Joint Optimization of Interference Coordination Parameters and Base-Station Density for Energy-Efficient Heterogeneous Networks

面向节能异构网络的干扰协调参数与基站密度联合优化

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Abstract

Heterogeneous networks (HetNets), consisting of macro-cells and overlaying pico-cells, have been recognized as a promising paradigm to support the exponential growth of data traffic demands and high network energy efficiency (EE). However, for two-tier heterogeneous architecture deployment of HetNets, the inter-tier interference will be challenging. Time domain further-enhanced inter-cell interference coordination (FeICIC) proposed in 3GPP Release-11 becomes necessary to mitigate the inter-tier interference by applying low power almost blank subframe (ABS) scheme. Therefore, for HetNets deployment in reality, the pico-cell range expansion (CRE) bias, the power of ABS and the density of pico base stations (PBSs) are three important factors for the network EE improvement. Aiming to improve the network EE, the above three factors are jointly considered in this paper. In particular, we first derive the closed-form expression of the network EE as a function of pico CRE bias, power reduction factor of low power ABS and PBS density based on stochastic geometry model. Then, the approximate relationship between pico CRE bias and power reduction factor is deduced, followed by a linear search algorithm to get the near-optimal pico CRE bias and power reduction factor together at a given PBS density. Next, a linear search algorithm is further proposed to optimize PBS density based on fixed pico CRE bias and power reduction factor. Due to the fact that the above pico CRE bias and power reduction factor optimization and PBS density optimization are optimized separately, a heuristic algorithm is further proposed to optimize pico CRE bias, power reduction factor and PBS density jointly to achieve global network EE maximization. Numerical simulation results show that our proposed heuristic algorithm can significantly enhance the network EE while incurring low computational complexity.

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