A Network Graph Approach for Network Energy Saving in Small Cell Networks

Small cell networks are key components in 5G networks to boost the network capacity, improve spectrum and energy efficiency, and enable flexible and new services. Due to the flexible spectrum access among and flexible deployment of small cells, the inter- cell coordination becomes critical for the performance of the network. In this paper, based on the key concept in software defined networking (SDN) for Internet, we first introduce the network graph approach as a tool for the control and coordination among small cells. The network graph is constructed from the abstracted network state information extracted from underlying base stations. It shields the logical centralized control unit from implementation details of the underlying physical layer and thus reduces the control overhead in a centralized solution. We use the network graph for network energy saving in small cell networks, in which network graphs are used to decide the optimal set of small cells in the network. For cells outside this set we can switch them off for energy saving. We propose three types of network graphs with different network state details. Based on these graphs, we formulate the energy saving problem as an integer linear programming (ILP) problem, and propose the practical algorithms to solve the problem. The performance of the algorithms are studied by simulation. It shows the potential of the proposed network graph approach for the inter-cell resource coordination in small cell networks.

[1]  Xianfu Chen,et al.  Energy-Efficiency Oriented Traffic Offloading in Wireless Networks: A Brief Survey and a Learning Approach for Heterogeneous Cellular Networks , 2015, IEEE Journal on Selected Areas in Communications.

[2]  Zheng Chang,et al.  Energy efficient resource allocation in heterogeneous software defined network: A reverse combinatorial auction approach , 2015, 2015 IEEE/CIC International Conference on Communications in China (ICCC).

[3]  Xianfu Chen,et al.  SoftMobile: control evolution for future heterogeneous mobile networks , 2014, IEEE Wireless Communications.

[4]  N. S. Mendelsohn,et al.  Coverings of Bipartite Graphs , 1958, Canadian Journal of Mathematics.

[5]  Hyundong Shin,et al.  Energy Efficient Heterogeneous Cellular Networks , 2013, IEEE Journal on Selected Areas in Communications.

[6]  Jeffrey G. Andrews,et al.  Femtocells: Past, Present, and Future , 2012, IEEE Journal on Selected Areas in Communications.

[7]  Leandros Tassiulas,et al.  Dynamic Resource Provisioning for Energy Efficiency in Wireless Access Networks: A Survey and an Outlook , 2014, IEEE Communications Surveys & Tutorials.

[8]  Mianxiong Dong,et al.  Energy-Efficient Context-Aware Matching for Resource Allocation in Ultra-Dense Small Cells , 2015, IEEE Access.

[9]  Di Yuan,et al.  Data Offloading in Load Coupled Networks: A Utility Maximization Framework , 2014, IEEE Transactions on Wireless Communications.

[10]  Jeffrey G. Andrews,et al.  Joint Resource Partitioning and Offloading in Heterogeneous Cellular Networks , 2013, IEEE Transactions on Wireless Communications.

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

[12]  Tapani Ristaniemi,et al.  User-cell association in heterogenous small cell networks: A context-aware approach , 2015, 2015 IEEE/CIC International Conference on Communications in China (ICCC).