认知MIMO系统基于有限反馈的干扰信道学习算法

Limited feedback based learning algorithm of interference channel in cognitive MIMO system

针对认知多输入多输出(MIMO)系统中的干扰信道学习问题进行研究,提出了一种基于有限反馈的干扰信道学习算法。算法用干扰信道的零空间代替整个信道矩阵作为反馈量,在推导最优零空间码字选择准则的基础上,分析干扰信道零空间量化结果的时间相关性,并在前一帧量化结果上加一个基于旋转码本的扰动构建当前帧零空间码本。为防止量化误差的传播,进一步推导了旋转量参数的更新。理论分析和仿真结果表明,所提算法在慢变MIMO干扰信道下对零空间的跟踪效果比Grassmannian子空间包码本好。

A limited feedback based learning algorithm of interference channel is proposed to tackle the issue of interference channel learning in cognitive Multi Input Multi Output(MIMO) system. The null space of interference channel instead of the whole channel matrix is fed back from the primary receiver to secondary transmitter. The time correlation of quantization result of interference channel null space is analyzed based on the derivation of null space codeword selection criteria, and the null space codebook of current frame is constructed by perturbing the quantization result of previous frame based on a rotation codebook. Furthermore,to avoid the propagation of quantization error,the update of rotation parameter is derived. Theoretical analysis and simulation results indicate that proposed algorithm tracks the null space of slow time-varying MIMO interference channel better than the Grassmannian Subspace Packing codebook.