Capacity of Gaussian Many-Access Channels

Abstract

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 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 the Internet of Things. The focus of the current paper is the 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 as well as decoded by the receiver. 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. The minimum cost of identifying the active users is also quantified.

Massive Access for Future Wireless Communication Systems

“…are orders of magnitude larger than the traditional model (i.e., massive). When the number of users goes to infinity, a positive rate as in the classical multiple access channel setup is not achievable [8]. This is one of the main reasons why SIC cannot guarantee an arbitrary small error decoding probability for massive access channel.…”

“…To address this issue, Chen et al propose a concept of symmetric message-length capacity for massive access communication over additive white gaussian noise (AWGN) channels [8]. The message-length capacity is defined under three important assumptions: 1) Each user is assigned an individual codebook;…”

“…Finally, as discussed in Section II and illustrated in Figure 2, user identification is an important step in coordinated massive access communication. The authors in [8] further investigate the model where only an unknown subset of users send data. These active users utilize unique signatures to enable their identification by the receiver.…”

“…The capacity penalty due to this user activity uncertainty is obtained as a form of the identified signature length. One important message from [8] is that, asymptotically when Sum Message Length (bits) 10 5 n=2000 n=2000 asymptotic n=3000 n=3000 asymptotic n=6000 n=6000 asymptotic number of users and blocklength grow to infinity with a fixed rate, and the payload of each user grows to infinity logarithmically with the number of active user, it is order optimal to do a two-step approach: a step of active user detection, and a communication stage (see also [10] for a generalization to the MIMO case).…”

Intelligent Reflecting Surface for Massive Device Connectivity: Joint Activity Detection and Channel Estimation

“…To overcome the challenge of detecting active devices and estimating their channels, [14,15] studied the activity detection and channel estimation problem for massive connectivity from the view of information theory. By exploiting the sparse activity pattern, the problem is formulated as a compressed sensing problem and resolved by the approximate message passing (AMP) [16,17].…”