{"id":"W4396847458","doi":"10.1007/s11276-024-03748-8","title":"Machine learning-inspired hybrid precoding with low-resolution phase shifters for intelligent reflecting surface (IRS) massive MIMO systems with limited RF chains","year":2024,"lang":"en","type":"article","venue":"Wireless Networks","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Fundamental Research Funds for the Central Universities","keywords":"Precoding; Computer science; MIMO; Phase (matter); Resolution (logic); Surface (topology); Electronic engineering; Telecommunications; Artificial intelligence; Physics; Channel (broadcasting); Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002710255,0.0004621168,0.0004628054,0.0002204393,0.0003122282,0.0002280462,0.0004422283,0.0001841238,0.000004097923],"category_scores_gemma":[0.00004145497,0.0004023742,0.00008470013,0.0006768678,0.0001290923,0.000321049,0.0001002711,0.0009517486,0.000004919445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004606195,"about_ca_system_score_gemma":0.00003543162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002989018,"about_ca_topic_score_gemma":0.00008110152,"domain_scores_codex":[0.9980195,0.0000678021,0.0004891307,0.0005258251,0.0002278424,0.0006699277],"domain_scores_gemma":[0.9985139,0.0004929896,0.0001649038,0.0006053413,0.0001184346,0.0001044578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001872607,0.00003901639,0.0001635381,0.0004138464,0.0002249968,0.00002694648,0.0003007255,0.9728917,0.0009709896,0.0007644256,0.0001046355,0.02391194],"study_design_scores_gemma":[0.0008096304,0.0003445856,0.000005427127,0.00177671,0.00005012097,0.00002702672,0.0005932444,0.9883288,0.005101293,0.00001693774,0.002423907,0.0005223505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1477336,0.008001754,0.8387012,0.0001462518,0.0003915527,0.0009534963,0.00002745356,0.003946159,0.00009860359],"genre_scores_gemma":[0.9922869,0.001778109,0.004833355,0.00001184333,0.0001231338,0.0003625728,0.0002003556,0.0002198718,0.0001838552],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8445534,"threshold_uncertainty_score":0.9998428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02094950509027202,"score_gpt":0.2662591158159817,"score_spread":0.2453096107257096,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}