{"id":"aff66d73-8f5b-4343-84ba-7a71eed38a5e","arxiv_id":"1908.00404","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":5,"one_line_summary":"A complex BP neural network is trained to mimic zero-forcing precoding in mmWave multiuser massive MIMO; the authors claim it outperforms conventional hybrid precoders in simulation.","lead":"The paper trains a complex-valued backpropagation neural network to reproduce full-digital zero-forcing precoding for millimeter-wave multiuser massive MIMO, reporting simulation gains in spectral efficiency and bit-error rate over two conventional hybrid precoders. It is relevant to the push to apply machine learning to physical-layer radio processing, but the proposed network does not enforce the phase-shifter constraints that define hybrid precoding.","discovery_kind":"extension","skeptic_critique":null,"referee_report":null,"author_rebuttal":null,"desk_editor":null,"rs_alignment":null,"lean_confirmation":null,"pith_extraction":null,"created_at":"2026-08-14T15:58:37.940933+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":null,"supporting_citations":[],"review_version":1}