Resource Allocation Parameters Improvement in Interference-Based Cognitive Radio

Subject Areas : Wireless communication

Sayyed Majid Mazinani ¹ , Ali Hasan Nezhad ²

1 - Electrical Engineering Department- Imam Reza International University, Mashhad, Iran
2 - Electrical Engineering Department- Imam Reza International University, Mashhad, Iran

Received: 2022-06-30 Accepted : 2022-08-28 Published : 2025-02-19

Keywords: Cognitive radio network, Interference Alignment, Special radiation interference homogeneity, multi-pregnant multi-antenna commun‌ica‌tions,

Abstract :

Interference Alignment is an efficient method of reducing interference in wireless networks, which can be used in radio cognitive networks. In the interference alignment, a suitable pre-encoder matrix will be found in each transmitter that all interferences are limited to a part of the signal subspace in each receiver, which causes the desired signal to be placed in the other part. Therefore, the desired signal can be easily received by a suitable interference removal filter. In this paper, an efficient method for using interference homogeneity in a cognitive radio network is presented. In the proposed method, the selection of radiation vectors for the formation of selection vectors based on adjacent vectors is done in equal steps. Selecting equal steps improves the convergence speed of the algorithm. The results show that computational efficiency and complexity have been greatly improved. To evaluate the proposed method of power allocation in the network, they are evaluated to maximize the network energy efficiency and the other to maximize the total rate of the cognitive radio network while keeping the initial user rate at the threshold level. The simulation results reveal the improvement of network performance using this method in both strategies.

References:

[1] S. Xiao, X. Zhou, D. Feng, Y. Yuan-Wu, G. Y. Li, W. Guo, "Energy-efficient mobile association in hetero­ge­ne­ous networks with device-to-device communications", IEEE Trans. on Wireless Commun­ications, vol. 15, no. 8, pp. 5260-5271, Aug. 2016 (doi: 10.1109/TWC.2016.2555797).

[2] Y. Ni, J. Zhao, Y. Wang, H. Zhu, "Beamforming and interference cancellation for D2D communication assisted by two-way decode-and-forward relay node", China Communications, vol. 15, no. 3, pp. 100-111, March 2018 (doi: 10.1109/CC.2018.8331994).

[3] M. Lin, J. Ouyang, W.P. Zhu, "Joint beamforming and power control for device-to-device communications underlaying cellular networks", IEEE Journal on Selected Areas in Communications, vol. 34, no. 1, pp. 138-150, Jan. 2016 (doi: 10.1109/JSAC.2015.2452491).

[4] M. Lin, Q. Huang, T. Cola, W. Jun-Bo , J. Wang,  M. Guizani, W. Jin-Yuan, " Integrated 5G-satellite networks: A perspective on physical layer reliability and security", IEEE Wireless Communications, vol. 27, no.6, Article Number: 152159, Oct. 2020 (doi:10.1109/MWC.001.2000143).

[5]  J. Shi, Q. Zhang, L. Ying-Chang, X. Yuan, "Distributed deep learning power allocation for D2D network based on outdated information", Proceeding of the IEEE/WCNC, pp. 1-6, Seoul, Korea (South), May 2020 (doi: 10.1109/WCNC45663.2020.9120717).

[6] Y. Wang, L. Chen, Y. Zhou, X. Liu, F. Zhou, N. Al-Dhahir, "Resource allocation and trajectory design in UAV-assisted jamming wideband cognitive radio networks", IEEE Trans. on Cognitive Communications and Networking, vol. 7, no. 2, pp. 635-647, Aug. 2020 (doi: 10.1109/TCCN.2020.3014208).

[7] F. Aghaei, A. Avokh, "MRCSC: A cross-layer algorithm for joint multicast routing, channel selection, sched­uling, and call admission control in multi-cell multi-channel multi-radio cognitive radio wireless networks", Pervasive and Mobile Computing, vol. 64, Article Number: 101150, April 2020 (doi: 10.1016/j.p­mc­j.20­20.101150).

[8] T. Wang, Y. Wang, X. Wang, Y. Cao, "A detailed review of D2D cache in helper selection", World Wide Web, vol. 23, no. 4, pp. 240, April 2020 (doi: 10.1007/s11280-019-00756-z).

[9] R. Liu, K. Guo, K. An, S. Zhu, C. Li, and L. Gao, "Performance Evaluation of NOMA-Based Cognitive Integrated Satellite Terrestrial Relay Networks With Primary Interference", IEEE Access, vol. 9, pp. 71422-71434, May 2021 (doi: 10.1109/ACCESS.2021.3078630).

[10] M. Haus, M. Waqas, A.Y. Ding, Y. Li, S. Tarkoma, J. Ott, "Security and privacy in device-to-device (D2D) communication: A review", IEEE Communications Surveys and Tutorials, vol. 19, no. 2, pp. 1054-1079, Jan. 2017 (doi: 10.1109/COMST.2017.2649687).

[11] A.D. Borah, M. Hussain, J. Bora, "Performance analysis of interference alignment-based precoding", Electronic Systems and Intelligent Computing: Springer, pp. 919-927, Sept. 2020 (doi: 10.1007/978-981-15-7031-5_87).

[12] A.M. Soman,  R. Nakkeeran, S.M. John, "Improved DFT-based channel estimation for spatial modulated orthogonal frequency division multiplexing systems", IEICE Communications Express, vol. 11, no. 5, pp. 208-215, May 2022 (doi: 10.1587/comex.2022XBL0020).

[13] G. Peter, J. Livin, A. Sherine, "Hybrid optimization algorithm based optimal resource allocation for cooperative cognitive radio network", Array, vol. 12, Article Number: 100093, March 2022 (doi: 10.1016/j.a­rray.20­21.100093).

[14] V.R. Cadambe, S.A. Jafar, C. Wang, "Interference alignment with asymmetric complex signaling- Settling the Høst-Madsen–Nosratinia conjecture", IEEE Trans. on Information Theory, vol. 56, no. 9, pp. 4552-4565, Aug. 2010 (doi: 10.1109/TIT.2010.2053895).

[15] M.H. Al-Ali, K. Ho, "Enhanced precoder for secondary user of MIMO cognitive radio in the presence of CSIT uncertainties in the desired and interference links", Signal Processing, vol. 190, Article Number: 108294, Aug. 2021 (doi: 10.1016/j.sigpro.2021.108294).

[16] A. Avokh, G. Mirjalily, "Performance analysis of broadcasting in small-scale multi-radio multi-channel wireless mesh networks", Proceeding of the IEEE/ICACT, pp. 537-542, PyeongChang, Korea (South), Feb. 2012.

[17] C. Hellings, F. Askerbeyli, W. Utschick, "Two-user SIMO interference channel with treating interference as noise: Improper signaling versus time-sharing", IEEE Trans. on Signal Processing, vol. 68, pp. 6467-6480, Sept. 2020 (doi: 10.1109/TSP.2020.3027903).

[18] M. Soleymani, I. Santamaria, P.J. Schreier, "Improper gaussian signaling for the $ K $-user MIMO interference channels with hardware impairments", IEEE Trans. on Vehicular Technology, vol. 69, no. 10, pp. 11632-11645, Aug. 2020 (doi: 10.1109/TVT.2020.3015558).

[19] P. Mohapatra, K. Nissar, C.R. Murthy, "Interference alignment algorithms for the $ K $ user constant MIMO interference channel", IEEE Trans. on Signal Processing, vol. 59, no. 11, pp. 5499-5508, Aug. 2011 (doi: 10.1109/TSP.2011.2164069).

[20] M.A. Beigi, S.M. Razavizadeh, "Cooperative beamforming in cognitive radio networks", Proceeding of the IEEE/IFIP, pp. 1-5, Paris, France, Dec. 2009 (doi: 10.1109/WD.2009.5449675).

[21] D. Zhang, Z. Chen, M. K. Awad, N. Zhang, H. Zhou, X.S. Shen, "Utility-optimal resource management and allocation algorithm for energy harvesting cognitive radio sensor networks", IEEE Journal on Selected Areas in Communications, vol. 34, no. 12, pp. 3552-3565, Sept. 2016 (doi: 10.1109/JSAC.2016.2611960).

[22] H. Kalbkhani, V. Solouk, M.G. Shayesteh,  Resource allocation in integrated femto– macrocell networks based on location awareness", IET Communications, vol. 7, pp. 917-932, May 2015 (doi: 10.1049/iet-co­m.2­01­4.0691).

[22] Hashem Kalbkhani, Vahid Solouk, Mahrokh G. Shayesteh, Resource allocation in integrated femto– macrocell networks based on location awareness ", IET Communications, vol. 7, pp. 917-932, 2015 (doi: 10.1049/iet-com.2014.0691)