Energy-efficient cognitive heterogeneous networks powered by the smart grid

Rapidly rising energy costs and increasingly rigid environmental standards have led to an emerging trend of addressing the “energy efficiency” aspect of mobile cellular networks. Cognitive heterogeneous mobile networks are considered as important techniques to improve the energy efficiency. However, most existing works do not consider the power grid, which provides electricity to cellular networks. Currently, the power grid is experiencing a significant shift from the traditional grid to the smart grid. In the smart grid environment, only considering energy efficiency may not be sufficient, since the dynamics of the smart grid will have significant impacts on mobile networks. In this paper, we study cognitive heterogeneous mobile networks in the smart grid environment. Unlike most existing studies on cognitive networks, where only the radio spectrum is sensed, our cognitive networks sense not only the radio spectrum environment but also the smart grid environment, based on which power allocation and interference management are performed. We formulate the problems of electricity price decision, energy-efficient power allocation and interference management as a three-level Stackelberg game. A homogeneous Bertrand game with asymmetric costs is used to model price decisions made by the electricity retailers. A backward induction method is used to analyze the proposed Stackelberg game. Simulation results show that our proposed scheme can significantly reduce operational expenditure and CO2 emissions in cognitive heterogeneous mobile networks.

[1]  Weidong Xiao,et al.  Communication systems for grid integration of renewable energy resources , 2011, IEEE Network.

[2]  F. Richard Yu,et al.  Energy-Efficient Resource Allocation for Heterogeneous Cognitive Radio Networks with Femtocells , 2012, IEEE Transactions on Wireless Communications.

[3]  Sudarshan Guruacharya,et al.  Hierarchical Competition in Femtocell-Based Cellular Networks , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[4]  F. Richard Yu,et al.  Spectrum sharing and resource allocation for energy-efficient heterogeneous cognitive radio networks with femtocells , 2012, 2012 IEEE International Conference on Communications (ICC).

[5]  Mario Pickavet,et al.  Energy efficiency in communications , 2010 .

[6]  Gürkan Gür,et al.  Green wireless communications via cognitive dimension: an overview , 2011, IEEE Network.

[7]  Vincent W. S. Wong,et al.  Optimal Real-Time Pricing Algorithm Based on Utility Maximization for Smart Grid , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[8]  Weihua Zhuang,et al.  Network cooperation for energy saving in green radio communications , 2011, IEEE Wireless Communications.

[9]  Daniel Pérez Palomar,et al.  A tutorial on decomposition methods for network utility maximization , 2006, IEEE Journal on Selected Areas in Communications.

[10]  Gilbert M. Masters,et al.  Renewable and Efficient Electric Power Systems: Masters/Electric Power Systems , 2004 .

[11]  M. Nicolosi Wind power integration and power system flexibility–An empirical analysis of extreme events in Germany under the new negative price regime , 2010 .

[12]  F. Richard Yu,et al.  Biologically inspired consensus-based spectrum sensing in mobile Ad Hoc networks with cognitive radios , 2010, IEEE Network.

[13]  Juan M. Morales,et al.  Real-Time Demand Response Model , 2010, IEEE Transactions on Smart Grid.

[14]  Holger Claussen,et al.  Improving Energy Efficiency of Femtocell Base Stations Via User Activity Detection , 2010, 2010 IEEE Wireless Communication and Networking Conference.

[15]  Joseph Mitola,et al.  Cognitive Radio An Integrated Agent Architecture for Software Defined Radio , 2000 .

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

[17]  Stephen P. Boyd Convex optimization: from embedded real-time to large-scale distributed , 2011, KDD.

[18]  Kwang-Cheng Chen,et al.  Cognitive and Game-Theoretical Radio Resource Management for Autonomous Femtocells with QoS Guarantees , 2011, IEEE Transactions on Wireless Communications.

[19]  Gilbert M. Masters,et al.  Renewable and Efficient Electric Power Systems , 2004 .

[20]  F. Richard Yu,et al.  Stochastic unit commitment in smart grid communications , 2011, 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[21]  Victor C. M. Leung Green Communications and Networking , 2012 .

[22]  Y. Narahari,et al.  Game Theoretic Problems in Network Economics and Mechanism Design Solutions , 2009, Advanced Information and Knowledge Processing.

[23]  Dogan Keles,et al.  Comparison of extended mean-reversion and time series models for electricity spot price simulation considering negative prices , 2012 .

[24]  Jeffrey G. Andrews,et al.  Power control in two-tier femtocell networks , 2008, IEEE Transactions on Wireless Communications.

[25]  Vijay K. Bhargava,et al.  Green Cellular Networks: A Survey, Some Research Issues and Challenges , 2011, IEEE Communications Surveys & Tutorials.

[26]  Geoffrey Ye Li,et al.  Interference-Aware Energy-Efficient Power Optimization , 2009, 2009 IEEE International Conference on Communications.

[27]  F. Genoese,et al.  Occurrence of negative prices on the German spot market for electricity and their influence on balancing power markets , 2010, 2010 7th International Conference on the European Energy Market.

[28]  Ling Qiu,et al.  Improving energy efficiency through multimode transmission in the downlink MIMO systems , 2011, EURASIP J. Wirel. Commun. Netw..