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基于增强型小区间干扰协调的异构网络节能微微小区覆盖范围扩展与密度联合优化

Energy Efficient Pico Cell Range Expansion and Density Joint Optimization for Heterogeneous Networks with eICIC.

作者信息

Sun Yanzan, Xia Wenqing, Zhang Shunqing, Wu Yating, Wang Tao, Fang Yong

机构信息

Shanghai Institute for Advanced Communication and Data Science, Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai 200072 China.

出版信息

Sensors (Basel). 2018 Mar 2;18(3):762. doi: 10.3390/s18030762.

DOI:10.3390/s18030762
PMID:29498701
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5876702/
Abstract

Heterogeneous networks, constituted by conventional macro cells and overlaying pico cells, have been deemed a promising paradigm to support the deluge of data traffic with higher spectral efficiency and Energy Efficiency (EE). In order to deploy pico cells in reality, the density of Pico Base Stations (PBSs) and the pico Cell Range Expansion (CRE) are two important factors for the network spectral efficiency as well as EE improvement. However, associated with the range and density evolution, the inter-tier interference within the heterogeneous architecture will be challenging, and the time domain Enhanced Inter-cell Interference Coordination (eICIC) technique becomes necessary. Aiming to improve the network EE, the above factors are jointly considered in this paper. More specifically, we first derive the closed-form expression of the network EE as a function of the density of PBSs and pico CRE bias based on stochastic geometry theory, followed by a linear search algorithm to optimize the pico CRE bias and PBS density, respectively. Moreover, in order to realize the pico CRE bias and PBS density joint optimization, a heuristic algorithm is proposed to achieve the network EE maximization. Numerical simulations show that our proposed pico CRE bias and PBS density joint optimization algorithm can improve the network EE significantly with low computational complexity.

摘要

由传统宏小区和叠加的微微小区组成的异构网络,被视为一种有前景的范例,可通过更高的频谱效率和能量效率(EE)来支持海量数据流量。为了在实际中部署微微小区,微微基站(PBS)的密度和微微小区范围扩展(CRE)是提高网络频谱效率和EE的两个重要因素。然而,随着范围和密度的演变,异构架构内的层间干扰将具有挑战性,时域增强型小区间干扰协调(eICIC)技术变得必要。本文旨在提高网络EE,联合考虑上述因素。更具体地说,我们首先基于随机几何理论推导网络EE作为PBS密度和微微CRE偏置函数的闭式表达式,随后分别采用线性搜索算法来优化微微CRE偏置和PBS密度。此外,为了实现微微CRE偏置和PBS密度的联合优化,提出了一种启发式算法以实现网络EE最大化。数值模拟表明,我们提出的微微CRE偏置和PBS密度联合优化算法能够以低计算复杂度显著提高网络EE。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30cc/5876702/8979784bafbe/sensors-18-00762-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30cc/5876702/aba836c72f59/sensors-18-00762-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30cc/5876702/474446641603/sensors-18-00762-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30cc/5876702/1a7a1e1e32d6/sensors-18-00762-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30cc/5876702/5e20194d63f1/sensors-18-00762-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30cc/5876702/dd2d465bec09/sensors-18-00762-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30cc/5876702/e57c1059e1be/sensors-18-00762-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30cc/5876702/897be3f5ad8c/sensors-18-00762-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30cc/5876702/8979784bafbe/sensors-18-00762-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30cc/5876702/aba836c72f59/sensors-18-00762-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30cc/5876702/474446641603/sensors-18-00762-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30cc/5876702/1a7a1e1e32d6/sensors-18-00762-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30cc/5876702/5e20194d63f1/sensors-18-00762-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30cc/5876702/dd2d465bec09/sensors-18-00762-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30cc/5876702/e57c1059e1be/sensors-18-00762-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30cc/5876702/897be3f5ad8c/sensors-18-00762-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30cc/5876702/8979784bafbe/sensors-18-00762-g008.jpg

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