Spectrum Sensing and Resource Allocation for Proficient Transmission in Cognitive Radio with 5G

References

• E. Hossain, D. Niyato, and D. I. Kim, “Evolution and future trends of research in cognitive radio: A contemporary survey,” Wirel. Commun. Mob. Comput., Vol. 15, no. 11, pp. 1530–64, 2015. doi: 10.1002/wcm.2443
   Web of Science ®Google Scholar
• M. Amjad, M. H. Rehmani, and S. Mao, “Wireless multimedia cognitive radio networks: A comprehensive survey,” IEEE Commun. Surv. Tutor., Vol. 20, no. 2, pp. 1056–103, 2018. doi: 10.1109/COMST.2018.2794358
   Web of Science ®Google Scholar
• F. K. Jondral, “Software-defined radio—Basics and evolution to cognitive radio,” EURASIP J. Wirel. Commun. Netw., 275–83, 2005.
   Google Scholar
• C.-I. Badoi, N. Prasad, V. Croitoru, and R. Prasad, “5G based on cognitive radio,” Wirel. Pers. Commun., Vol. 57, no. 3, pp. 441–64, 2011. doi: 10.1007/s11277-010-0082-9
   Web of Science ®Google Scholar
• H. A. Mahmoud, T. Yucek, and H. Arslan, “OFDM for cognitive radio: Merits and challenges,” IEEE Wirel. Commun., Vol. 16, no. 2, pp. 6–15, 2009. doi: 10.1109/MWC.2009.4907554
   Web of Science ®Google Scholar
• A. Ali and W. Hamouda, “Advances on spectrum sensing for cognitive radio networks: Theory and applications,” IEEE Commun. Surv. Tutor., Vol. 19, no. 2, pp. 1277–304, 2016. doi: 10.1109/COMST.2016.2631080
   Web of Science ®Google Scholar
• A. Margoosian, J. Abouei, and K. N. Plataniotis, “An accurate Kernelized energy detection in Gaussian and non-Gaussian/impulsive noises,” IEEE Trans. Signal Process., Vol. 63, no. 21, pp. 5621–36, 2015. doi: 10.1109/TSP.2015.2457400
   Web of Science ®Google Scholar
• C. Liu, M. Li, and M.-L. Jin, “Blind energy-based detection for spatial spectrum sensing,” IEEE Wirel. Commun. Lett., Vol. 4, no. 1, pp. 98–101, 2015. doi: 10.1109/LWC.2014.2377233
   Web of Science ®Google Scholar
• T. E. Bogale and L. Vandendorpe, “Max-min SNR signal energy based spectrum sensing algorithms for cognitive radio networks with noise variance uncertainty,” IEEE Trans. Commun., Vol. 13, no. 1, pp. 280–90, 2014.
   Web of Science ®Google Scholar
• P. Chauhan, S. K. Deka, M. Devi, and N. Sarma, “Cooperative spectrum sensing scheduling in multi-channel cognitive radio networks: A broad perspective,” Networking and Internet Architecture, 2017.
   Google Scholar
• E. Ahmed, A. Gani, S. Abolfazli, L. J. Yao, and S. U. Khan, “Channel assignment algorithms in cognitive radio networks: Taxonomy, open issues, and challenges,” IEEE Commun. Surv. Tutor., Vol. 18, no. 1, pp. 795–823, 2016. doi: 10.1109/COMST.2014.2363082
   Web of Science ®Google Scholar
• L. Xin, Z. Wei-Zhi, and C. Kun-Qi, “Optimization of sensing time and cooperative user allocation for OR-rule cooperative spectrum sensing in cognitive radio network,” J. Cent. South Univ. T., Vol. 22, no. 7, pp. 2646–54, 2015. doi: 10.1007/s11771-015-2795-0
   Web of Science ®Google Scholar
• S. Althunibat and F. Granelli, “On results’ reporting of cooperative spectrum sensing in cognitive radio networks,” Telecommun. Syst., Vol. 62, no. 3, pp. 569–80, 2016. doi: 10.1007/s11235-015-0095-5
   Web of Science ®Google Scholar
• A. Bhowmick, K. Yadav, S. D. Roy, and S. Kundu. “Multi slot-throughput tradeoff in an improved energy detector based faded cognitive radio network,” Wirel. Netw., Vol. 24, no. 7, pp. 2539–52, 2017. doi: 10.1007/s11276-017-1487-0
   Web of Science ®Google Scholar
• G. I. Tsiropoulos, O. A. Dobre, M. H. Ahmed, and K. E. Baddour, “Radio resource allocation techniques for efficient spectrum access in cognitive radio networks,” IEEE Commun. Surv. Tutor., Vol. 18, no. 1, pp. 824–47, 2016. doi: 10.1109/COMST.2014.2362796
   Web of Science ®Google Scholar
• W. Lu, Z. Quan, Q. Liu, D. Zhang, and W. Xu, “QoE based spectrum allocation optimization using bees algorithm in cognitive radio networks,” in International Conference on Algorithms and Architecture of Parallel Processing, Zhangjiajie, China. Springer, 2015, pp. 327–38.
   Google Scholar
• Z. Liu and C. Li, “On spectrum allocation in cognitive radio networks: a double auction-based methodology,” Wirel. Netw., Vol. 23, no. 2, pp. 453–66, 2017. doi: 10.1007/s11276-015-1152-4
   Web of Science ®Google Scholar
• N. Janatian, M. Modarres-Hashemi, and S. Sun, “Joint versus separate spectrum sensing and resource allocation in OFDMA-based cognitive radio networks,” IET Commun., Vol. 10, no. 7, pp. 839–47, 2016. doi: 10.1049/iet-com.2015.0912
   Web of Science ®Google Scholar
• N. Mokari, F. Alavi, S. Parsaeefard, and T. Le-Ngoc, “Limited-feedback resource allocation in heterogeneous cellular networks,” IEEE Trans. Veh. Technol., Vol. 65, no. 4, pp. 2509–21, 2015. doi: 10.1109/TVT.2015.2428997
   Web of Science ®Google Scholar
• L. Khalid and A. Anpalagan, “Adaptive assignment of heterogeneous users for group-based cooperative spectrum sensing,” IEEE Trans. Wirel. Commun., Vol. 15, no. 1, pp. 232–46, 2016. doi: 10.1109/TWC.2015.2469667
   Web of Science ®Google Scholar
• A. Nath and N. Sarma, “A distributed solution for cooperative spectrum sensing scheduling in multiband cognitive radio networks,” J. Netw. Comput. Appl, Vol. 94, pp. 69–77, 2017. doi: 10.1016/j.jnca.2017.07.003
   Web of Science ®Google Scholar
• Z. Zhang, W. Zhang, S. Zeadally, Y. Wang, and Y. Liu, “Cognitive radio spectrum sensing framework based on multi-agent architecture for 5G networks,” IEEE Wirel. Commun., Vol. 22, no. 6, pp. 34–9, 2015. doi: 10.1109/MWC.2015.7368822
   Web of Science ®Google Scholar
• M. Ghaznavi and A. Jamshidi, “A reliable spectrum sensing method in the presence of malicious sensors in distributed cognitive radio network,” IEEE Sens. J., Vol. 15, no. 3, pp. 1810–16, 2015.
   Web of Science ®Google Scholar
• Y. Wang, G. Nie, G. Li, and C. Shi, “Sensing-throughput tradeoff in cluster-based cooperative cognitive radio networks with a TDMA reporting frame structure,” Wirel. Pers. Commun., Vol. 71, no. 3, pp. 1795–818, 2013. doi: 10.1007/s11277-012-0911-0
   Web of Science ®Google Scholar
• A. Roy, S. Midya, K. Majumder, S. Phadikar, and A. Dasgupta, “Optimized secondary user selection for quality of service enhancement of two-tier multi-user cognitive radio network: A game theoretic approach,” Comput. Netw., Vol. 123, pp. 1–18, 2017. doi: 10.1016/j.comnet.2017.05.002
   Web of Science ®Google Scholar
• H. B. Salameh and M. El-Attar, “Cooperative OFDM-based virtual clustering scheme for distributed coordination in cognitive radio networks,” IEEE Trans. Veh. Technol., Vol. 64, no. 8, pp. 3624–34, 2015. doi: 10.1109/TVT.2014.2360985
   Web of Science ®Google Scholar
• E. Basar, “On multiple-input multiple-output OFDM with index modulation for next generation wireless networks,” IEEE Trans. Sign Proc., Vol. 64, no. 15, pp. 3868–78, 2015. doi: 10.1109/TSP.2016.2551687
   Web of Science ®Google Scholar
• D. B. Rawat, “Evaluating performance of cognitive radio users in MIMO-OFDM based wireless networks,” IEEE Wirel. Commun. Lett., Vol. 5, no. 5, pp. 476–9, 2016. doi: 10.1109/LWC.2016.2587262
   Web of Science ®Google Scholar
• X. Zhang and X. Zhang, “Using artificial bee colony algorithm with crossover for power allocation in cognitive MIMO-OFDM system,” Phys. Commun., Vol. 25, no. 2, pp. 363–8, 2017. doi: 10.1016/j.phycom.2017.06.003
   Google Scholar
• W. Hao, S. Yang, B. Ning, and W. Hao, “Optimal resource allocation for cooperative orthogonal frequency division multiplexing based cognitive radio networks with imperfect spectrum sensing,” IET Commun., Vol. 9, no. 4, pp. 548–57, 2015. doi: 10.1049/iet-com.2014.0605
   Web of Science ®Google Scholar
• T. Xue, X. Dong, and Y. Shi, “Resource allocation strategy for multi-user cognitive radio systems: Location-aware spectrum access,” IEEE Trans. Veh. Technol., Vol. 66, no. 1, pp. 884–9, 2017.
   Web of Science ®Google Scholar
• S. Pan, X. Zhao, and Y.-C. Liang, “Robust power allocation for OFDM-based cognitive radio networks: A switched affine based control approach,” IEEE. Access., Vol. 5, pp. 18778–92, 2017. doi: 10.1109/ACCESS.2017.2751565
   Web of Science ®Google Scholar
• H. Nam, N. Saeed, M. Ben-Ghorbel, and M. S. Alouini, “Primary user localisation and uplink resource allocation in orthogonal frequency division multiple access cognitive radio systems,” IET Commun., Vol. 9, no. 8, pp. 1131–7, 2015. doi: 10.1049/iet-com.2014.1002
   Web of Science ®Google Scholar
• A. N. Jadhav, S. R. Mujawar, and P. S. Pise, “Optimal and water-filling algorithm approach for power allocation in OFDM based cognitive radio system,” Int. J. Eng. Res. Technol., Vol. 10, no. 1, pp. 470–6, 2017.
   Google Scholar
• H. Li, X. Xing, J. Zhu, X. Cheng, K. Li, R. Bie, and T. Jing, “Utility-based cooperative spectrum sensing scheduling in cognitive radio networks,” IEEE Trans. Veh. Technol., Vol. 66, no. 1, pp. 645–55, 2017.
   Web of Science ®Google Scholar
• S. Hussain and X. Fernando, “Approach for cluster-based spectrum sensing over band-limited reporting channels,” IET Commun., Vol. 6, no. 11, pp. 1466–74, 2012. doi: 10.1049/iet-com.2010.0510
   Web of Science ®Google Scholar
• M. El-Absi, M. Shaat, F. Bader, and T. Kaiser, “Interference alignment with frequency-clustering for efficient resource allocation in cognitive radio networks,” IEEE Trans. Wirel. Commun., Vol. 14, no. 12, pp. 7070–82, 2015. doi: 10.1109/TWC.2015.2464371
   Web of Science ®Google Scholar
• L. Zhang, T. Jiang, and K. Luo, “Dynamic spectrum allocation for the downlink of OFDMA-based hybrid access cognitive femtocell networks,” IEEE Trans. Veh. Technol., Vol. 65, no. 3, pp. 1772–81, 2016. doi: 10.1109/TVT.2015.2414424
   Web of Science ®Google Scholar
• M. Raeis, K. Shahtalebi, and A. R. Forouzan, “Computationally efficient adaptive algorithm for resource allocation in orthogonal frequency-division multiple-access-based cognitive radio networks,” IET Commun., Vol. 9, no. 12, pp. 1442–9, 2015. doi: 10.1049/iet-com.2014.0554
   Web of Science ®Google Scholar