Power allocation analysis for dynamic power utility in cognitive radio systems

The focus of this paper is to investigate the fundamental limits of power allocation when taking into account a dynamic power pricing scheme. This paper proposes an optimal power allocation analysis for wireless systems when real time power pricing is available. We propose to minimize the total power consumption cost while ensuring minimum individual and total throughput limits. We consider different models for the power pricing function. Analytic solutions for the power allocation are derived for each model. The derived solutions are shown to be modified versions of the water-filling solution. Low-complexity algorithms are proposed for the resource allocation with each pricing model. Performance comparison and pricing effect are shown through simulations.

[1]  Mohsen Guizani,et al.  Analyzing Cognitive Network Access Efficiency Under Limited Spectrum Handoff Agility , 2014, IEEE Transactions on Vehicular Technology.

[2]  Nico M. Temme,et al.  Numerical methods for special functions , 2007 .

[3]  Bechir Hamdaoui,et al.  Efficient Objective Functions for Coordinated Learning in Large-Scale Distributed OSA Systems , 2013, IEEE Transactions on Mobile Computing.

[4]  Arafat J. Al-Dweik,et al.  Opportunistic Spectrum Access in Cognitive Radio Networks Under Imperfect Spectrum Sensing , 2014, IEEE Transactions on Vehicular Technology.

[5]  Bechir Hamdaoui Adaptive spectrum assessment for opportunistic access in cognitive radio networks , 2009, IEEE Transactions on Wireless Communications.

[6]  Chung-Ju Chang,et al.  Modeling and Analysis for Spectrum Handoffs in Cognitive Radio Networks , 2012, IEEE Transactions on Mobile Computing.

[7]  Dawei Wang,et al.  Optimal Power Allocation for Hybrid Overlay/Underlay Spectrum Sharing in Multiband Cognitive Radio Networks , 2013, IEEE Transactions on Vehicular Technology.

[8]  Theodore Antonakopoulos,et al.  Bit and Power Allocation in Constrained Multicarrier Systems: The Single-User Case , 2008, EURASIP J. Adv. Signal Process..

[9]  Jiandong Li,et al.  Optimal Power Control for Cognitive Radio Networks Under Coupled Interference Constraints: A Cooperative Game-Theoretic Perspective , 2010, IEEE Transactions on Vehicular Technology.

[10]  Mohsen Guizani,et al.  Distributed dynamic spectrum access with adaptive power allocation: Energy efficiency and cross-layer awareness , 2014, 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[11]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[12]  Kang G. Shin,et al.  OS-MAC: An Efficient MAC Protocol for Spectrum-Agile Wireless Networks , 2008, IEEE Transactions on Mobile Computing.

[13]  Vijay K. Bhargava,et al.  Optimal and Suboptimal Power Allocation Schemes for OFDM-based Cognitive Radio Systems , 2008, IEEE Transactions on Wireless Communications.

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

[15]  Mohsen Guizani,et al.  Opportunistic Bandwidth Sharing Through Reinforcement Learning , 2010, IEEE Transactions on Vehicular Technology.

[16]  Sudhir Singh,et al.  Power allocation in underlay cognitive radio systems with feasibility detection , 2012, 2012 Australian Communications Theory Workshop (AusCTW).

[17]  Geoffrey Ye Li,et al.  Fundamental trade-offs on green wireless networks , 2011, IEEE Communications Magazine.