Efficient Channel Allocation using Matching Theory for QoS Provisioning in Cognitive Radio Networks

The focus of research efforts in cognitive radio networks (CRNs) has primarily remained confined to maximizing the utilization of the discovered resources. However, it is also important to enhance the user satisfaction in CRNs by finding a suitable match between the secondary users and the idle channels available from the primary network while taking into consideration not only the quality of service (QoS) requirements of the secondary users but the quality of the channels as well. In this work, the Gale Shapley matching theory was applied to find the best match, so that the most suitable channels from the available pool were allocated that satisfy the QoS requirements of the secondary users. Before applying matching theory, two objective functions were defined from the secondary user’s perspective as well as from the channel’s perspective. The objective function of secondary users is the weighted sum of the data rate of the secondary users and the probability of reappearance of the primary user on the channel. Whereas, the objective function of the channel is the maximum utilization of the channel. The weight factors included in the objective functions allow for diverse service classes of secondary users (SUs) or varying channel quality characteristics. The objective functions were used in developing the preference lists for the secondary users and the idle channels. The preference lists were then used by the Gale Shapely matching algorithm to determine the most suitably matched SU-channel pairs. The performance of the proposed scheme was evaluated using Monte–Carlo simulations. The results show significant improvement in the overall satisfaction of the secondary users with the proposed scheme in comparison to other contemporary techniques. Further, the impact of changing the weight factors in the objective functions on the secondary user’s satisfaction and channel utilization was also analyzed.

[1]  Walid Saad,et al.  Matching theory for future wireless networks: fundamentals and applications , 2014, IEEE Communications Magazine.

[2]  Jianping Pan,et al.  Channel Assignment in Cognitive Radio Networks: A Joint Utility and Stable Matching Approach , 2017, 2017 26th International Conference on Computer Communication and Networks (ICCCN).

[3]  Ilyong Chung,et al.  Spectrum mobility in cognitive radio networks , 2012, IEEE Communications Magazine.

[4]  Walaa Hamouda,et al.  Resource Allocation for Underlay Cognitive Radio Networks: A Survey , 2017, IEEE Communications Surveys & Tutorials.

[5]  Octavia A. Dobre,et al.  Radio Resource Allocation Techniques for Efficient Spectrum Access in Cognitive Radio Networks , 2016, IEEE Communications Surveys & Tutorials.

[6]  Norulhusna Ahmad,et al.  A Spectrum Handoff Scheme based on Joint Location and Channel State Prediction in Cognitive Radio , 2018, 2018 2nd International Conference on Telematics and Future Generation Networks (TAFGEN).

[7]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[8]  Arun Prakash,et al.  Spectrum handoff in cognitive radio networks: A classification and comprehensive survey , 2016, J. Netw. Comput. Appl..

[9]  Pramod K. Varshney,et al.  Matching theory for cognitive spectrum allocation in wireless networks , 2016, 2016 Annual Conference on Information Science and Systems (CISS).

[10]  Joel J. P. C. Rodrigues,et al.  Game Theoretic Analysis of Post Handoff Target Channel Sharing in Cognitive Radio Networks , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[11]  Mesmin J. Mbyamm Kiki,et al.  Spectrum Handoff Mechanism in the Framework of Mobility Management in Cognitive Radio Networks , 2019, Int. J. Technol. Diffusion.

[12]  L. S. Shapley,et al.  College Admissions and the Stability of Marriage , 2013, Am. Math. Mon..

[13]  Sherali Zeadally,et al.  Spectrum Assignment in Cognitive Radio Networks: A Comprehensive Survey , 2013, IEEE Communications Surveys & Tutorials.

[14]  Jianzhao Zhang,et al.  Matching Theory for Channel Allocation in Cognitive Radio Networks , 2016, 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring).

[15]  Vivek Rajpoot,et al.  A novel proactive handoff scheme with CR receiver based target channel selection for cognitive radio network , 2019, Phys. Commun..

[16]  Seyoun Lim,et al.  A Self-Scheduling Multi-Channel Cognitive Radio MAC Protocol Based on Cooperative Communications , 2011, IEICE Trans. Commun..

[17]  Shahzad Ali Malik,et al.  Analysis of Efficient Spectrum Handoff in a Multi-Class Hybrid Spectrum Access Cognitive Radio Network Using Markov Modelling , 2019, Sensors.

[18]  Guy Pujolle,et al.  Spectrum mobility management in cognitive two-tier networks , 2018, Int. J. Netw. Manag..

[19]  Ephraim Zehavi,et al.  Stable matching for channel access control in cognitive radio systems , 2010, 2010 2nd International Workshop on Cognitive Information Processing.

[20]  KumarKrishan,et al.  Spectrum handoff in cognitive radio networks , 2016 .

[21]  Qiang Ni,et al.  Radio Resource Allocation in Collaborative Cognitive Radio Networks Based on Primary Sensing Profile , 2018, IEEE Access.

[22]  Mohammad Mehedi Hassan,et al.  Probability-Based Centralized Device for Spectrum Handoff in Cognitive Radio Networks , 2019, IEEE Access.

[23]  Chung-Ju Chang,et al.  Optimal Target Channel Sequence Design for Multiple Spectrum Handoffs in Cognitive Radio Networks , 2012, IEEE Transactions on Communications.

[24]  Shahzad A. Malik,et al.  Design and Evaluation of Self Organizing, Collision Free MAC Protocol for Distributed Cognitive Radio Networks , 2018, Wirel. Pers. Commun..

[25]  Prince Semba Yawada,et al.  Intelligent Process of Spectrum Handoff/Mobility in Cognitive Radio Networks , 2019, J. Electr. Comput. Eng..

[26]  Halim Yanikomeroglu,et al.  Access Strategies for Spectrum Sharing in Fading Environment: Overlay, Underlay, and Mixed , 2010, IEEE Transactions on Mobile Computing.

[27]  Bin-Jie Hu,et al.  A Fair Multi-Channel Assignment Algorithm With Practical Implementation in Distributed Cognitive Radio Networks , 2018, IEEE Access.

[28]  H. Vincent Poor,et al.  Spectral and Energy Efficiency Trade-offs in Cellular Networks , 2013, IEEE Transactions on Wireless Communications.

[29]  Abdorasoul Ghasemi,et al.  A congestion-game based scheme for handoff management in cognitive radio networks , 2013, 2013 21st Iranian Conference on Electrical Engineering (ICEE).

[30]  Masoumeh Nasiri-Kenari,et al.  Optimal Probabilistic Initial and Target Channel Selection for Spectrum Handoff in Cognitive Radio Networks , 2015, IEEE Transactions on Wireless Communications.

[31]  Athanasios D. Panagopoulos,et al.  Spectrum Leasing in Cognitive Radio Networks: A Matching Theory Approach , 2015, 2015 IEEE 81st Vehicular Technology Conference (VTC Spring).

[32]  Mona Shokair,et al.  Backup Channel Selection Approach for Spectrum Handoff in Cognitive Radio Networks , 2018, 2018 13th International Conference on Computer Engineering and Systems (ICCES).

[33]  Brian M. Sadler,et al.  Opportunistic Spectrum Access via Periodic Channel Sensing , 2008, IEEE Transactions on Signal Processing.

[34]  Umberto Spagnolini,et al.  Packet-wise vertical handover for unlicensed multi-standard spectrum access with cognitive radios , 2008, IEEE Transactions on Wireless Communications.