一种加权均方误差最小化的鲁棒性干扰对齐算法

线性干扰对齐的一个常见优化目标是总传输速率最大化，但因为和速率函数的非凸特性而难以直接求解。加权均方误差最小化算法借助均方误差与和速率之间的等价关系解决了这一问题。这一方法需要获得准确的信道状态信息，在实际应用中，通道估计误差的存在会导致算法性能的下降。该文提出一种改进算法，在干扰对齐预编码矩阵与接收矩阵的优化求解过程中将通道估计误差的统计特性考虑在内。仿真结果表明，相比以往的加权均方误差最小化算法，该文算法对信道估计误差具有较高的鲁棒性，可以有效提高总的传输速率。

A Robust Interference Alignment Algorithm Based on Weighted Mean Square Error Minimization

Sum rate maximization is often used as the target of linear Interference Alignment (IA). However, the sum rate function is non-convex and hard to be solved. This problem is solved according to the relationship of mean square error and sum rate which is known as the Weighted Minimum Mean Square Error (WMMSE). This method relies on the knowledge of channel state information. In real systems, the channel estimation error may cause significant descent to the sum rate performance. This paper proposes an improved algorithm, which considers the statistical character of channel estimation error. Simulation results show that the proposed algorithm is robust to channel estimation error and improves the sum-rate efficiently, compared with the usual WMMSE method.