Open Access Open Access  Restricted Access Subscription Access

Adaptive Resource Optimization for Cognitive Radio Networks


Affiliations
1 Department of Electronics Engineering, MIT Campus, Anna University, Chennai, India
 

In cognitive radio network, the spectrum sensing finds either the channel is occupied or idle, the problem is assigning the unused channels of the primary user (PU) to the secondary users in an efficient manner is most challenging issue. In this work, we investigates the above issue and proposed an adaptive resource allocation to the secondary users in terms of channel allocation and power allocation. The proposed work intelligently handles both frequency and space efficiently without affecting the quality of service (QoS) of the primary user. We considered both underlay and overlay spectrum access, based on that resource allocation is carried out in an efficient manner. The maximum transmitted data rate of the secondary user (SU) obtained is 225Kbps determined by using Shannon channel capacity theorem. The proposed work also shows the effectiveness of the simulation in terms of energy efficiency up to 8.25 x 105 bits/Joule.

Keywords

Resource allocation, Cognitive radio, OFDM, data rate, energy efficiency
User
Notifications
Font Size

  • S. Pandit, G. Singh, “An overview of spectrum sharing techniques in cognitive radio communication system,” Wireless Network, 23(2), 2017, 497–518.
  • Force, S.P.T. Spectrum Policy Task Force Report Et Docket No. 02-135; US Federal Communications Commission: Washington, DC, USA, 2002.
  • Pickholtz, RL., Schilling, DL., Milstein, LB.: “Theory of spread-spectrum communications-A tutorial,” IEEE Transactions on Communications, 30(5), 1982, 855–84.
  • Gopalakrishnan, B., Bhagyaveni, MA.: “Anti jamming communication for body area network using chaotic frequency hopping,” Healthcare Technology Letters, 4(6), 2017, 233–237.
  • Dhivyadharshini and B. Gopalakrishnan, “Comparative Analysis of FH and CFH Spread Spectrum Under Different Jammers,” 2020 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, 2020, 1361-136.
  • G. Asaithambi and B. Gopalakrishnan, “Design of Code and Chaotic Frequency Modulation for Secure and High Data rate Communication,” 2021 5th International Conference on Computer, Communication and Signal Processing (ICCCSP), 2021, pp. 1-6.
  • S. Barve, A. Akotkar, A. Chavan, A. Kumar and M. Dhaigude, “Open Source Software Defined Radio Using GNU Radio and USRP,” International journal of scientific and technology research, 3(5), 2014, 305-308.
  • J. Mitola, III, “Software radio architecture: A mathematical perspective,” IEEE J. Sel. Areas Commun., 17(4) , 2010, 514–538.
  • Q. Zhao and B. M. Sandler, “A survey of dynamic spectrum access,” IEEE Signal Process. Mag., 24(3), 2007, 79–89.
  • A. Ghasemi and E. S. Sousa, “Fundamental limits of spectrum-sharing in fading environments,” IEEE Trans. Wireless Commun., 6(2), 2007, 649–658.
  • G. Marques, L. M. Lopez-Ramos, G. B. Giannakis, and J. Ramos, “Resource allocation for interweave and underlay CRS under probability of interference constraints,” IEEE J. Sel. Areas Commun., 30(10), 2012, 1922–1933.
  • J. Zou, H. Xiong, D. Wang and C. W. Chen, “Optimal Power Allocation for Hybrid Overlay/Underlay Spectrum Sharing in Multiband Cognitive Radio Networks,” IEEE Transactions on Vehicular Technology, 62(4), 2013, 1827-1837.
  • Yongjun Xu, Xiaohui Zhao, “Robust adaptive power control for cognitive radio networks,” Signal Processing IET, 10(1), 2016, 19-27.
  • T. Xue, X. Dong and Y. Shi, “Resource-Allocation Strategy for Multiuser Cognitive Radio Systems: Location-Aware Spectrum Access,” IEEE Transactions on Vehicular Technology, 66(1), 2017, 884-889.
  • G. Bansal, M. J. Hossain, V. K. Bhargava and T. Le Ngoc, “Subcarrier and Power Allocation for OFDMA-Based Cognitive Radio Systems With Joint Overlay and Underlay Spectrum Access Mechanism,” IEEE Transactions on Vehicular Technology, 62(3), 2013, 1111-1122
  • S. Wang, Z. Zhou, M. Ge and C. Wang, “Resource Allocation for Heterogeneous Cognitive Radio Networks with Imperfect Spectrum Sensing,” IEEE Journal on Selected Areas in Communications, 31(3), 2013, 464 -475.
  • S. Wang, W. Shi and C. Wang, “Energy -Efficient Resource Management in OFDM -Based Cognitive Radio Networks Under Channel Uncertainty,” IEEE Transactions on Communications, 63(9), 2015, 3092 - 3102.
  • G. C. Deepak, K. Navaie and Q. Ni, “Radio Resource Allocation in Collaborative Cognitive Radio Networks Based on Primary Sensing Profile,” IEEE Access, 6, 2018, 50344 -50357.
  • Kayalvizhi, E, and Gopalakrishnan, B, “Estimation of optimal channel gain in cognitive radio networks using bisectional algorithm,” International Journal of Advanced Networking and Applications, 11(1), 2019, 417 1 -4176.
  • Swati parmar, Keshav Mishra “Moth Flame Optimization Based Maximization Of Transmission Rate For Cognitive Radio Users,” International Journal of Advanced Science and Technology , 29(12), 2020, 1845 -1851.
  • F. Shah -Mohammadi, H. H. Enaami and A. Kwasinski, “Neural Network Cognitive Engine for Autonomous and Distributed Underlay Dynamic Spectrum Access,” in IEEE Open Journal of the Communications Society, vol. 2, 2021, 719 -737.

Abstract Views: 240

PDF Views: 1




  • Adaptive Resource Optimization for Cognitive Radio Networks

Abstract Views: 240  |  PDF Views: 1

Authors

Kayalvizhi E.
Department of Electronics Engineering, MIT Campus, Anna University, Chennai, India
Balamurugan Gopalakrishnan
Department of Electronics Engineering, MIT Campus, Anna University, Chennai, India

Abstract


In cognitive radio network, the spectrum sensing finds either the channel is occupied or idle, the problem is assigning the unused channels of the primary user (PU) to the secondary users in an efficient manner is most challenging issue. In this work, we investigates the above issue and proposed an adaptive resource allocation to the secondary users in terms of channel allocation and power allocation. The proposed work intelligently handles both frequency and space efficiently without affecting the quality of service (QoS) of the primary user. We considered both underlay and overlay spectrum access, based on that resource allocation is carried out in an efficient manner. The maximum transmitted data rate of the secondary user (SU) obtained is 225Kbps determined by using Shannon channel capacity theorem. The proposed work also shows the effectiveness of the simulation in terms of energy efficiency up to 8.25 x 105 bits/Joule.

Keywords


Resource allocation, Cognitive radio, OFDM, data rate, energy efficiency

References