{"id":"W3107506823","doi":"10.1109/ojsp.2020.3040590","title":"Linear CE and 1-bit Quantized Precoding With Optimized Dithering","year":2020,"lang":"en","type":"article","venue":"IEEE Open Journal of Signal Processing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"National Science Foundation","keywords":"Dither; Precoding; Quantization (signal processing); Transmitter; Predistortion; Transmission (telecommunications); Telecommunications link; Electronic engineering; Bit error rate; Transmitter power output; Computer science; Mathematics; Control theory (sociology); Amplifier; Topology (electrical circuits); Algorithm; Telecommunications; MIMO; Bandwidth (computing); Engineering; Decoding methods","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":[],"consensus_categories":[],"category_scores_codex":[0.0002753433,0.0001764014,0.0004106954,0.0000639974,0.000105619,0.0002631546,0.000270078,0.00004998166,0.00002412846],"category_scores_gemma":[0.00002468071,0.0001444825,0.00003047154,0.0002292316,0.00002972871,0.001504461,0.00003489432,0.0002786487,0.000001871592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003715657,"about_ca_system_score_gemma":0.00006148531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001362483,"about_ca_topic_score_gemma":5.701912e-7,"domain_scores_codex":[0.9990104,0.00003029748,0.0004792496,0.0001435102,0.000150733,0.0001857989],"domain_scores_gemma":[0.9992543,0.00004734005,0.0003088789,0.00005455496,0.0001631512,0.0001717637],"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":[0.0003056916,0.000008220942,0.00006806078,0.0002568537,0.0000552591,0.00004187465,0.001090478,0.9346369,0.05052364,0.000002837784,0.00003598594,0.0129742],"study_design_scores_gemma":[0.003130762,0.000243677,0.000009188736,0.001424688,0.00006928739,0.0002755723,0.0006481966,0.9564282,0.03717711,0.00003364473,0.0002243299,0.0003353889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05012901,0.001008937,0.9477668,0.0001154742,0.00007523092,0.0002224477,0.000001338374,0.00005802399,0.0006227292],"genre_scores_gemma":[0.8279397,0.00005930355,0.1716738,0.00004066003,0.0002131098,0.000003207883,4.582382e-7,0.00005541458,0.00001440224],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7778107,"threshold_uncertainty_score":0.5891826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03112275130928852,"score_gpt":0.2599415009134211,"score_spread":0.2288187496041326,"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."}}