Capacity of Gaussian Many-Access Channels

TL;DR

This paper introduces a new many-user information theory paradigm for Gaussian channels with an increasing number of users, focusing on capacity characterization when user activity is unknown and must be detected.

Contribution

It proposes a novel capacity notion for Gaussian many-access channels with growing users and unknown activity, combining user detection and message decoding.

Findings

Capacity characterized for Gaussian many-access channels with random user activity.

Achievability involves detecting active users before decoding messages.

Framework applicable to massive machine-type communication systems.

Abstract

Classical multiuser information theory studies the fundamental limits of models with a fixed (often small) number of users as the coding blocklength goes to infinity. This work proposes a new paradigm, referred to as {\em many-user information theory}, where the number of users is allowed to grow with the blocklength. This paradigm is motivated by emerging systems with a massive number of users in an area, such as machine-to-machine communication systems and sensor networks. The focus of the current paper is the {\em many-access} channel model, which consists of a single receiver and many transmitters, whose number increases unboundedly with the blocklength. Moreover, an unknown subset of transmitters may transmit in a given block and need to be identified. A new notion of capacity is introduced and characterized for the Gaussian many-access channel with random user activities. The capacity can be achieved by first detecting the set of active users and then decoding their messages.