{"id":"W3109838026","doi":"","title":"Massive MIMO precoding and spectral shaping with low resolution DACs and active constellation extension.","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Precoding; MIMO; Spectral efficiency; Computer science; Transmitter; Algorithm; Constellation; Performance improvement; Converters; Electronic engineering; Control theory (sociology); Telecommunications; Artificial intelligence; Engineering; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004291874,0.0004860179,0.00028551,0.0002098911,0.0001819139,0.0005397607,0.0004934187,0.0006136146,0.0008323174],"category_scores_gemma":[0.001693447,0.0002091918,0.0002722031,0.0004891635,0.0005176062,0.0006450508,0.0006060792,0.0005938839,0.0002732487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002842696,"about_ca_system_score_gemma":0.0004650326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003547878,"about_ca_topic_score_gemma":0.0007375944,"domain_scores_codex":[0.9995803,0.0001639457,0.00001511834,0.00004959733,0.0001630036,0.0000280492],"domain_scores_gemma":[0.999401,0.0002944981,0.00009625773,0.00009760979,0.00008338973,0.00002727992],"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":[0.0002729427,0.0001008324,0.001048717,0.0003133649,0.00009086243,0.0004343367,0.0001669058,0.6346627,0.06841927,0.1059056,0.001270991,0.1873135],"study_design_scores_gemma":[0.000009721415,0.0001434375,0.0001926342,0.00001484503,0.000012939,0.000316126,0.00001619274,0.9756362,0.01204364,0.00956761,0.002036314,0.00001032888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01428331,0.0003750399,0.9816854,0.000088443,0.00004513717,0.00002319433,0.00002023401,0.0001057105,0.003373452],"genre_scores_gemma":[0.6960856,0.0005778684,0.2975062,0.0001137391,0.0001019942,0.00005810069,0.00006210911,0.00001552084,0.005478892],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008323174,"threshold_uncertainty_score":0.002784371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0653896513811696,"score_gpt":0.1931097388987965,"score_spread":0.1277200875176269,"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."}}