On the Capacity of Noncoherent Wireless Networks

Speaker: Joyson Sebastian
Affiliation: Ph.D. Candidate - UCLA

Abstract:  Wireless networks are characterized by variation in the network states. In practice, the variations are combated by allocating resources for learning the network states. In networks with high mobility users, the variations are fast enough so that allocating separate resources for training may be suboptimal. In this work, we study the optimal schemes for noncoherent networks, where the channel states are unknown and are changing within given time periods. We address the question on how to optimally allocate the resources for training and communication.

In the first part, we consider single flow noncoherent wireless networks, where there is a single information source and a single destination. We consider the noncoherent MIMO with nonidentical link strengths. This could arise in the 5G architecture where the basestations can cooperate through a backhaul and when there is device-to-device cooperation through a sidechannel. The study of noncoherent MIMO is also fundamental in understanding the capacity of noncoherent networks through the cut sets. We derive the generalized degrees of freedom (gDoF) for SIMO, MISO and 2×2 MIMO with symmetric  statistics. We prove that for these cases and also for larger symmetric MxM MIMO, a training scheme that learns all the channel parameters is not gDoF optimal. We then proceed to study the noncoherent 2-relay diamond network with asymmetric link strengths. We prove that in certain regimes, it is optimal to perform a relay selection and operate the network. In other regimes where we need to operate both the relays, it is not optimal to learn all the channel states through training. We propose a novel scheme that partially trains the network and combine it with scaling at the relays and quantize-map-forward operation and prove that our scheme is gDoF optimal.

In the second part, we consider multiple flow noncoherent wireless networks. We specifically consider the noncoherent 2-user interference channel (IC), where both the transmitters and the receivers do not know the channels strengths, but the statistics are known. We propose a noncoherent scheme with rate-splitting, based on the statistics of the channel. We prove that this scheme achieves higher gDoF than a training based scheme. The results extend to the case of noncoherent IC with feedback, where the outputs at the receivers are fed back to the corresponding transmitter.

Biography: Joyson Sebastian received his B.Tech. degree from Indian Institute of Technology, Kharagpur in 2012 in Electronics and Electrical Communication Engineering. He received his M.S. degree from UCLA, in 2015, and is currently a Ph.D. candidate at UCLA, all in Electrical Engineering. He was a recipient of UCLA Graduate Division Fellowship in 2013 and the Guru Krupa Fellowship in 2014 and 2018. His research interests are broadly in information theory, wireless networks, and algorithms.

For more information, contact Prof. Suhas Diggavi (suhasdiggavi@ucla.edu)

Date/Time:
Date(s) - Sep 06, 2018
12:00 am

Location:
E-IV Maxwell Room #57-124
420 Westwood Plaza - 5th Flr. , Los Angeles CA 90095