{"id":"W2996043598","doi":"10.48550/arxiv.1912.10187","title":"Power Control for Massive MIMO Systems with Nonorthogonal Pilots","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"MIMO; Benchmark (surveying); Fading; Computer science; Telecommunications link; Power control; Mathematical optimization; Transmitter power output; Power (physics); Path loss; Throughput; Optimization problem; Convex optimization; Control theory (sociology); Control (management); Regular polygon; Algorithm; Mathematics; Telecommunications; Wireless; Transmitter; Decoding methods","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007777584,0.000637176,0.0004420636,0.0001602809,0.0001989903,0.0005775487,0.0003237975,0.0003380502,0.0005092659],"category_scores_gemma":[0.002108457,0.000253287,0.000222025,0.0003053262,0.0008330942,0.0005623632,0.0005873437,0.0005315918,0.0001131325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000351257,"about_ca_system_score_gemma":0.0005955845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009355934,"about_ca_topic_score_gemma":0.0007013385,"domain_scores_codex":[0.9995752,0.0001769917,0.00001269722,0.00006172626,0.00012436,0.0000488958],"domain_scores_gemma":[0.9993512,0.0004060133,0.0001084811,0.00004162611,0.00007562268,0.00001700299],"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":[0.00004129828,0.00001719481,0.0001776399,0.00003169536,0.00001623864,0.00004011379,0.0000183512,0.9709074,0.003296133,0.01548968,0.0002535205,0.009710814],"study_design_scores_gemma":[0.000005873774,0.00003311378,0.00006428377,0.00000240253,0.000002552324,0.000008269671,0.000003530927,0.9949862,0.0006140191,0.004138011,0.0001392476,0.000002548986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03364107,0.0002852936,0.9634866,0.0001918586,0.00003367768,0.00001742394,0.00002468686,0.0000711769,0.002248216],"genre_scores_gemma":[0.9620913,0.0003528788,0.03594385,0.00006906314,0.00004828185,0.00003945327,0.00002474967,0.00001882924,0.001411629],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009355934,"threshold_uncertainty_score":0.004113197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02214772338333433,"score_gpt":0.1598860164149377,"score_spread":0.1377382930316033,"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."}}