Optimizing Small Cell Deployment in Arbitrary Wireless Networks with Minimum Service Rate Constraints

• Main Authors: Cheng-Pang Chien, 簡正邦
• Other Authors: 謝宏昀
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/70034206197929239124

碩士 === 國立臺灣大學 === 電信工程學研究所 === 101 === Heterogeneous network with small cells has recently been regarded as a promising scenario for enhancing macrocell coverage and/or capacity in LTE-Advanced systems. While deployment of small cells has typically followed the bottom-up paradigm driven by the ad hoc demand of users, more and more studies have prompted a move towards a more managed deployment model for better tradeoff between performance and cost. Unlike related work that assumes a stochastic distribution model for small cells, in this thesis we consider the deployment problem for arbitrary wireless networks under different network scenarios, including shared and dedicated resource models as well as open and closed access modes for small cells. To proceed, we formulate an optimization problem for small cell deployment in an arbitrary network that involves determination of deployment locations and operation parameters to maximize the supported number of customers with QoS constraints. Since the formulated problem belongs to mixed-integer non-linear programming (MINLP), we propose an anytime algorithm that transforms the joint problem into a cluster formation sub-problem (involving location selection and cell coverage) and a resource management sub-problem (involving power control and resource allocation) for effectively solving all optimization variables in an iterative fashion. Finally, the proposed approach is extended for solving the post-optimization problem where QoS constraints and/or number of customers are changed after initial network planning. Compared with other approaches for small cell deployment, evaluation results show that the proposed algorithm can effectively solve the target problem while striking a better performance tradeoff between computation complexity and solution quality.