On the Vector Broadcast Channel with Alternating CSIT: A Topological Perspective

TL;DR

This paper explores how topological factors like link strength influence the degrees-of-freedom in a two-user broadcast channel with alternating CSIT, introducing novel topological signal management schemes.

Contribution

It develops generalized degrees-of-freedom bounds incorporating topology and feedback, and proposes new topological signal management schemes for practical feedback settings.

Findings

Derived exact DoF expressions as functions of topology and feedback statistics.

Identified optimal feedback mechanisms for different topological scenarios.

Provided insights on leveraging topological diversity for performance gains.

Abstract

In many wireless networks, link strengths are affected by many topological factors such as different distances, shadowing and inter-cell interference, thus resulting in some links being generally stronger than other links. From an information theoretic point of view, accounting for such topological aspects has remained largely unexplored, despite strong indications that such aspects can crucially affect transceiver and feedback design, as well as the overall performance. The work here takes a step in exploring this interplay between topology, feedback and performance. This is done for the two user broadcast channel with random fading, in the presence of a simple two-state topological setting of statistically strong vs. weaker links, and in the presence of a practical ternary feedback setting of alternating channel state information at the transmitter (alternating CSIT) where for each channel realization, this CSIT can be perfect, delayed, or not available. In this setting, the work derives generalized degrees-of-freedom bounds and exact expressions, that capture performance as a function of feedback statistics and topology statistics. The results are based on novel topological signal management (TSM) schemes that account for topology in order to fully utilize feedback. This is achieved for different classes of feedback mechanisms of practical importance, from which we identify specific feedback mechanisms that are best suited for different topologies. This approach offers further insight on how to split the effort --- of channel learning and feeding back CSIT --- for the strong versus for the weaker link. Further intuition is provided on the possible gains from topological spatio-temporal diversity, where topology changes in time and across users.