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

## TL;DR

This paper introduces low-complexity algorithms for estimating the maximum sum rate of MIMO systems with successive zero-forcing dirty paper coding under per-antenna power constraints, improving computational efficiency.

## Contribution

It proposes two novel approaches—an optimal algorithm using problem transformation and alternating optimization, and a machine learning-based suboptimal method—for sum rate estimation in MIMO systems with practical constraints.

## Key findings

- The optimal algorithm outperforms existing high-complexity methods.
- The machine learning approach provides a simple, effective suboptimal solution.
- Extensive validation confirms the superiority of the proposed methods.

## Abstract

In this paper, we study the sum rate maximization for successive zero-forcing dirty-paper coding (SZFDPC) with per-antenna power constraint (PAPC). Although SZFDPC is a low-complexity alternative to the optimal dirty paper coding (DPC), efficient algorithms to compute its sum rate are still open problems especially under practical PAPC. The existing solution to the considered problem is computationally inefficient due to employing high-complexity interior-point method. In this study, we propose two new low-complexity approaches to this important problem. More specifically, the first algorithm achieves the optimal solution by transforming the original problem in the broadcast channel into an equivalent problem in the multiple access channel, then the resulting problem is solved by alternating optimization together with successive convex approximation. We also derive a suboptimal solution based on machine learning to which simple linear regressions are applicable. The approaches are analyzed and validated extensively to demonstrate their superiors over the existing approach.