Nothing Special   »   [go: up one dir, main page]

Skip to main content

Advertisement

Log in

Multicast Spatial Filter Beamforming with Resource Allocation Using Joint Multi-objective Optimization Approaches in Wireless Powered Communication Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Optimum transmission strategy must be adopted in radio frequency energy-harvesting networks. For the purpose the study considered various radio applications in which the nodes operate on the batteries thereby minimizing the energy consumption and consequently obtaining high throughput and satisfactory delay. This paper analysed the best model for minimizing transmission energy which reduces the total consumption of energy needed to send required number of bits. Hence the study exploited proximal gradient convex optimization algorithm and spatial filter-based beam formers for minimizing the transmission power and reducing the computational time. These minimizations might be achieved by optimizing the signal to noise ratio. In general, receiving signals radiating from a particular location and directing the signal reception or transmission seems to be a challenging task. To overcome this the proposed spatial filter-based beamforming, a signal processing technique receives signals that are radiating from specific location and also attenuate signals from different locations. Moreover, it can easily direct signal reception or transmission. The simulation results depicted that the proposed algorithm is found to be energy efficient that describes the trade-off existing between the required harvested powers. This study employed Multi-objective Hungarian algorithm for the detection of channels that have low transmission power and less computational time for efficient resource allocation. The performance evaluation of the proposed system has been validated and compared with state of art methods like Joint optimization, fixed time allocation and bisectional search. The experimental results show that the proposed system outperforms the existing systems in terms of signal to noise ratio, transmission of energy and resource allocation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data Availability

Not Applicable.

Code Availability

Not Applicable.

References

  1. Li J, Xiong K, Cao J, Yang X, Liu T (2020) Energy Efficiency in RF Energy Harvesting-Powered Distributed Antenna Systems for the Internet of Things. Sensors, 20: 4631

    Article  Google Scholar 

  2. Zhou, Z., Gao, C., Xu, C., Chen, T., Zhang, D., & Mumtaz, S. (2017). Energy-efficient stable matching for resource allocation in energy harvesting-based device-to-device communications. IEEE access, 5, 15184–15196.

    Article  Google Scholar 

  3. Lukman, S., Agajo, J., & Salihu, B. (2021). A survey of radio frequency energy harvesting techniques toward effective powering of Mobile Devices using Cockcroft Walton voltage. Multiplier, EasyChair 2516–2314.

  4. Hossain, M. A., Noor, R. M., Yau, K. L. A., Ahmedy, I., & Anjum, S. S. (2019). A survey on simultaneous wireless information and power transfer with cooperative relay and future challenges,. IEEE access, 7, 19166–19198.

    Article  Google Scholar 

  5. Srivantana T,  Maichalernnukul K. (2017). Two-way multi-antenna relaying with simultaneous wireless information and power transfer. Symmetry, 9: 42,

    Article  MathSciNet  MATH  Google Scholar 

  6. Basnayake V, Jayakody DNK, Sharma V, Sharma N, Muthuchidambaranathan P, Mabed H (2020) A new green prospective of non-orthogonal multiple access (noma) for 5G. Information, vol. 11, p. 89,

    Article  Google Scholar 

  7. Choi, K. W., Hwang, S. I., Aziz, A. A., Jang, H. H., Kim, J. S., Kang, D. S., et al. (2020). Simultaneous wireless information and power transfer (SWIPT) for internet of things: novel receiver design and experimental validation,. IEEE Internet of Things Journal, 7, 2996–3012.

    Article  Google Scholar 

  8. Sun, W., Liu, C., Qian, M., Xu, S., & Chen, Y. (2020). Downlink ergodic sum capacity maximisation for massive distributed antenna systems with SWIPT protocol. IET Communications, 15(3), 464-475

    Article  Google Scholar 

  9. Zheng, Y., Bi, S., Zhang, Y. J. A., Lin, X., & Wang, H. (2020). Joint beamforming and power control for throughput maximization in IRS-assisted MISO WPCNs,. IEEE Internet of Things Journal, 8, 8399–8410.

    Article  Google Scholar 

  10. Khani, M., Alizadeh, M., Hoydis, J., & Fleming, P. (2020). Adaptive neural signal detection for massive MIMO,. IEEE Transactions on Wireless Communications, 19, 5635–5648.

    Article  Google Scholar 

  11. Briantoro, H., Funabiki, N., Kuribayashi, M., Munene, K. I., Sudibyo, R. W., Islam, M. M., et al. (2020). Transmission power optimization of concurrently communicating two access points in wireless local area network,. International Journal of Mobile Computing and Multimedia Communications (IJMCMC), 11, 1–25.

    Article  Google Scholar 

  12. Samanta, A., & Misra, S. (2017). Energy-efficient and distributed network management cost minimization in opportunistic wireless body area networks,. IEEE Transactions on Mobile Computing, 17, 376–389.

    Article  Google Scholar 

  13. Nalband, A. H., Sarvagya, M., & Ahmed, M. R. (2021). Spectral Efficient Beamforming for mmWave MISO Systems using Deep Learning Techniques. Arabian Journal for Science and Engineering, 46, 9783-9795

    Article  Google Scholar 

  14. Tuan, P. V., & Koo, I. (2017). Optimal multiuser MISO beamforming for power-splitting SWIPT cognitive radio networks. Ieee Access : Practical Innovations, Open Solutions, 5, 14141–14153.

    Article  Google Scholar 

  15. Alavi, F., Cumanan, K., Ding, Z., & Burr, A. G. (2018). Beamforming techniques for nonorthogonal multiple access in 5G cellular networks,. IEEE Transactions on Vehicular Technology, 67, 9474–9487.

    Article  Google Scholar 

  16. Tervo, O., Pennanen, H., Christopoulos, D., Chatzinotas, S., & Ottersten, B. (2017). Distributed optimization for coordinated beamforming in multicell multigroup multicast systems: power minimization and SINR balancing,. IEEE Transactions on Signal Processing, 66, 171–185.

    Article  MathSciNet  MATH  Google Scholar 

  17. Nguyen, D., Zomorrodi, M., Karmakar, N., & Ho, K. (2020). Efficient beamforming technique based on sparse MIMO array and spatial Filter Bank,. IEEE Antennas and Wireless Propagation Letters, 19, 1147–1151.

    Article  Google Scholar 

  18. Higuchi, T., Ito, N., Araki, S., Yoshioka, T., Delcroix, M., & Nakatani, T. (2017). Online MVDR beamformer based on complex gaussian mixture model with spatial prior for noise robust ASR,. IEEE/ACM Transactions on Audio Speech and Language Processing, 25, 780–793.

    Article  Google Scholar 

  19. Senel, K., Björnson, E., & Larsson, E. G. (2019). Joint transmit and circuit power minimization in massive MIMO with downlink SINR constraints: when to turn on massive MIMO?,. IEEE Transactions on Wireless Communications, 18, 1834–1846.

    Article  Google Scholar 

  20. Zhang, X., Wang, J., & Poor, H. V. (2022). Joint optimization of IRS and UAV-Trajectory: for supporting statistical Delay and Error-Rate Bounded QoS over mURLLC-Driven 6G Mobile Wireless Networks using FBC,. IEEE Vehicular Technology Magazine, 17, 55–63.

    Article  Google Scholar 

  21. Zhang, T., Wang, Y., Yi, W., Liu, Y., & Nallanathan, A. (2022). Joint Optimization of Caching Placement and Trajectory for UAV-D2D Networks. IEEE Transactions on Communications, 70(8), 5514-5527

    Article  Google Scholar 

  22. Ranjan, R., Agrawal, N., & Joshi, S. (2020). Interference mitigation and capacity enhancement of cognitive radio networks using modified greedy algorithm/channel assignment and power allocation techniques,. IET Communications, 14, 1502–1509.

    Article  Google Scholar 

  23. Banerjee, A., Paul, A., & Maity, S. P. (2017). Joint power allocation and route selection for outage minimization in multihop cognitive radio networks with energy harvesting,. IEEE Transactions on Cognitive Communications and Networking, 4, 82–92.

    Article  Google Scholar 

  24. Nguyen, N. Q., & Prager, R. W. (2018). “A spatial coherence approach to minimum variance beamforming for plane-wave compounding. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 65: 522–534,

    Article  Google Scholar 

  25. Jiang, X., Wu, Z., Yin, Z., Yang, Z., & Zhao, N. (2020). Power consumption minimization of UAV relay in NOMA networks,. IEEE Wireless Communications Letters, 9, 666–670.

    Article  Google Scholar 

  26. EL-Mokadem, E. S., El‐Kassas, A. M., Elgarf, T. A., & El‐Hennawy, H. (2020). Throughput enhancement of cognitive M2M networks based on NOMA for 5G communication systems,. International Journal of Communication Systems, 33, e4468.

    Article  Google Scholar 

  27. Dash, S. P., Subhashini, K., & Satapathy, J. (2020). Optimal location and parametric settings of FACTS devices based on JAYA blended moth flame optimization for transmission loss minimization in power systems,. Microsystem Technologies, 26, 1543–1552.

    Article  Google Scholar 

  28. Shim, Y., Park, H., & Shin, W. (2020). Joint time allocation for wireless energy harvesting decode-and-forward relay-based IoT networks with rechargeable and nonrechargeable batteries,. IEEE Internet of Things Journal, 8, 2792–2801.

    Article  Google Scholar 

  29. Mu, G. (2020). Joint beamforming and power allocation for wireless powered UAV-assisted cooperative NOMA systems. EURASIP Journal on Wireless Communications and Networking, 2020: 1–14

    Article  Google Scholar 

  30. Shin, J. (2021). Min-SINR-maximizing wireless-powered AF relay for multisource and multidestination networks,. Wireless Personal Communications, 116, 1785–1793.

    Article  Google Scholar 

Download references

Funding

This research work was not funded by any organization/institute/agency.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. Malarvizhi.

Ethics declarations

Conflict of interest

I confirm that this work is original and has either not been published elsewhere, or is currently under consideration for publication elsewhere.

Consent to Participate

I confirm that any participants (or their guardians if unable to give informed consent, or next of kin, if deceased) who may be identifiable through the manuscript (such as a case report), have been given an opportunity to review the final manuscript and have provided written consent to publish.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Thomas, R.M., Malarvizhi, S. Multicast Spatial Filter Beamforming with Resource Allocation Using Joint Multi-objective Optimization Approaches in Wireless Powered Communication Networks. Wireless Pers Commun 129, 2481–2501 (2023). https://doi.org/10.1007/s11277-023-10242-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-023-10242-5

Keywords

Navigation