基于半正定松弛的MIMO盲检测

为解决MIMO系统盲检测问题，该文以最大似然序列检测为估计准则，通过推导建立了一种新的半正定松弛(SemiDefinite Relaxation, SDR)求解模型，使得到的松弛解的秩等于发送天线数。为了解决了松弛解秩大于1时估计原始发送序列的难题，该文提出一种特征向量近似法和随机法相结合的方法。通过限定目标函数的取值上限，使算法能够根据目标函数值自适应判断求解发送序列个数，从而减少每次求解的约束个数和SDR的求解次数，分析表明算法的计算复杂度与发送天线数成线性关系。最后，通过仿真表明所提算法能够在与秩1的算法性能保持相当的条件下减少计算时间，并验证了算法计算复杂度与发送天线数成线性关系。

Blind Detection of MIMO via Semidefinite Relaxation

In order to solve the problem of blind detection of MIMO system, this paper takes maximum-likelihood sequence detection as the criterion and derives the formulas to get a model based on SemidDefinite Relaxation. The rank of SDR solution equals to the number of the transmit antennas. For the rank of SDR solution is greater than 1, a new method is proposed to approximate the solution of the original problem, which combines the eigenvector approximation method and randomization method. By setting the upper limit of objective function, the proposed method could judge the number of detection sequence adaptively and reduce constrains number and the number of solving SDR. The analysis shows that the computation complexity of proposed method has linear relationship with the number of transmit antennas. At last, simulation results indicate that compared with Rank-1 algorithm, the proposed detector could provide the same bit error performance with decrease of computation cost, and validate the linear relationship between the computation complexity and the number of transmit antennas.