tan, N. T., & Choi, S.-G. (2018). Analysis of partial relay selection in NOMA systems with RF energy harvesting. In 2018 2nd International conference on recent advances in signal processing, telecommunications and computing (SigTelCom) (pp. 13–18).

  7. Le, Q. N., Bao, V. N., & Quoc, A. B. (2018). Full-duplex distributed switch-and-stay energy harvesting selection relaying networks with imperfect CSI: Design and outage analysis. Journal of Communications and Networks, 20(1), 29–46.

    Article  Google Scholar 

  8. Gong, J., Chen, X., & Xia, M. (2018). Transmission optimization for hybrid half/full-duplex relay with energy harvesting. IEEE Transactions on Wireless Communications Year, 17(5), 3046–3058.

    Article  Google Scholar 

  9. Tang, H., Xie, X., & Chen, J. (2018). X-duplex relay with self-interference signal energy harvesting and its hybrid mode selection method. In 2018 27th Wireless and optical communication conference (WOCC) (pp. 1–6).

  10. Singh, K., Ku, M.-L., Lin, J.-C., & Ratnarajah, T. (2018). Toward optimal power control and transfer for energy harvesting amplify-and-forward relay networks. IEEE Transactions on Wireless Communications.

  11. Wu, Y., Qian, L., Huang, L., & Shen, X. (2018). Optimal relay selection and power control for energy-harvesting wireless relay networks. IEEE Transactions on Green Communications and Networking, 2(2), 471–481.

    Article  Google Scholar 

  12. Fan, R., Atapattu, S., Chen, W., Zhang, Y., & Evans, J. (2018). Throughput maximization for multi-hop decode-and-forward relay network with wireless energy harvesting. IEEE Access, 6, 24582–24595.

    Article  Google Scholar 

  13. Huang, Y., Wang, J., Zhang, P., & Wu, Q. (2018). Performance analysis of energy harvesting multi-antenna relay networks with different antenna selection schemes. IEEE Access, 6, 5654–5665.

    Article  Google Scholar 

  14. Babaei, M., Aygl, M., & Basar, E. (2018). BER analysis of dual-hop relaying with energy harvesting in Nakagami-m fading channel. IEEE Transactions on Wireless Communications, p. 1.

  15. Kalluri, T., Peer, M., Bohara, V. A., da Costa, D. B., & Dias, U. S. (2018). Cooperative spectrum sharing-based relaying protocols with wireless energy harvesting cognitive user. IET Communications Journals and Magazines, 12(7), 838–847.

    Article  Google Scholar 

  16. Xie, D., Lai, X., Lei, X., & Fan, L. (2018). Cognitive multiuser energy harvesting decode-and-forward relaying system with direct links. IEEE Access, 6, 5596–5606.

    Article  Google Scholar 

  17. Yan, Z., Chen, S., Zhang, X., & Liu, H. -L. (2018). Outage performance analysis of wireless energy harvesting relay-assisted random underlay cognitive networks. IEEE Internet of Things Journal, pp. 23406–23419.

  18. Boddapati, H. K., Bhatnagar, M. R., & Prakriya, S. (2018). Performance of incremental relaying protocols for cooperative multihop CRNs. IEEE Transactions on Vehicular Technology, 67(7).

  19. Bapatla, D., & Prakriya, S. (2019). Performance of incremental relaying with an energy-buffer aided relay. In 2019 IEEE 89th vehicular technology conference (VTC2019-Spring).

  20. Bapatla, D., & Prakriya, S. (2019). Performance of energy-buffer aided incremental relaying in cooperative networks. IEEE Transactions on Wireless Communications, 18(7).

  21. Liu, D., Zhao, M., & Zhou, W. (2018). Energy efficiency optimization in energy harvesting incremental relay system. In 2018 10th international conference on wireless communications and signal processing (WCSP).

  22. Zheng, Q., Yang, M., Yang, J., Zhang, Q., & Zhang, X. (2018). Improvement of generalization ability of deep CNN via implicit regularization in two-stage training process. IEEE Access.

  23. Zheng, Q., Xinyub, T., Jiang, N., & Yang, M. (2019). Layer-wise learning based stochastic gradient descent method for the optimization of deep convolutional neural network. Journal of Intelligent and Fuzzy Systems, 37(4), 5641–5654.

    Article  Google Scholar 

  24. Zheng, Q., Tian, X., Yang, M., Wu, Y., & Su, H. (2020). PAC-Bayesian framework based drop-path method for 2D discriminative convolutional network pruning. Multidimensional Systems and Signal Processing, 31(3), 793–827.

    Article  MathSciNet  Google Scholar 

  25. Zheng, Q., Yang, M., Tian, X., Jiang N., & Wang, D. (2020). A full stage data augmentation method in deep convolutional neural network for natural image classification. Discrete Dynamics in Nature and Society, pp. 1–11. https://doi.org/10.1155/2020/4706576.

  26. Zheng, Q., Zhao, P., Li, Y., Hongjun, W., & Yang, Y. (2020). Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification. Neural Processing and Applications. https://doi.org/10.1007/s00521-020-05514-1.

    Article  Google Scholar 

  27. Lei, H., Xu, M., Ansari, I. S., Pan, G., Qaraqe, K. A., & Alouini, M.-S. (2017). On secure underlay MIMO cognitive radio networks with energy harvesting and transmit antenna selection. IEEE Transactions on Green Communications and Networking ( Early Access) , pp. 192–203.

  28. Proakis, J. (2007). Digital communications. Mac Graw-Hill.

  29. Hasna, M. O., & Alouini, M.-S. (2004). Harmonic mean and end-to-end performance of transmission systems with relays. IEEE Transactions on Communications, 52(1), 130–135.

    Article  Google Scholar 

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Funding

No Funding received for this paper. It is the contribution of Prof. Nadhir Ben Halima and Prof. Hatem Boujemaa.

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Authors and Affiliations

  1. Mediterranean Institute of Technology, South Mediterranean University, Tunis, Tunisia

    Nadhir Ben Halima

  2. COSIM Lab, Tunis, Tunisia

    Hatem Boujemâa

Authors
  1. Nadhir Ben Halima
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  2. Hatem Boujemâa
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Correspondence to Nadhir Ben Halima.

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Ben Halima, N., Boujemâa, H. Adaptive Cooperation for Energy Harvesting Systems. Wireless Pers Commun 122, 3499–3512 (2022). https://doi.org/10.1007/s11277-021-09097-5

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  • DOI: https://doi.org/10.1007/s11277-021-09097-5

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