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Sum Rate Maximization in STAR-RIS-Assisted Uplink AmBC-CR Networks with NOMA

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Abstract

In this paper, we investigate a network of backscatter devices (BDs) empowered with cognitive radio (CR) that uses non-orthogonal multiple access (NOMA) to communicate with the backscatter receiver. The presence of dual fading in backscatter networks weakens the strength of the backscatter link, thus impacting achievable performance. To address this issue, reconfigurable intelligent surfaces (RIS) can be used in such networks. However, the conventional RIS provides half-space coverage. Therefore, the use of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) can offer services to BDs in a full-space. Consequently, we jointly optimize the reflection/transmission (R/T) phase shift matrices, the R/T periods at the STAR-RIS, and the power reflection coefficients (PRCs) at the BDs to maximize the sum rate in the STAR-RIS-assisted NOMA-enabled AmBC-CR (SRIS-NeAmCR) networks, subject to the quality of service (QoS) constraints of individual BDs and the primary receiver. We introduce an approach to solve the resulting non-convex optimization problem by employing semidefinite relaxation and rank-one approximation, seeking a suboptimal solution through an alternating optimization algorithm. The simulation results show that the proposed scheme works better than the baseline schemes like NOMA without STAR-RIS, NOMA with random phases, orthogonal multiple access (OMA) without STAR-RIS, and OMA with optimal phases.

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Notes

  1. In the NeAmCR system, a network of BDs communicates with the BR. The BDs receive signals from the PT and reflect them towards the BR. In this way, the signal passes through two fading paths (PT to BD and BD to BR), which significantly reduces the received signal strength and is called the double fading effect[22].

  2. In this work, passive STAR-RIS is employed, which has limited gain but is power efficient. Suppose a high gain is desirable and the power budget is not a constraint. In that case, active STAR-RIS may be a good choice [36].

References

  1. Xu, L. D., He, W., & Li, S. (2014). Internet of things in industries: A survey. IEEE Transactions on Industrial Informatics, 10(4), 2233–2243.

    Article  Google Scholar 

  2. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of things: A survey on Enabling technologies, protocols, and applications. IEEE Communications Surveys and Tutorials, 17(4), 2347–2376.

    Article  Google Scholar 

  3. Chettri, L., & Bera, R. (2020). A comprehensive survey on internet of things (IoT) toward 5G wireless systems. IEEE Internet of Things Journal, 7(1), 16–32.

    Article  Google Scholar 

  4. Liu, Y., Yi, W., Ding, Z., Liu, X., Dobre, O. A., & Al-Dhahir, N. (2022). Developing NOMA to next generation multiple access: Future vision and research opportunities. IEEE Wireless Communications, 29(6), 120–127.

    Article  Google Scholar 

  5. Dai, L., Wang, B., Yuan, Y., Han, S., Chih-lin, I., & Wang, Z. (2015). Non-orthogonal multiple access for 5G: Solutions, challenges, opportunities, and future research trends. IEEE Communications Magazine, 53(9), 74–81.

    Article  Google Scholar 

  6. Chen, Z., Ding, Z., Dai, X., & Zhang, R. (2017). An optimization perspective of the superiority of NOMA compared to conventional OMA. IEEE Transactions on Signal Processing, 65(19), 5191–5202.

    Article  MathSciNet  Google Scholar 

  7. Singh, S. K., & Gandhi, A. S. (2019). Preamble based timing offset estimation and correction in OFDM assisted massive mimo systems in the presence of inter-user-interference. Wireless Personal Communications, 106, 1325–1338.

    Article  Google Scholar 

  8. Singh, S. K., Rathkanthiwar, A. P., & Gandhi, A. S. (2017). New algorithm for time and frequency synchronization in MIMO-OFDM systems. Wireless Personal Communications, 96, 3283–3295.

    Article  Google Scholar 

  9. Yucek, T., & Arslan, H. (2009). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys & Tutorials, 11(1), 116–130.

    Article  Google Scholar 

  10. Wang, B., & Liu, K. R. (2011). Advances in cognitive radio networks: A survey. IEEE Journal of Selected Topics in Signal Processing, 5(1), 5–23.

    Article  Google Scholar 

  11. Zhou, F., Wu, Y., Liang, Y.-C., Li, Z., Wang, Y., & Wong, K.-K. (2018). State of the art, taxonomy, and open issues on cognitive radio networks with NOMA. IEEE Wireless Communications, 25(2), 100–108.

    Article  Google Scholar 

  12. Salameh, H. B., Abdel-Razeq, S., & Al-Obiedollah, H. (2023). Integration of cognitive radio technology in NOMA-based B5G networks: State of the art, challenges, and enabling technologies. IEEE Access, 11, 12949–12962.

    Article  Google Scholar 

  13. Liu, X., Lin, B., Zhou, M., & Jia, M. (2021). NOMA-based cognitive spectrum access for 5G-enabled internet of things. IEEE Network, 35(5), 290–297.

    Article  Google Scholar 

  14. Zhang, Y., Yang, Q., Zheng, T.-X., Wang, H.-M., Ju, Y., & Meng, Y. (2016). Energy efficiency optimization in cognitive radio inspired non-orthogonal multiple access. In 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) pp. 1–6.

  15. Liu, X., Wang, Y., Liu, S., & Meng, J. (2018). Spectrum resource optimization for NOMA-based cognitive radio in 5G communications. IEEE Access, 6, 24904–24911.

    Article  Google Scholar 

  16. Liu, V., Parks, A., Talla, V., Gollakota, S., Wetherall, D., & Smith, J. R. (2013). Ambient backscatter: Wireless communication out of thin air. ACM SIGCOMM computer communication review, 43(4), 39–50.

    Article  Google Scholar 

  17. Van Huynh, N., Hoang, D. T., Lu, X., Niyato, D., Wang, P., & Kim, D. I. (2018). Ambient backscatter communications: A contemporary survey. IEEE Communications Surveys & Tutorials, 20(4), 2889–2922.

    Article  Google Scholar 

  18. Le, C.-B., Do, D.-T., Silva, A., Khan, W. U., Khalid, W., Yu, H., & Nguyen, N. D. (2022). Joint design of improved spectrum and energy efficiency with backscatter NOMA for IoT. IEEE Access, 10, 7504–7519.

    Article  Google Scholar 

  19. Zhuang, Y., Li, X., Ji, H., Zhang, H., & Leung, V. C. M. (2020). Optimal resource allocation for RF-powered underlay cognitive radio networks with ambient backscatter communication. IEEE Transactions on Vehicular Technology, 69(12), 15216–15228.

    Article  Google Scholar 

  20. Wang, J., Ye, H.-T., Kang, X., Sun, S., & Liang, Y.-C. (2020). Cognitive backscatter NOMA networks with multi-slot energy causality. IEEE Communications Letters, 24(12), 2854–2858.

    Article  Google Scholar 

  21. Guo, S., Zhao, X., & Zhang, W. (2023). Throughput maximization for RF powered cognitive noma networks with backscatter communication by deep reinforcement learning. IEEE Transactions on Wireless Communications. https://doi.org/10.1109/TWC.2023.3337409

    Article  Google Scholar 

  22. Zuo, J., Liu, Y., Yang, L., Song, L., & Liang, Y.-C. (2021). Reconfigurable intelligent surface enhanced NOMA assisted backscatter communication system. IEEE Transactions on Vehicular Technology, 70(7), 7261–7266.

    Article  Google Scholar 

  23. Wu, Q., & Zhang, R. (2020). towards smart and reconfigurable environment: Intelligent reflecting surface aided wireless network. IEEE Communications Magazine, 58(1), 106–112.

    Article  Google Scholar 

  24. Wu, Qingqing, & Zhang, Rui. (2019). Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming. IEEE Transactions on Wireless Communications, 18(11), 5394–5409.

    Article  Google Scholar 

  25. Thenua, R. K., & Gandhi, A. S. (2023). Joint beamforming design for RIS-aided AmBC-enabled MISO NOMA networks. Wireless Personal Communications, 133(1), 373–393.

    Article  Google Scholar 

  26. Jian, M., Alexandropoulos, G. C., Basar, E., Huang, C., Liu, R., Liu, Y., & Yuen, C. (2022). Reconfigurable intelligent surfaces for wireless communications: Overview of hardware designs, channel models, and estimation techniques. Intelligent and Converged Networks, 3(1), 1–32.

    Article  Google Scholar 

  27. Zhi, K., Pan, C., Ren, H., & Wang, K. (2022). Power scaling law analysis and phase shift optimization of RIS-Aided massive MIMO systems With statistical CSI. IEEE Transactions on Communications, 70(5), 3558–3574.

    Article  Google Scholar 

  28. Zhi, K., Pan, C., Ren, H., Wang, K., Elkashlan, M., Renzo, M. D., Schober, R., Poor, H. V., Wang, J., & Hanzo, L. (2023). Two-timescale design for reconfigurable intelligent surface-aided massive MIMO systems with imperfect CSI. IEEE Transactions on Information Theory, 69(5), 3001–3033.

    Article  MathSciNet  Google Scholar 

  29. Basharat, S., Hassan, S. A., Pervaiz, H., Mahmood, A., Ding, Z., & Gidlund, M. (2021). Reconfigurable intelligent surfaces: Potentials, applications, and challenges for 6U wireless networks. IEEE Wireless Communications, 28(6), 184–191.

    Article  Google Scholar 

  30. Björnson, Emil, Özdogan, Özgecan., & Larsson, Erik G. (2020). Intelligent reflecting surface versus decode-and-forward: How large surfaces are needed to beat relaying? IEEE Wireless Communications Letters, 9(2), 244–248.

    Article  Google Scholar 

  31. Du, W., Chu, Z., Chen, G., Xiao, P., Xiao, Y., Wu, X., & Hao, W. (2023). STAR-RIS assisted wireless powered IoT networks. IEEE Transactions on Vehicular Technology, 72(8), 10644–10658.

    Article  Google Scholar 

  32. Liu, Y., Mu, X., Xu, J., Schober, R., Hao, Y., Poor, H. V., & Hanzo, L. (2021). STAR: simultaneous transmission and reflection for 360 coverage by intelligent surfaces. IEEE Wireless Communications, 28(6), 102–109.

    Article  Google Scholar 

  33. Mu, X., Liu, Y., Guo, L., Lin, J., & Schober, R. (2022). Simultaneously transmitting and reflecting (STAR) RIS aided wireless communications. IEEE Transactions on Wireless Communications, 21(5), 3083–3098.

    Article  Google Scholar 

  34. Huang, A., Mu, X., & Guo, L. (2023). STAR-RIS assisted downlink active and uplink backscatter communications with NOMA. IEEE Transactions on Vehicular Technology, 72(11), 14516–14530.

    Google Scholar 

  35. Katwe, M., Singh, K., Clerckx, B., & Li, C.-P. (2023). Improved spectral efficiency in STAR-RIS aided uplink communication using rate splitting multiple access. IEEE Transactions on Wireless Communications, 22(8), 5365–5382.

    Article  Google Scholar 

  36. Zhi, K., Pan, C., Ren, H., Chai, K. K., & Elkashlan, M. (2022). Active RIS versus passive RIS: Which is superior with the same power budget? IEEE Communications Letters, 26(5), 1150–1154.

    Article  Google Scholar 

  37. Ma, H., Wang, H., Li, H., & Feng, Y. (2022). Transmit power minimization for STAR-RIS-empowered uplink NOMA system. IEEE Wireless Communications Letters, 11(11), 2430–2434.

    Article  Google Scholar 

  38. Zuo, J., Liu, Y., Ding, Z., Song, L., & Poor, H. V. (2023). Joint design for simultaneously transmitting and reflecting (STAR) RIS assisted NOMA systems. IEEE Transactions on Wireless Communications, 22(1), 611–626.

    Article  Google Scholar 

  39. Guo, K., Liu, R., Alazab, M., Jhaveri, R. H., Li, X., & Zhu, M. (2023). STAR-RIS-Empowered Cognitive Non-Terrestrial Vehicle Network With NOMA. IEEE Transactions on Intelligent Vehicles, 8(6), 3735–3749.

    Article  Google Scholar 

  40. Xiao, S., Guo, H., & Liang, Y.-C. (2019). Resource allocation for full-duplex-enabled cognitive backscatter networks. IEEE Transactions on Wireless Communications, 18(6), 3222–3235.

    Article  Google Scholar 

  41. Zeng, M., Li, X., Li, G., Hao, W., & Dobre, O. A. (2021). Sum rate maximization for IRS-assisted uplink NOMA. IEEE Communications Letters, 25(1), 234–238.

    Article  Google Scholar 

  42. Grant, M., & Boyd, S. (2014). CVX: MATLAB Software for Disciplined Convex Programming.

  43. Li, Z., Chen, W., Wu, Q., Wang, K., & Li, J. (2022). Joint beamforming design and power splitting optimization in IRS-assisted SWIPT NOMA networks. IEEE Transactions on Wireless Communications, 21(3), 2019–2033.

    Article  Google Scholar 

  44. Luo, Z.-Q., Ma, W.-K., So, A.M.-C., Ye, Y., & Zhang, S. (2010). Semidefinite relaxation of quadratic optimization problems. IEEE Signal Processing Magazine, 27(3), 20–34.

    Article  Google Scholar 

  45. Zhuang, Y., Li, X., Ji, H., & Zhang, H. (2022). Exploiting intelligent reflecting surface for energy efficiency in ambient backscatter communication-enabled NOMA networks. IEEE Transactions on Green Communications and Networking, 6(1), 163–174.

    Article  Google Scholar 

  46. Wu, Y., Zhou, F., Wu, W., Wu, Q., Hu, R. Q., & Wong, K.-K. (2022). Multi-objective optimization for spectrum and energy efficiency tradeoff in IRS-assisted CRNs with NOMA. IEEE Transactions on Wireless Communications, 21(8), 6627–6642.

    Article  Google Scholar 

  47. Tiwari, S., Sengupta, J., & Bokde, N. D. (2023). Analysis of individual user data rate in a TDMA-RIS-NOMA downlink system: beyond the limitation of conventional NOMA. Electronics, 12(3), 618.

    Article  Google Scholar 

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  1. Department of Electronics and Communication Engineering, VNIT Nagpur, Nagpur, Maharashtra, India

    Raj Kumar Thenua & Abhay S. Gandhi

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  1. Raj Kumar Thenua
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“Raj Kumar Thenua contributed to model design, implementation, and manuscript preparation, and Abhay S. Gandhi provided guidance for the proper interpretation of the results."

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Thenua, R.K., Gandhi, A.S. Sum Rate Maximization in STAR-RIS-Assisted Uplink AmBC-CR Networks with NOMA. Wireless Pers Commun 136, 2419–2441 (2024). https://doi.org/10.1007/s11277-024-11392-w

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