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In this paper, we study the sum rate maximization for successive zero-forcingdirty-paper coding (SZFDPC) with per-antenna power constraint (PAPC). AlthoughSZFDPC is a low-complexity alternative to the optimal dirty paper coding (DPC),efficient algorithms to compute its sum rate are still open problems especiallyunder practical PAPC. The existing solution to the considered problem iscomputationally inefficient due to employing high-complexity interior-pointmethod. In this study, we propose two new low-complexity approaches to thisimportant problem. More specifically, the first algorithm achieves the optimalsolution by transforming the original problem in the broadcast channel into anequivalent problem in the multiple access channel, then the resulting problemis solved by alternating optimization together with successive convexapproximation. We also derive a suboptimal solution based on machine learningto which simple linear regressions are applicable. The approaches are analyzedand validated extensively to demonstrate their superiors over the existingapproach.

On Estimating Maximum Sum Rate of MIMO Systems with Successive Zero-Forcing Dirty Paper Coding and Per-antenna Power Constraint