A new ICI matrix estimation technique using padded m-sequences for wireless OFDM systems

The intercarrier interference (ICI) matrix for the orthogonal frequency division multiplexing (OFDM) systems usually involves a fairly large dimension. The traditional least-square solution based on the pseudo-inverse operation therefore has its limitation. In addition, a sufficiently long training sequence to estimate the complete ICI matrix is not feasible since it results in the severe throughput reduction. In this paper, we derive a lower bound for the mean-square estimation error among the least-square ICI matrix estimators using different training sequences and prove that the minimum mean-square error (MMSE) optimality is attained when the training sequences carried by different OFDM blocks are orthogonal to each other, regardless of the sequence length. Then we propose to employ the training sequences based on the padded maximal-length shift-register sequences (m-sequences) so as to achieve a highly efficient and optimal ICI matrix estimator with the minimum mean-square estimation error among all least-square ICI matrix estimators. Besides, our new scheme involves only linear complexity while other existing least-square methods require the complexity proportional to the cube of the ICI matrix size. Both analytical and experimental comparisons between our new scheme using padded m-sequences and the existing method using conventional m-sequences justify the significant advantages of our new ICI matrix estimator