Research Of Critical Technologies For Enhanced Inter-Cell Interference Coordination In LTE-Advanced Heterogeneous Networks

On the basis of OFDM and MIMO technologies, 3GPP LTE dedicates to fulfill the requirements of lower transmission delay and higher transmission rate. Considering that people focus more on scenarios of indoor, hotspot, low velocity movement and high data rate in Mobile Internet Era, LTE-Advanced introduces Low Power Nodes, which form Heterogeneous Networks with Macro e Node Bs together, to improve user experience and wireless coverage. Such new networking pattern can meet the increasing demand of communication quality and frequency reuse efficiency, but accompanying technical problems are complicated and to be resolved. This paper aims to solve one of the critical problems in LTE-A Heterogeneous Networks, namely, enhanced Inter-Cell Interference Cooperation(e ICIC) technology.Firstly, we analyze the network architecture, inter-cell interference issues, interference management methods of Release 8~11, and principals of e ICIC. Secondly, overseas and domestic research situation of e ICIC are investigated, with Macro-Pico Heterogeneous Networks especially. Emphasis and difficulties of various algorithms or schemes are summarized. Moreover, several existing e ICIC algorithms, which have different focuses from one another, are simulated. We also discuss the crucial issues which the proposers have not considered or elaborated.As for the most significant contribution, we propose a distributed resource optimization algorithm based on e ICIC, which is applicable to LTE-A Macro-Pico Heterogeneous Networks. We formulate the joint optimization problem of resource allocation strategy in each e Node B, cell selection of users, Cell Range Expansion bias adaption, and optimal Almost Blank Sub-frame configuration. We apply the block coordinate descent(BCD) method to decouple the joint problem into sub-problems and solve them in an alternating manner, including some heuristic methods. Simulation result shows that our algorithm performs much better than conventional approaches in many performance indicators, such as network-wide load balancing, system-wide average throughput, and service fairness of users. In addition, our method can adapt to different deployment conditions with no need for any predefined threshold.