Dynamic decision-based spectrum sharing framework for next-generation (5G) systems

Looking into the concept of next-generation (5G) cellular systems, it is necessary to do a revision of existing radio spectrum management techniques and come up with more flexible solutions. A new wave of spectrum policy reforms can be envisaged with a direction shift from static to dynamic optimization. According to the peak hours, the number of served users in mobile networks is increasing. Since the radio spectrum is limited, cognitive radio (CR) technology provides an opportunity to recognize under-utilized cellular spectrum (licensed band) resources. To this end, efficient spectrum management techniques based on CR technology should be implemented to share the spectrum between different types of users in order to maximize spectrum utilization and spectral efficiency. In this work, we present dynamic decision-based spectrum sharing model among multiple classes of users in CR network (CRN) in order to increase network utilization and the quality of experience (QoE) by increasing the users' satisfaction. Obtained simulation results from created toolkit in Matlab tool (calibrated by data set from real 3GGP LTE-Advanced system) show the performance of the developed model and appropriate user selection among multiple users' types.

[1]  N. Bhushan,et al.  Strategic Decision Making: Applying the Analytic Hierarchy Process , 2004 .

[2]  Sarangapani Jagannathan,et al.  Cooperative Resource Allocation for Primary and Secondary Users with Adjustable Priorities in Cognitive Radio Networks , 2011 .

[3]  Metin Kaplan,et al.  A dynamic spectrum decision scheme for heterogeneous cognitive radio networks , 2009, 2009 24th International Symposium on Computer and Information Sciences.

[4]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.

[5]  Aleksandr Ometov,et al.  3GPP LTE‐Assisted Wi‐Fi‐Direct: Trial Implementation of Live D2D Technology , 2015 .

[6]  Balasubramaniam Natarajan,et al.  QoS constrained resource allocation to secondary users in cognitive radio networks , 2009, Comput. Commun..

[7]  Ian F. Akyildiz,et al.  A survey on spectrum management in cognitive radio networks , 2008, IEEE Communications Magazine.

[8]  Basem Shihada,et al.  Adaptive Decision-Making Scheme for Cognitive Radio Networks , 2014, 2014 IEEE 28th International Conference on Advanced Information Networking and Applications.

[9]  Balasubramaniam Natarajan,et al.  Modeling fairness in resource allocation for secondary users in a competitive cognitive radio network , 2010, 2010 Wireless Telecommunications Symposium (WTS).

[10]  Tugrul Cavdar,et al.  Instant overbooking framework for cognitive radio networks , 2015 .

[11]  Thomas L. Saaty,et al.  Models, Methods, Concepts & Applications of the Analytic Hierarchy Process , 2012 .

[12]  C.E. Shannon,et al.  Communication in the Presence of Noise , 1949, Proceedings of the IRE.

[13]  Thomas L. Saaty,et al.  DECISION MAKING WITH THE ANALYTIC HIERARCHY PROCESS , 2008 .