蜂窝系统自适应K近邻干扰对齐算法

An Adaptive K-nearest-neighbor Interference Alignment Algorithm in Cellular System

干扰对齐算法应用于蜂窝网络时,存在计算复杂度高、系统开销大等缺点,本文基于分簇模型,提出了一种自适应K近邻干扰对齐算法,并给出一种无需收发端迭代的干扰对齐预编码向量求解方法。该算法依据信干比门限自适应选取参与干扰对齐的干扰信号,对预筛选的干扰信号利用优化方法进行干扰对齐,降低了算法的计算复杂度和系统开销。仿真分析表明,通过选取合适的信干比门限,本文所提算法在干扰消除性能相当的情况下,其算法复杂度和系统开销显著降低,提高了干扰对齐算法的适用性。

For the disadvantages that the high computational complexity and heavy overheads when interference alignment algorithm was applied to the cellular system,this paper,based on the clustered model,proposed an adaptive K-nearest-neighbor interference alignment algorithm and a solving method to get the precoding vector without iteration between the transmitter and the receiver. This algorithm could adaptively choose the interference for interference alignment according to the threshold of the signal-to-interference ratio（ SIR） and align the prechosen interference with the optimal method,which could decrease the computational complexity and system overheads. Simulation results show that by choosing an appropriate threshold of SIR,the proposed algorithm could obtain a quite good performance of interference cancellation while the computational complexity and system overheads could be greatly decreased,which can improved the applicability of interference alignment algorithm.