Complementary Set Matrices Satisfying a Column Correlation Constraint
  Motivated by the problem of reducing the peak to average power ratio (PAPR) of transmitted signals, we consider a design of complementary set matrices whose column sequences satisfy a correlation constraint. The design algorithm recursively builds a collection of $2^{t+1}$ mutually orthogonal (MO) complementary set matrices starting from a companion pair of sequences. We relate correlation properties of column sequences to that of the companion pair and illustrate how to select an appropriate companion pair to ensure that a given column correlation constraint is satisfied. For $t=0$, companion pair properties directly determine matrix column correlation properties. For $t\geq 1$, reducing correlation merits of the companion pair may lead to improved column correlation properties. However, further decrease of the maximum out-off-phase aperiodic autocorrelation of column sequences is not possible once the companion pair correlation merit is less than a threshold determined by $t$. We also reveal a design of the companion pair which leads to complementary set matrices with Golay column sequences. Exhaustive search for companion pairs satisfying a column correlation constraint is infeasible for medium and long sequences. We instead search for two shorter length sequences by minimizing a cost function in terms of their autocorrelation and crosscorrelation merits. Furthermore, an improved cost function which helps in reducing the maximum out-off-phase column correlation is derived based on the properties of the companion pair. By exploiting the well-known Welch bound, sufficient conditions for the existence of companion pairs which satisfy a set of column correlation constraints are also given. 
Di Wu and Predrag Spasojevic