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Interference alignment with iterative channel estimation for the reciprocal M × 2 MIMO X Network

机译：互惠M×2 MIMO X网络的带迭代信道估计的干扰对准。

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摘要

This paper investigates an interference alignment (IA) scheme for a reciprocal multi-input multi-output (MIMO)M×2X network where the knowledge of channel state information (CSI) is required. In our proposed approach, singular vectors, calculated from the singular value decomposition (SVD) of channel matrices, are used to compute precoding and zero-forcing (ZF) decoding matrices at transmitters and receivers, respectively. The orthogonality between precoding and decoding vectors that results from SVD is an advantage for realizing IA scheme because we can rely on an iterative scheme, known as iterative power method (IPM). The singular vectors resulting from the IPM approach converge to the actual ones after multiple iterations assuming a common “virtually static” channel between each link. However, due to the fast fading nature of the channel, computed precoding and ZF decoding vectors will be different from those resulting from the SVD of the actual channel. To this end, the IPM applied to get an estimate of precoding and ZF decoding vectors allows a better tracking of the time-varying channel. The bit error rate of the proposed scheme is evaluated by means of Monte Carlo simulations and compared with that achieved by a perfect CSI based system.
机译：本文研究了需要信道状态信息（CSI）的双向多输入多输出（MIMO）M×2X网络的干扰对准（IA）方案。在我们提出的方法中，从信道矩阵的奇异值分解（SVD）计算出的奇异矢量分别用于在发射机和接收机处计算预编码和迫零（ZF）解码矩阵。 SVD导致的预编码和解码向量之间的正交性是实现IA方案的一个优势，因为我们可以依靠称为“迭代功率方法（IPM）”的迭代方案。 IPM方法产生的奇异矢量在多次迭代后收敛到实际矢量，并假设每个链路之间有一个公共的“虚拟静态”通道。但是，由于信道的快速衰落特性，计算出的预编码和ZF解码向量将与实际信道的SVD产生的向量不同。为此，用于获得预编码和ZF解码矢量估计的IPM可以更好地跟踪时变信道。通过蒙特卡洛仿真评估了所提出方案的误码率，并与基于理想CSI的系统实现的误码率进行了比较。

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