  In this paper, we consider the interference rejection combining (IRC)
receiver, which improves the cell-edge user throughput via suppressing
inter-cell interference and requires estimating the covariance matrix including
the inter-cell interference with high accuracy. In order to solve the problem
of sample covariance matrix estimation with limited samples, a regularization
parameter optimization based on the minimum eigenvalue criterion is developed.
It is different from traditional methods that aim at minimizing the mean
squared error, but goes straight at the objective of optimizing the final
performance of the IRC receiver. A lower bound of the minimum eigenvalue that
is easier to calculate is also derived. Simulation results demonstrate that the
proposed approach is effective and can approach the performance of the oracle
estimator in terms of the mutual information metric.
