基于干扰对齐的认知无线电网络中用户性能的优化设计

摘要

Interference alignment (IA) is a promising interference management technology,which is widely used in cognitive radio (CR) networks,in order to eliminate the interference from cognitive users (CU) to authorized users (AU).However,IA will cause the decrease in the received signal-to-interference-plus-noise ratio (SINR) of AU,which affects its transmission rate.Especially at low SNR,the performance of AU is not guaranteed.According to different scenarios,this paper innovatively proposed two algorithms to design precoders for AU to optimize its transmission rate,the proposed algorithms improve the performance of AU tremendously.Moreover,to improve the CUs' performance,this paper proposed an optimum scheme for CUs' decoding filters to maximize their transmission rates.The simulation results show that the AU's transmission rate can be improved through using the proposed two algorithms,while CUs' transmission rates are also guaranteed.%干扰对齐(IA)是一种新兴的干扰管理技术,被广泛的应用于认知无线电(CR)网络之中,以期消除认知用户(CU)对授权用户(AU)的干扰.然而干扰对齐技术带来了授权用户接收信干噪比(SINR)的下降,使得授权用户的传输速率受到影响.在低信噪比下,授权用户性能无法保证.本文针对不同的场景,创新性地提出了两种算法设计授权用户预编码滤波器,优化其传输速率,极大地提高了授权用户的性能.同时,为了提高认知用户的性能,本文给出了认知用户的干扰抑制滤波器的优化方案,最大化认知用户的和速率.仿真结果表明,这两种算法都能在一定程度上提高授权用户的传输速率,同时认知用户的传输速率也得以保证.