Autonomous Transmission Power Decision Strategy for Energy Efficient Operation of a Dense Small Cell Network

Smart interference management methods are required to enhance the throughput, coverage, and energy efficiency of a dense small cell network. In this paper, we propose a transmit power control for energy efficient operation of a dense small cell network. We cast the power control problem as a noncooperative game to satisfy the design requirement that small cells do not need any information exchange among them. We analyze the sufficient condition for the existence of a Nash equilibrium (NE) state of the proposed game. We also analyze that the NE state is unique by transforming the original nonlinear fractional programming problem into a nonlinear parametric programming problem. Through simulation studies, we verify our analysis results. In addition, we show that the proposed method achieves higher energy efficiency of a network and balances the energy efficiency among cells more evenly than the methods based on the AIMD (additive increase and multiplicative decrease) algorithm.

[1]  Chuan Heng Foh,et al.  Low-complexity green scheduling for the coordinated downlink of HetNet system , 2015, 2015 IEEE 20th International Workshop on Computer Aided Modelling and Design of Communication Links and Networks (CAMAD).

[2]  Victor C. M. Leung,et al.  Energy Efficient User Association and Power Allocation in Millimeter-Wave-Based Ultra Dense Networks With Energy Harvesting Base Stations , 2017, IEEE Journal on Selected Areas in Communications.

[3]  Mustafa Cenk Gursoy,et al.  Energy-Efficient Power Control in Fading Channels With Markovian Sources and QoS Constraints , 2016, IEEE Transactions on Communications.

[4]  Ming Xiao,et al.  Millimeter Wave Communications for Future Mobile Networks (Guest Editorial), Part I , 2017, IEEE J. Sel. Areas Commun..

[5]  Tho Le-Ngoc,et al.  Energy-Efficient Power Adaptation over a Frequency-Selective Fading Channel with Delay and Power Constraints , 2013, IEEE Transactions on Wireless Communications.

[6]  Luis Alonso,et al.  Multiobjective Auction-Based Switching-Off Scheme in Heterogeneous Networks: To Bid or Not to Bid? , 2016, IEEE Transactions on Vehicular Technology.

[7]  Amr M. Youssef,et al.  Ultra-Dense Networks: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[8]  Muhammad Ali Imran,et al.  How much energy is needed to run a wireless network? , 2011, IEEE Wireless Communications.

[9]  Lei Liu,et al.  Interference Management in Ultra-Dense Networks: Challenges and Approaches , 2017, IEEE Network.

[10]  Tarcisio F. Maciel,et al.  Massive MIMO: survey and future research topics , 2016, IET Commun..

[11]  Xiaohu Ge,et al.  On the Energy-Efficient Deployment for Ultra-Dense Heterogeneous Networks With NLoS and LoS Transmissions , 2018, IEEE Transactions on Green Communications and Networking.

[12]  Mohamed-Slim Alouini,et al.  Energy-efficient power control for OFDMA cellular networks , 2016, 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[13]  N. P. Kumar Energy-Efficient Resource Allocation in OFDMA Systems with Large Numbers of Base Station Antennas , 2017 .

[14]  Alagan Anpalagan,et al.  Interference-Aware Energy Efficiency Maximization in 5G Ultra-Dense Networks , 2017, IEEE Transactions on Communications.

[15]  Jeffrey G. Andrews,et al.  What Will 5G Be? , 2014, IEEE Journal on Selected Areas in Communications.

[16]  Nurul H. Mahmood,et al.  Interference Coordination for 5G New Radio , 2018, IEEE Wireless Communications.

[17]  Matti Latva-aho,et al.  Ultra Dense Small Cell Networks: Turning Density Into Energy Efficiency , 2016, IEEE Journal on Selected Areas in Communications.

[18]  Xianfu Chen,et al.  Optimal Base Station Sleeping in Green Cellular Networks: A Distributed Cooperative Framework Based on Game Theory , 2015, IEEE Transactions on Wireless Communications.

[19]  David Grace,et al.  Traffic-Aware Cell Management for Green Ultradense Small-Cell Networks , 2017, IEEE Transactions on Vehicular Technology.

[20]  Behrouz Maham,et al.  Energy Efficient Price Based Power Allocation in a Small Cell Network by Using a Stackelberg Game , 2018, 2018 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom).

[21]  Victor C. M. Leung,et al.  Downlink Energy Efficiency of Power Allocation and Wireless Backhaul Bandwidth Allocation in Heterogeneous Small Cell Networks , 2017, IEEE Transactions on Communications.

[22]  Wen Wang,et al.  A Cluster-Based Energy-Efficient Resource Management Scheme for Ultra-Dense Networks , 2016, IEEE Access.

[23]  Werner Dinkelbach On Nonlinear Fractional Programming , 1967 .

[24]  Ali Imran,et al.  Coordinated Multi-Point Clustering Schemes: A Survey , 2017, IEEE Communications Surveys & Tutorials.

[25]  H. Vincent Poor,et al.  Downlink Beamforming for Energy-Efficient Heterogeneous Networks With Massive MIMO and Small Cells , 2018, IEEE Transactions on Wireless Communications.

[26]  Xiaohu You,et al.  Energy-Efficient Noncooperative Power Control in Small-Cell Networks , 2017, IEEE Transactions on Vehicular Technology.

[27]  F. Richard Yu,et al.  A Joint Cross-Layer and Colayer Interference Management Scheme in Hyperdense Heterogeneous Networks Using Mean-Field Game Theory , 2016, IEEE Transactions on Vehicular Technology.

[28]  Balasubramaniam Natarajan,et al.  Small Cell Base Station Sleep Strategies for Energy Efficiency , 2016, IEEE Transactions on Vehicular Technology.

[29]  Gozde Ozcan,et al.  QoS-Driven Energy-Efficient Power Control With Random Arrivals and Arbitrary Input Distributions , 2017, IEEE Transactions on Wireless Communications.