Effective Compression for Sample Covariance Matrix in Interference Rejection Combining

In the interference rejection combining (IRC) algorithm, the sample covariance matrices generally have to be stored before being utilized, which can be extremely costly due to the large volumes of matrices in applied slots or bandwidth, coupled with its difficult compression characteristics. In this paper, we design a compression algorithm which can significantly reduce the storage requirement while maintaining the performance of IRC. The core of our algorithm is to transform the matrix to be compressed into a diagonally dominant matrix via a suitable unitary transform, and to store the off-diagonal entries in floating point numbers with shorter bit-widths. Besides the storage, the computation expense of the proposed unitary transform is low. Theoretical analysis and numerical results are both provided to demonstrate the effectiveness of proposed algorithm.