Optimality of Treating Inter-Cell Interference as Noise under Finite Precision CSIT

Hamdi Joudeh, Giuseppe Caire

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)

Abstract

In this work, we study the generalized degrees- of-freedom (GDoF) of downlink and uplink cellular networks, modeled as Gaussian interfering broadcast channels (IBC) and Gaussian interfering multiple access channels (IMAC), respectively. We focus on regimes of low inter-cell interference, where single-cell transmission with power control and treating inter-cell interference as noise (mc-TIN) is GDoF optimal. Recent works have identified two relevant regimes in this context: one in which the GDoF region achieved through mc-TIN for both the IBC and IMAC is a convex polyhedron without the need for time-sharing (mc-CTIN regime), and a smaller (sub)regime where mc-TIN is GDoF optimal for both the IBC and IMAC (mc-TIN regime). In this work, we extend the mc-TIN framework to cellular scenarios where channel state information at the transmitters (CSIT) is limited to finite precision. We show that in this case, the GDoF optimality of mc-TIN extends to the entire mc-CTIN regime, where GDoF benefits due to interference alignment (IA) are lost. Our result constitutes yet another successful application of robust outer bounds based on the aligned images (AI) approach.

Original languageEnglish
Title of host publication2020 IEEE International Symposium on Information Theory (ISIT)
PublisherInstitute of Electrical and Electronics Engineers
Pages2936-2941
Number of pages6
ISBN (Electronic)978-1-7281-6432-8
DOIs
Publication statusPublished - 24 Aug 2020
Externally publishedYes
Event2020 IEEE International Symposium on Information Theory, ISIT 2020 - Los Angeles, United States
Duration: 21 Jun 202026 Jun 2020

Conference

Conference2020 IEEE International Symposium on Information Theory, ISIT 2020
Country/TerritoryUnited States
CityLos Angeles
Period21/06/2026/06/20

Funding

This work was partially supported by the European Research Council (ERC) under the ERC Advanced Grant N. 789190, CARENET.

FundersFunder number
CARENET
European Union's Horizon 2020 - Research and Innovation Framework Programme789190
European Research Council

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