Linear Sum Capacity for Gaussian Multiple Access Channels with Feedback

TL;DR

This paper characterizes the linear-feedback sum-capacity of Gaussian multiple access channels with feedback, showing that Kramer's code achieves this capacity and providing insights into the maximum sum-rate with feedback.

Contribution

It introduces a precise characterization of linear-feedback sum-capacity and demonstrates that Kramer's code attains this capacity in Gaussian multiple access channels.

Findings

Kramer's code achieves the linear-feedback sum-capacity.

The linear-feedback sum-capacity is explicitly characterized.

Evidence suggests Kramer's code may also achieve the overall sum-capacity.

Abstract

The capacity region of the N-sender Gaussian multiple access channel with feedback is not known in general. This paper studies the class of linear-feedback codes that includes (nonlinear) nonfeedback codes at one extreme and the linear-feedback codes by Schalkwijk and Kailath, Ozarow, and Kramer at the other extreme. The linear-feedback sum-capacity C_L(N,P) under symmetric power constraints P is characterized, the maximum sum-rate achieved by linear-feedback codes when each sender has the equal block power constraint P. In particular, it is shown that Kramer's code achieves this linear-feedback sum-capacity. The proof involves the dependence balance condition introduced by Hekstra and Willems and extended by Kramer and Gastpar, and the analysis of the resulting nonconvex optimization problem via a Lagrange dual formulation. Finally, an observation is presented based on the properties of the conditional maximal correlation---an extension of the Hirschfeld--Gebelein--Renyi maximal correlation---which reinforces the conjecture that Kramer's code achieves not only the linear-feedback sum-capacity, but also the sum-capacity itself (the maximum sum-rate achieved by arbitrary feedback codes).