Matrix-monotonic optimization for MIMO systems

For MIMO systems, due to the deployment of multiple antennas at both the transmitter and the receiver, the design variables, e.g., precoders, equalizers, and training sequences, are usually matrices. It is well known that matrix operations are usually more complicated compared with their vector counterparts. In order to overcome the high complexity resulting from matrix variables, in this paper, we investigate a class of elegant multi-objective optimization problems, namely matrix-monotonic optimization problems (MMOPs). In our work, various representative MIMO optimization problems are unified into a framework of matrix-monotonic optimization, which includes linear transceiver design, nonlinear transceiver design, training sequence design, radar waveform optimization, the corresponding robust design and so on as its special cases. Then, exploiting the framework of matrix-monotonic optimization the optimal structures of the considered matrix variables can be derived first. Based on the optimal structure, the matrix-variate optimization problems can be greatly simplified into the ones with only vector variables. In particular, the dimension of the new vector variable is equal to the minimum number of columns and rows of the original matrix variable. Finally, we also extend our work to some more general cases with multiple matrix variables.