Compared with traditional network structure, cloud-radio access network (C-RAN) can provide higher capacity with lower latency transmission performance. Meanwhile, more user equipment will require more radio remote heads (RRHs) in the same region. This paper studies the cancellation of interference from adjacent RRHs and maximization for sum-rate based on the limitation that imperfect channel state information is known at RRHs in a downlink multiple-input multiple-output C-RAN scenario, where line-of-sight and scattered components are both considered. We adopt the mutual information tight lower bound to substitute channel capacity designing criterion for the exact form of the latter is hard to obtain. Owing to the non-convexity of the problem based on lower bound designing criterion, we transform the problem into a convex form and give the optimal structure of precoding and decoding matrices by proving the equivalence between the minimum mean square error and the original objective function. Numerical results show that the proposed iterative algorithm effectively improves the transmission capacity and eliminates coherent interference between RRHs by jointly precoding and decoding.

与传统网络结构相比，云无线接入网（C-RAN）能够以更低的时延传输性能提供更高的容量。同时，更多的用户设备将在同一地区需要更多的无线电远程头 (RRH)。本文基于在下行链路多输入多输出 C-RAN 场景中 RRH 已知不完善信道状态信息的限制，研究了来自相邻 RRH 的干扰的消除和总速率的最大化，其中视距和分散的分量都被考虑。我们采用互信息紧下界来替代信道容量设计准则，因为后者的确切形式难以获得。由于基于下界设计准则的问题的非凸性，我们将问题转化为凸形式，通过证明最小均方误差与原始目标函数的等价性，给出预编码和解码矩阵的最优结构。数值结果表明，所提出的迭代算法通过联合预编码和解码有效地提高了传输容量并消除了RRH之间的相干干扰。