A Novel method for assigning Joint power spectrum and Power Selection in device to device networks to improve performance

  • Anahita Jabbari Department of Electrical EngineeringIslamic Azad Univercity of Najaf Abad
  • S. Mahmood Daneshvar Farzanegan I assistant prof slamic Azazd university of Najaf Abad, Faculty of Electric engineering, Najaf Abad, Iran
Keywords: KEYWORDS: frequency spectrum, Cognitive radio, machine learning, device to device pair, cellular automata

Abstract

Optimal utilization of frequency spectrum in wireless networks particularly in device to device communication is of significant importance owing to the growing demand. Traditional methods to optimal spectrum utilization of spectrum are not sufficiently efficient and result in loss of spectrum. Recently, application of Cognetive radio is suggested to solve this problem. Cognetive radio is a smart wireless system which is aware of the spectral traffic condition of its environment in an instantaneous way and through these spectral conditions, changes the power of transmitter and the type of modulation and it adapts to the environment. The main purpose of this paper is to investigate the problem of spectral sharing. Today, communication systems suffer from main problems including limited bandwidth, download speed increase, rate increase and saving in transmitted power. To solve such problems, new methods based on machine learning in spectrum sharing are necessary to overcome such challenges. In this work, using cellular learner automata, a method is proposed for simultaneous assigning of spectrum and resource. The aim of each pair of transmission is to transmit in an appropriate channel and power level so that it can maximize its compensation in cellular learner automata. In these scenarios, compensation is taken as the difference between operational (collective) and consumed power. The cost of the consumed power is the signal to interference noise ration. Proposed method is simulated on a LTE-A network as well as an NS2. Proposed algorithm is of rapid convergence and semi-optimal efficiency in low repetitions.

References

[1] A. Asheralieva and Y. Miyanaga, "Dynamic buffer status-based control for LTE-A network with underlay D2D communication," IEEE transactions on communications, vol. 64, no. 3, pp. 1342-1355, 2016.
[2] I. F. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mohanty, "NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey," Computer networks, vol. 50, no. 13, pp. 2127-2159, 2006.
[3] W. Y. Lee, "Spectrum management in cognitive radio wireless networks," Georgia Institute of Technology, 2009.
[4] A. Vosoughi, J. R. Cavallaro, and A. Marshall, "Robust consensus-based cooperative spectrum sensing under insistent spectrum sensing data falsification attacks," in 2015 IEEE Global Communications Conference (GLOBECOM), 2015, pp. 1-6: IEEE.
[5] W. Zhang, Z. Wang, Y. Guo, H. Liu, Y. Chen, and J. Mitola III, "Distributed cooperative spectrum sensing based on weighted average consensus," in 2011 IEEE Global Telecommunications Conference-GLOBECOM 2011, 2011, pp. 1-6: IEEE.
[6] S. Tanwar, S. Tyagi, N. Kumar, and M. S. Obaidat, "LA-MHR: Learning Automata Based Multilevel Heterogeneous Routing for Opportunistic Shared Spectrum Access to Enhance Lifetime of WSN," IEEE Systems Journal, no. 99, pp. 1-11, 2018.
[7] N. Vucevic, I. F. Akyildiz, and J. Pérez-Romero, "Cooperation reliability based on reinforcement learning for cognitive radio networks," in 2010 Fifth IEEE Workshop on Networking Technologies for Software Defined Radio Networks (SDR), 2010, pp. 1-6: IEEE.
[8] Y. Li, D. Jin, J. Yuan, and Z. Han, "Coalitional games for resource allocation in the device-to-device uplink underlaying cellular networks," IEEE Transactions on wireless communications, vol. 13, no. 7, pp. 3965-3977, 2014.
[9] Y. Xiao, K.-C. Chen, C. Yuen, Z. Han, and L. A. DaSilva, "A Bayesian overlapping coalition formation game for device-to-device spectrum sharing in cellular networks," IEEE Transactions on Wireless Communications, vol. 14, no. 7, pp. 4034-4051, 2015.
[10] L. Rose, S. Lasaulce, S. M. Perlaza, and M. Debbah, "Learning equilibria with partial information in decentralized wireless networks," IEEE communications Magazine, vol. 49, no. 8, pp. 136-142, 2011.
[11] S. Rasaneh and M. Jahanshahi, "A QoS aware learning automata based channel assignment method in cognitive network," Wireless Personal Communications, vol. 97, no. 1, pp. 495-519, 2017.
[12] S. Gheisari and M. R. Meybodi, "LA-CWSN: A learning automata-based cognitive wireless sensor networks," Computer Communications, vol. 94, pp. 46-56, 2016.
[13] B.-Y. Huang, S.-T. Su, C.-Y. Wang, C.-W. Yeh, and H.-Y. Wei, "Resource allocation in D2D communication-A game theoretic approach," in 2014 IEEE International Conference on Communications Workshops (ICC), 2014, pp. 483-488: IEEE.
[14] A. Larmo, M. Lindström, M. Meyer, G. Pelletier, J. Torsner, and H. Wiemann, "The LTE link-layer design," IEEE Communications magazine, vol. 47, no. 4, pp. 52-59, 2009.
[15] A. Asheralieva and Y. Miyanaga, "An autonomous learning-based algorithm for joint channel and power level selection by D2D pairs in heterogeneous cellular networks," IEEE transactions on communications, vol. 64, no. 9, pp. 3996-4012, 2016.
[16] T. Alpcan, H. Boche, M. L. Honig, and H. V. Poor, Mechanisms and games for dynamic spectrum allocation. Cambridge University Press, 2013.
Published
2019-09-05
How to Cite
Jabbari, A., & Daneshvar Farzanegan, S. M. (2019). A Novel method for assigning Joint power spectrum and Power Selection in device to device networks to improve performance. Majlesi Journal of Telecommunication Devices, 8(3), 101-110. Retrieved from http://journals.iaumajlesi.ac.ir/td/index/index.php/td/article/view/571
Section
Articles