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TU2.I: Machine Learning for Communications II

Session Type: Lecture
Time: Tuesday, 5 May, 16:30 - 18:30
Location: On-Demand
Session Chair: Marios Kountouris, EURECOM
 
   TU2.I.1: DEEP LEARNING FOR ROBUST POWER CONTROL FOR WIRELESS NETWORKS
         Wei Cui; University of Toronto
         Kaiming Shen; University of Toronto
         Wei Yu; University of Toronto
 
   TU2.I.2: FEEDBACK TURBO AUTOENCODER
         Yihan Jiang; University of Washington
         Hyeji Kim; Samsung AI Research Cambridge
         Himanshu Asnani; Tata Institute of Fundamental Research
         Sewoong Oh; University of Washington
         Sreeram Kannan; University of Washington
         Pramod Viswanath; University of Illinois at Urbana Champaign
 
   TU2.I.3: EXPLOITING CHANNEL LOCALITY FOR ADAPTIVE MASSIVE MIMO SIGNAL DETECTION
         Mehrdad Khani; Massachusetts Institute of Technology
         Mohammad Alizadeh; Massachusetts Institute of Technology
         Jakob Hoydis; Nokia Bell Labs
         Phil Fleming; Independent Consultant
 
   TU2.I.4: DEEP LEARNING-BASED BEAM ALIGNMENT IN MMWAVE VEHICULAR NETWORKS
         Nitin Jonathan Myers; University of Texas at Austin
         Yuyang Wang; University of Texas at Austin
         Nuria González-Prelcic; University of Texas at Austin
         Robert W. Heath Jr.; University of Texas at Austin
 
   TU2.I.5: JOINT SOURCE-CHANNEL CODING AND BAYESIAN MESSAGE PASSING DETECTION FOR GRANT-FREE RADIO ACCESS IN IOT
         Johannes Dommel; Fraunhofer Heinrich Hertz Institute
         Zoran Utkovski; Fraunhofer Heinrich Hertz Institute
         Slawomir Stanczak; Fraunhofer Heinrich Hertz Institute
         Osvaldo Simeone; King’s College London
 
   TU2.I.6: CNN-BASED ANALOG CSI FEEDBACK IN FDD MIMO-OFDM SYSTEMS
         Mahdi Boloursaz Mashhadi; Imperial College London
         Qianqian Yang; Imperial College London
         Deniz Gündüz; Imperial College London