Per-antenna power constraints: constructing Pareto-optimal precoders with cubic complexity under non-negligible noise conditions
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911151003009024 |
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| author | Petrov, Sergey Lasaulce, Samson Debbah, Merouane |
| author_facet | Petrov, Sergey Lasaulce, Samson Debbah, Merouane |
| contents | Precoding matrix construction is a key element of the wireless signal processing using the multiple-input and multiple-output model. It is established that the problem of global throughput optimization under per-antenna power constraints belongs, in general, to the class of monotonic optimization problems, and is unsolvable in real-time. The most widely used real-time baseline is the suboptimal solution of Zero-Forcing, which achieves a cubic complexity by discarding the background noise coefficients. This baseline, however, is not readily adapted to per-antenna power constraints, and performs poorly if background noise coefficients are not negligible. In this paper, we are going to present a computational algorithm which constructs a precoder that is SINR multiobjective Pareto-optimal under per-antenna power constraints - with a complexity that differs from that of Zero-Forcing only by a constant factor. The algorithm has a set of input parameters, changing which skews the importance of particular user throughputs: these parameters make up an efficient parameterization of the entire Pareto boundary. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09646 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Per-antenna power constraints: constructing Pareto-optimal precoders with cubic complexity under non-negligible noise conditions Petrov, Sergey Lasaulce, Samson Debbah, Merouane Numerical Analysis Signal Processing 49M05, 68P30 G.1.3; F.2.1 Precoding matrix construction is a key element of the wireless signal processing using the multiple-input and multiple-output model. It is established that the problem of global throughput optimization under per-antenna power constraints belongs, in general, to the class of monotonic optimization problems, and is unsolvable in real-time. The most widely used real-time baseline is the suboptimal solution of Zero-Forcing, which achieves a cubic complexity by discarding the background noise coefficients. This baseline, however, is not readily adapted to per-antenna power constraints, and performs poorly if background noise coefficients are not negligible. In this paper, we are going to present a computational algorithm which constructs a precoder that is SINR multiobjective Pareto-optimal under per-antenna power constraints - with a complexity that differs from that of Zero-Forcing only by a constant factor. The algorithm has a set of input parameters, changing which skews the importance of particular user throughputs: these parameters make up an efficient parameterization of the entire Pareto boundary. |
| title | Per-antenna power constraints: constructing Pareto-optimal precoders with cubic complexity under non-negligible noise conditions |
| topic | Numerical Analysis Signal Processing 49M05, 68P30 G.1.3; F.2.1 |
| url | https://arxiv.org/abs/2508.09646 |