{"id":"W2584419254","doi":"10.1109/glocom.2016.7841908","title":"Interference Alignment for Heterogeneous Full-Duplex Cellular Networks","year":2016,"lang":"en","type":"article","venue":"","topic":"Full-Duplex Wireless Communications","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Macrocell; Telecommunications link; Duplex (building); Base station; Computer science; Interference (communication); Precoding; Interference alignment; Femtocell; Macro; Computer network; Femto-; Transmission (telecommunications); Electronic engineering; Telecommunications; MIMO; Engineering; Beamforming","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":[0.000484561,0.0004300128,0.0004372421,0.0002508259,0.0003797672,0.0007382935,0.0004324241,0.0003856393,0.0008692597],"category_scores_gemma":[0.001258584,0.0001906577,0.0002809956,0.0005245781,0.0006274027,0.0007379716,0.0006086528,0.0003307459,0.0001722635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007448717,"about_ca_system_score_gemma":0.0004571695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002954239,"about_ca_topic_score_gemma":0.002023447,"domain_scores_codex":[0.9995146,0.0001575928,0.00001203043,0.00007913357,0.0001328615,0.0001038203],"domain_scores_gemma":[0.9993894,0.0003055609,0.0001075849,0.00006054946,0.00009645355,0.00004040332],"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":[0.00007243331,0.00002410614,0.0007861115,0.00002793363,0.00002634246,0.0001744705,0.00003912067,0.9683614,0.005352501,0.0144534,0.0003018579,0.01038037],"study_design_scores_gemma":[0.000004844613,0.00002264601,0.0003119335,0.000001791029,0.000005733708,0.00004162116,0.00001983189,0.9952363,0.000710047,0.003388944,0.0002519524,0.000004393755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1364987,0.0005173876,0.8555811,0.0001152811,0.00004441194,0.00002359186,0.00009432632,0.0001038261,0.007021493],"genre_scores_gemma":[0.9836393,0.0002376495,0.01450385,0.00004140823,0.00002951494,0.00002759901,0.00005547029,0.000009996462,0.00145514],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002954239,"threshold_uncertainty_score":0.005874097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01740459099391933,"score_gpt":0.2168312620316045,"score_spread":0.1994266710376851,"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."}}