{"id":"W2123144456","doi":"10.1109/icassp.2009.4960095","title":"Game theory for precoding in a multi-user system: Bargaining for overall benefits","year":2009,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Precoding; Bargaining problem; Game theory; Computer science; Mathematical optimization; Nash equilibrium; Construct (python library); Constraint (computer-aided design); Interference (communication); Channel (broadcasting); Dual (grammatical number); Mathematics; Mathematical economics; Telecommunications; Computer network; MIMO","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":[0.002693685,0.001247531,0.001205015,0.00070607,0.001125063,0.002841177,0.001585708,0.002443718,0.004919531],"category_scores_gemma":[0.003626749,0.0005413585,0.001091427,0.001002329,0.003932862,0.003459399,0.001757033,0.002431884,0.0006775594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00247528,"about_ca_system_score_gemma":0.001744138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001305405,"about_ca_topic_score_gemma":0.00128537,"domain_scores_codex":[0.9978927,0.001237219,0.00005948218,0.0001717061,0.0004699244,0.0001690168],"domain_scores_gemma":[0.9986538,0.0009576186,0.0001057443,0.00007699787,0.0001214961,0.00008427555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002065018,0.00001844174,0.00006449554,0.00005540666,0.00001953374,0.00009109167,0.0001263441,0.1332291,0.0006821934,0.8598978,0.0006824944,0.005112461],"study_design_scores_gemma":[0.00002614189,0.00005322564,0.00005985941,0.00002938202,0.00001301514,0.00007702081,0.00007151563,0.4957069,0.00019514,0.5010794,0.002663841,0.00002460177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004141429,0.0004521327,0.9799497,0.0006914537,0.00007699286,0.00006467441,0.00001745246,0.00002154433,0.01458481],"genre_scores_gemma":[0.7025443,0.002202874,0.2780371,0.0005274099,0.0004004597,0.0008794891,0.0000534887,0.00007437235,0.01528046],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004919531,"threshold_uncertainty_score":0.01795948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03372635128811263,"score_gpt":0.2679414944121611,"score_spread":0.2342151431240485,"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."}}