{"ID":2882711,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.09646","arxiv_id":"2508.09646","title":"Per-antenna power constraints: constructing Pareto-optimal precoders with cubic complexity under non-negligible noise conditions","abstract":"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.","short_abstract":"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 unsol...","url_abs":"https://arxiv.org/abs/2508.09646","url_pdf":"https://arxiv.org/pdf/2508.09646v2","authors":"[\"Sergey Petrov\",\"Samson Lasaulce\",\"Merouane Debbah\"]","published":"2025-08-13T09:26:15Z","proceeding":"math.NA","tasks":"[\"math.NA\",\"eess.SP\"]","methods":"[]","has_code":false}