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Spectral and energy efficiency trade-off in massive MIMO systems using multi-objective bat algorithm


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[1] E. Björnson, J. Hoydis, and L. Sanguinetti, “Massive MIMO Networks: Spectral, Energy and Hardware Efficiency”, Foundation and Trends in Signal Processing, pp. 154–655, doi: 10.1561/2000000093, 2017.10.1561/2000000093 Search in Google Scholar

[2] A. Fehske, G. Fettweis, J. Malmodin, and G. Biczok, “The Global Footprint of Mobile Communications: The Ecological and Economic Perspective”, IEEE Transactions on Wireless Communications vol. 49, no. 8, pp. 55–62, doi: 10.1109/MCOM.2011.597 8416, 2011. Search in Google Scholar

[3] Q. He, L. Xiao, X. Zhong, and S. Zhou, “Increasing the Sum-throughput of Cells with a Sectorization Method for Massive MIMO”, IEEE Communications Letters, vol. 18, no. 10, pp. 1827–1830, doi: 10.1109/LCOMM.2014.2346483, 2014.10.1109/LCOMM.2014.2346483 Search in Google Scholar

[4] B. Zhang, Y. Tian, and W. Wang, “On the Downlink Throughput Capacity of Hybrid Wireless Networks with Massive MIMO”, Eurasip Journal on Wireless Communications and Networking, vol. 1, no. 110, pp. 26086–26091, doi: 10.1186/s13638-018-1134-1, 2018.10.1186/s13638-018-1134-1 Search in Google Scholar

[5] G. Yang, C. K. Ho, R. Zhang, and Y. L. Guan, “Throughput Optimization for Massive MIMO Systems Powered by Wireless Energy Transfer”, IEEE Journal on Selected Areas in Communications vol. 33, no. 8, pp. 1640–1650, doi: 10.1109/JSAC.2015.2391835, 2015.10.1109/JSAC.2015.2391835 Search in Google Scholar

[6] F. Heydari, S. Ghazi-Maghrebi, A. Shahzadi, and M. J. R. Fatemi, “Better spectral efficiency of device to device underlying massive multi-input multi-output using receiver filter algorithm and power control model”, International Journal of Communication Systems, vol. 34, no. 5, pp. e4655, doi: 10.1002/dac.4655, 2021.10.1002/dac.4655 Search in Google Scholar

[7] T. Zeng, and E. Ouyang, “Massive multi-in multi-out multiuser multiplexing based on transmission mode 3”, International Journal of Communication Systems, vol. 34, no. 7, pp. e4761, doi: 10.1002/dac.4761, 2021.10.1002/dac.4761 Search in Google Scholar

[8] A. Shukla, V. Goyal, M. Kumar, M. C. Trivedi, and V. K. Deolia, “MMSE based beamformer in massive MIMO-IDMA downlink systems”, Journal of Electrical Engineering, vol. 71, no. 1, pp. 65–68, doi: 10.2478/jee-2020-0010, 2020.10.2478/jee-2020-0010 Search in Google Scholar

[9] B. K. Gül, and N. Taşpınar, “Application of Intelligent Optimization Techniques to Spectral and Energy Efficiencies in Massive MIMO Systems at Different Circuit Power Levels”, Mühendislik Bilimleri ve Arastirmalari Dergisi, vol. 3, no. 1, pp. 102–111, doi: 10.46387/bjesr. 893643, 2021. Search in Google Scholar

[10] Z. Liu, W. Du, and D. Sun, “Energy and Spectral Efficiency Tradeoff for Massive MIMO Systems with Transmit Antenna Selection”, IEEE Transactions on Vehicular Technology, vol. 66, no. 5, pp. 4453-4457, doi: 10.1109/TVT.2016.2598842, 2017.10.1109/TVT.2016.2598842 Search in Google Scholar

[11] Y. Hei, C. Zhang, W. Song, and Y. Kou, “Energy and Spectral Efficiency Tradeoff in Massive MIMO Systems with Multi-objective Adaptive Genetic Algorithm”, Soft Computing, vol. 23, no. 16, pp. 7163–7179, doi: 10.1007/s00500-018-3356-x, 2019.10.1007/s00500-018-3356-x Search in Google Scholar

[12] E. Björnson, E. G. Larsson, and M. Debbah, “Massive MIMO for Maximal Spectral Efficiency: How Many Users and Pilots Sholud Be Allocated?”, IEEE Transactions on Wireless Communiations, vol. 15, no. 2, pp. 1293–1308, doi: 10.1109/TWC.2015.2488634, 2015.10.1109/TWC.2015.2488634 Search in Google Scholar

[13] L. Li, W. Meng, and S. Ju, “A novel artificial bee colony detection algorithm for massive MIMO system”, Wireless Communications and Mobile Computing, vol. 16, pp. 3139–3152, doi: 10.1002/wcm 2754, 2016. Search in Google Scholar

[14] X.-S. Yang, “Bat algorithm for multi-objective optimisation”, International Journal of Bio-Inspired Computation, vol. 3, no. 4, pp. 267–274, doi: 10.1504/IJBIC.2011.042259, 2011.10.1504/IJBIC.2011.042259 Search in Google Scholar

[15] Y. Yuan, H. Xu, B. Zhang, and X. Yao, “Balancing convergence and diversity in decomposition-based many-objective optimizers”, IEEE Transactions on Evolutionary Computation, vol. 19, no. 5, pp. 694–716, doi: 10.1109/TEVC.2015.2443001, 2015.10.1109/TEVC.2015.2443001 Search in Google Scholar

[16] C. Goh, and K. C. Tan, “An investigation on noisy environments in evolutionary multiobjective optimization”, IEEE Transactions on Evolutionary Computation, vol. 11, no. 3, pp. 354–381, doi: 10.1109/TEVC.2006.882428, 2007.10.1109/TEVC.2006.882428 Search in Google Scholar

[17] J. Schott, “Fault tolerant design using single and multicriteria genetic algorithm optimization”, MS thesis, Massachusetts Technology, 1995. Search in Google Scholar

[18] T. Murata, and H. Ishibuchi, “MOGA: Multiobjective genetic algorithms”, Proc. of IEEE International Conference on Evolutionary Computation, pp. 289–294, 1995. Search in Google Scholar

[19] W. Gong, and Z. Cai, “A multiobjective differential evolution algorithm for constrained optimization”, IEEE Congress on Evolutionary Computation, pp. 181–188, doi: 10. 1109/CEC.2008.46 30 796, 2008. Search in Google Scholar

[20] C. A. C. Coello, G. T. Pulido, and M. S. Lechuga, “Handling multiple objectives with particle swarm optimization”, IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 256–279, doi: 10.1109/TEVC.2004.826067, 2004.10.1109/TEVC.2004.826067 Search in Google Scholar

eISSN:
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Language:
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