Optimal linear coding for vector channels
Abstract
The problem of optimal coding (minimumleastsquarederror linear transformation) of vector signals is considered. The source is a timediscrete memoryless Gaussian vector source and the channel is kdimensional with channel noise being a kdimensional white Gaussian stationary vector sequence. The optimal encoder is a linear transformation process that matches the rdimensional signal vector and the kdimensional channel under the given channel power constraint. The eigenvalues of the signal covariance matrix and the noise covariance matrix together with a performance weighting matrix determine the optimum coder design. An inverse eigenvalue problem must be solved if the individual subchannel powers are constrained.
 Publication:

IEEE Transactions on Communications
 Pub Date:
 December 1976
 Bibcode:
 1976ITCom..24.1283L
 Keywords:

 Channels (Data Transmission);
 Communication Theory;
 Linear Transformations;
 Multichannel Communication;
 Signal Encoding;
 Signal Transmission;
 Covariance;
 Decoders;
 Eigenvalues;
 Matrices (Mathematics);
 Optimization;
 Random Variables;
 Vectors (Mathematics);
 Communications and Radar