Age-Limited Capacity of Massive MIMO

Abstract

In this paper, we investigate the age-limited capacity of the Gaussian many channel with total N users, out of which a random subset of Ka users are active in any transmission period and a large-scale antenna array at the base station (BS). Motivated by IoT applications and promises of the massive MIMO technology, we consider the setting in which both the number of users, N , and the number of antennas at the BS, M , are allowed to grow large at a fixed ratio ζ = M N . Assuming perfect channel state information (CSI) at the receiver, we derive the achievability bound under maximal ratio combining. As the number of active users, Ka, increases, the achievable spectral efficiency is found to increase monotonically to a limit log 2 1 + M Ka . Using the age of information (AoI) metric, first coined in [1], as our measure of data timeliness/freshness, we investigate the trade-offs between the AoI and spectral efficiency in the context massive connectivity with large-scale receiving antenna arrays. Based on our large system analysis, we provide an accurate characterization of the asymptotic spectral efficiency as a function of the number of antennas/users, the attempt probability, and the AoI. It is found that while the spectral efficiency can be made large, the penalty is an increase in the minimum AoI obtainable. The proposed achievability bound is further compared against recent massive MIMO-based massive unsourced random access (URA) schemes.

On the Age of Information in a Two-User Multiple Access Setup

“…Motivated by [12,24,29] in this paper we consider a multiple access channel (MAC) with two users that have AoI-oriented traffic with different characteristics. The receiver has multiple antennas, and the communication channels are subject to Rayleigh fading and path loss, as depicted in Figure 1.…”

“…In [24], the authors proposed the probabilistic random-access (PRA) and DPP methods for solving an optimization problem that aims to minimize the average AoI of the energy-harvesting node subject to the queue stability constraint of the grid-connected node. Recently, the performance of AoI has been investigated in Multiple-Input Multiple-Output (MIMO) systems [26][27][28][29][30]. In [26,27], the user scheduling problem has been investigated to minimize AoI in a multiuser MIMO status update system where multiple single-antenna devices send their information over a common wireless uplink channel to a multiple-antenna access point.…”

“…In [28], a novel MIMO broadcast setting is studied to minimize the sum average AoI through precoding and transmission scheduling. The age-limited capacity through MIMO setup was investigated in [29], where a random subset of users are active in any transmission period. In [30], the authors analyzed and optimized the performance of AoI in a grant-free random-access system with massive MIMO.…”