超密集蜂巢網路之小型基地台佈建規劃

Deployment of Ultra-dense Small Cells on Cellular Networks

王志勝(Chih-Sheng Wang)

指導教授 ： 逄愛君

國立臺灣大學/電機資訊學院/資訊工程學研究所/碩士(2015年)

https://doi.org/10.6342/NTU.2015.01974

摘要

由於近年來行動網路的流量需求日益增加，5G 網路的經營者必須在有限的資源下去想辦法改善網路覆蓋 (network coverage) 和網路容量(network capacity)，而佈建超密集小型基地台 (ultra-dense small cells) 這
種作法則是目前被視作一個很好的改善方式。以小型基地台而言，它的網路覆蓋和網路容量是分別由其導頻功率 (pilot power) 和流量功率(traffic power) 所影響，而這兩種功率的總和會小於一個小型基地台所能提供的總功率。因此，如何去好好處理、抉擇取捨這兩種功率的分配是一個在佈建超密集小型基地台中很重要且具有挑戰性的議題。本篇論文著重於在 5G 網路中佈建超密集小型基地台的網路覆蓋和網路容量的最佳化問題。我們將我們的目標問題制定成一個多目標最佳化
的問題並且藉由拉格朗日法 (Lagrangian method) 去找出一個分散式最佳化演算法來解決我們的問題。最後的模擬實驗則是去評估我們演算法的表現及效能，而實驗結果證實了我們的演算法和基線方法相比，確實能夠有效的去改善網路覆蓋和網路容量。

關鍵字

小型基地台 ； 覆蓋優化 ； 容量優化 ； 多目標問題 ； 凸優化

並列摘要

To support the rapid growth of mobile data traffic, deploying ultra-dense small cells is regarded as a promising solution for 5G operators to improve network coverage and capacity with low cost. The network coverage and capacity
of a small cell are respectively affected by its pilot power and traffic power, and the summation of the two power quantities is bounded by the total transmit power which depends on the power supply of the small cell. Thus, how to deal with the tradeoff between the pilot and the traffic power allocation is one of the important and challenging issues for ultra-dense small cell deployment. This paper studies the network coverage and capacity joint optimization problem for ultra-dense small cell deployment in 5G cellular
systems. We formulate the target problem as a multi-objective optimization problem and propose a distributed optimal algorithm by Lagrangian method to tackle the problem. We conduct a series of simulations to evaluate the performance of the proposed algorithm, and the simulation results confirm that our proposed algorithm can significantly improve the network coverage and
capacity, compared with the baseline approach.

並列關鍵字

Ultra-dense Small cells ； Coverage optimization ； Capacity optimization ； Multi-objective ； convex optimization

參考文獻

[5] L. T. Ho, I. Ashraf, and H. Claussen. Evolving femtocell coverage optimization algorithms

using genetic programming. In Personal, Indoor and Mobile Radio Communications,

[6] I. Ashraf, H. Claussen, and L. T. Ho. Distributed radio coverage optimization in

[7] L. Huang, Y. Zhou, X. Han, Y. Wang, M. Qian, and J. Shi. Distributed coverage

optimization for small cell clusters using game theory. In Wireless Communications