Dynamic Base Station Sleep Control via Submodular Optimization for Green mmWave Networks

This paper proposes a dynamic millimeter-wave (mmWave) base station (BS) sleep control scheme for green mmWave networks. The typical coverage radius of mmWave BS is short due to high propagation and shadowing loss, thus large number of BSs are required to be deployed densely. A network consisting of many BSs consumes large energy. Sleep and activation control is a promising technique to reduce energy consumption. However, to select a set of BSs to sleep from large number of BSs to maximize total throughput under on condition that the total energy consumption of the network is limited is a NP-hard problem and it requires huge computation time. This paper formulates sleep control based on submodular optimization which can be solved quickly by using a greedy algorithm and the performance in the worst case is guaranteed to be \((1-\mathrm {e}^{-1})\)-approximation. We design a utility function defined as total expected rate for mmWave access networks in consideration of the characteristics of mmWave communication, and prove that it is submodular and monotone. The sleep and activation control of mmWave BSs is formulated as a combinatorial optimization problem to maximize a monotone submodular function under the constraint that the number of BSs to be activated is limited due to energy constraints. Simulation results confirmed that the proposed scheme obtains a BS set achieving higher throughput than random selection and the scheme is polynomial time algorithm.

[1]  Zhisheng Niu,et al.  Toward dynamic energy-efficient operation of cellular network infrastructure , 2011, IEEE Communications Magazine.

[2]  Chia-Chin Chong,et al.  Millimeter-Wave Wireless Communication Systems: Theory and Applications , 2007, EURASIP J. Wirel. Commun. Netw..

[3]  Marco Luise,et al.  Mobile and Personal Communications in the 60 GHz Band: A Survey , 1999, Wirel. Pers. Commun..

[4]  Chia-Chin Chong,et al.  An Overview of Multigigabit Wireless through Millimeter Wave Technology: Potentials and Technical Challenges , 2007, EURASIP J. Wirel. Commun. Netw..

[5]  Zhang Chao,et al.  Green Mobile Access Network with Dynamic Base Station Energy Saving , 2009 .

[6]  Song Guo,et al.  Tree-Based Distributed Multicast Algorithms for Directional Communications and Lifetime Optimization in Wireless Ad Hoc Networks , 2007, EURASIP J. Wirel. Commun. Netw..

[7]  Theodore S. Rappaport,et al.  Millimeter Wave Mobile Communications for 5G Cellular: It Will Work! , 2013, IEEE Access.

[8]  Nan Guo,et al.  60-GHz Millimeter-Wave Radio: Principle, Technology, and New Results , 2007, EURASIP J. Wirel. Commun. Netw..

[9]  M. L. Fisher,et al.  An analysis of approximations for maximizing submodular set functions—I , 1978, Math. Program..

[10]  Robert W. Heath,et al.  Analysis of Blockage Effects on Urban Cellular Networks , 2013, IEEE Transactions on Wireless Communications.

[11]  Masahiro Morikura,et al.  Proactive Base Station Selection Based on Human Blockage Prediction Using RGB-D Cameras for mmWave Communications , 2014, GLOBECOM 2014.

[12]  Majid Ghaderi,et al.  Energy cost reduction in cellular networks through dynamic base station activation , 2014, 2014 Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).

[13]  Masahiro Morikura,et al.  Proactive Handover Based on Human Blockage Prediction Using RGB-D Cameras for mmWave Communications , 2016, IEICE Trans. Commun..

[14]  Maxim Sviridenko,et al.  A note on maximizing a submodular set function subject to a knapsack constraint , 2004, Oper. Res. Lett..

[15]  Laurent Dussopt,et al.  Millimeter-wave access and backhauling: the solution to the exponential data traffic increase in 5G mobile communications systems? , 2014, IEEE Communications Magazine.

[16]  Andreas Krause,et al.  Submodularity and its applications in optimized information gathering , 2011, TIST.