Minimization of Age of Information in Fading Multiple Access Channels

• Published in: IEEE journal on selected areas in communications 2021-05, Vol.39 (5), p.1471-1484
• Main Authors: Bhat, Rajshekhar Vishweshwar, Vaze, Rahul, Motani, Mehul
• Format: Article
• Language: English
• Subjects: Age; Age of information (AoI); bounded performance; Fading; Information age; Mathematical model; Minimization; multiple access channel (MAC); NOMA; Optimization; Power gain; stationary randomized policies (SRPs); Time Division Multiple Access; time-division multiple access (TDMA) and non-orthogonal multiple access~(NOMA); Wireless communication
• ISSN: 0733-8716; 1558-0008
• DOI: 10.1109/JSAC.2021.3065048

Freshness of information is an important requirement in many real-time applications. It is measured by a metric called the age of information (AoI), defined as the time elapsed since the generation of the last successful update received by the destination. We consider M sources (users) updating their statuses to a base station (BS) over a block-fading multiple access channel (MAC). At the start of each fading block, the BS acquires perfect information about channel power gain realizations of all the users in the block. Using this information, a centralized scheduling policy at the BS decides, for each block, which users should transmit and with what powers. The objective is to minimize a long-term weighted average AoI across all users subject to a long-term average power constraint at each user. Under this setting, we first consider a simple time-division multiple access (TDMA) strategy, in which at most one user can transmit in a slot, and propose a simple age-independent stationary randomized policy (AI-SRP). The AI-SRP makes transmission decisions based on the channel power gain realizations, without considering the AoIs. We then consider a more general non-orthogonal multiple access (NOMA) strategy, in which any number of users can transmit in a slot subject to capacity constraints of the MAC and propose an AI-SRP. The AI-SRPs we propose are optimal solutions to appropriate optimization problems. We show that the minimum achievable weighted average AoIs across the users under the proposed AI-SRPs are at most two times those of the respective optimal policies under TDMA and NOMA strategies.