The Gaussian parallel relay network, in which two parallel relays assist a source to convey information to a destination, was introduced by Schein and Gallager. An upper bound on the capacity can be obtained by considering broadcast cut between the source and relays and multiple access cut between the relays and the destination. Niesen and Diggavi derived an upper bound for Gaussian parallel N-relay network by considering all other possible cuts and showed an achievability scheme that can attain rates close to the upper bound in different channel gain regimes thus establishing approximate capacity. In this talk, we consider symmetric layered Gaussian relay networks in which there can be many layers of parallel relays. The channel gains for the channels between two adjacent layers are symmetrical (identical). Relays in each layer broadcast information to the relays in the next layer. For 2-layer N-relay Gaussian network, we give upper bounds on the capacity. Our analysis reveals that for the upper bounds, joint optimization over correlation coefficients is not necessary for obtaining stronger results.
Satyajit Thakor received his B.Eng. in Electronics and Telecommunication from Dr. B. A. M. University, India in 2004. He received his M.Eng. and PhD in Telecommunications in 2006 and 2012 from Institute for Telecommunications Research, University of South Australia. He was a postdoctoral fellow at Institute of Network Coding, The Chinese University of Hong Kong from 2011 to 2014. Since January 2014, he is an Assistant Professor in School of Computing and Electrical Engineering at Indian Institute of Technology Mandi. Dr. Satyajit Thakor was a recipient of Michael Miller Medal 2012 for his PhD thesis and the Endeavour Research Fellowship 2009. His research interests include information theory, network coding and distributed storage.