{"id":"W4221153184","doi":"10.1109/jstsp.2022.3158820","title":"Learning Progressive Distributed Compression Strategies From Local Channel State Information","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Fusion center; Computer science; Channel state information; Quantization (signal processing); Overhead (engineering); Scalability; Data compression; Algorithm; Bandwidth (computing); Decoding methods; Compressed sensing; Computer engineering; Distributed computing; Real-time computing; Telecommunications; Cognitive radio; Wireless","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009540157,0.0007783229,0.000621911,0.0003424539,0.000287614,0.000606269,0.001241294,0.0006482404,0.001009955],"category_scores_gemma":[0.003383957,0.0003824607,0.0002659211,0.0003296256,0.0009648826,0.001730835,0.001202492,0.001193521,0.0002229234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006421143,"about_ca_system_score_gemma":0.0009935764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001856849,"about_ca_topic_score_gemma":0.002708601,"domain_scores_codex":[0.9996194,0.00008478206,0.00002210019,0.0001068095,0.0001144872,0.00005249008],"domain_scores_gemma":[0.9988681,0.0006262525,0.0001402073,0.0001608572,0.0001612502,0.00004343879],"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":[0.000106447,0.00009057781,0.0006427258,0.00005481508,0.00003557149,0.00005935623,0.00008481088,0.868165,0.009282251,0.01974005,0.0007300589,0.1010083],"study_design_scores_gemma":[0.000008926446,0.00002840831,0.0000556955,0.000004403946,0.000004133926,0.00001207505,0.000006722386,0.9930106,0.001840069,0.004805866,0.000218552,0.000004565073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01542665,0.0001322454,0.9829115,0.00009533519,0.00001478583,0.00002766117,0.00001682338,0.0001784559,0.001196598],"genre_scores_gemma":[0.8459201,0.0002245933,0.1511262,0.0001680695,0.00004495638,0.0001177741,0.00008508658,0.0000438501,0.002269299],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001856849,"threshold_uncertainty_score":0.005045414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008996017593758858,"score_gpt":0.2314757041335062,"score_spread":0.2224796865397473,"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."}}