{"id":"W2903386141","doi":"10.1007/978-3-030-02158-0","title":"Hybrid Massive MIMO Precoding in Cloud-RAN","year":2018,"lang":"en","type":"book","venue":"Wireless networks","topic":"Antenna Design and Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Precoding; Baseband; Computer science; Channel state information; MIMO; sync; Cloud computing; C-RAN; Channel (broadcasting); Electronic engineering; Radio access network; Wireless; Telecommunications; Base station; Engineering; Bandwidth (computing)","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":[0.0002017749,0.0007791919,0.0004647634,0.0002868924,0.0002072857,0.001068387,0.0004948595,0.0005307292,0.01111794],"category_scores_gemma":[0.0004008842,0.0002833933,0.0002692999,0.0009014111,0.0003186508,0.0008016614,0.0005692309,0.001143447,0.00573016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004127535,"about_ca_system_score_gemma":0.0003804077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00060346,"about_ca_topic_score_gemma":0.001301044,"domain_scores_codex":[0.9998196,0.000020635,0.000005073576,0.00002672711,0.0001136001,0.00001439211],"domain_scores_gemma":[0.9998136,0.00007020598,0.000006919744,0.00003788839,0.00006022535,0.0000112195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001276924,0.00008309478,0.0002483353,0.0004018839,0.00007322271,0.0002033057,0.00006494768,0.07615156,0.03176281,0.1302059,0.08670883,0.6739684],"study_design_scores_gemma":[0.00002719633,0.0001819992,0.0008753227,0.000195283,0.00004865733,0.00111094,0.00006263582,0.4295609,0.02430102,0.1615466,0.3820118,0.00007765622],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.004710792,0.01632201,0.8752465,0.0007620697,0.002069373,0.00004523412,0.0002651809,0.001189412,0.0993894],"genre_scores_gemma":[0.176553,0.028007,0.4096345,0.00125664,0.003346193,0.0001267464,0.000968468,0.0008437072,0.3792637],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.01111794,"threshold_uncertainty_score":0.03719318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009126531542768764,"score_gpt":0.1950449916199604,"score_spread":0.1859184600771916,"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."}}