Age of Information Minimization for Grant-Free Non-Orthogonal Massive Access Using Mean-Field Games

Hongliang Zhang, Yuhan Kang, Lingyang Song, Zhu Han, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


Grant-free access, in which channels are accessed without undergoing assignment through a handshake process, is a promising solution to support massive connectivity needed for Internet-of-Things (IoT) networks. In this paper, we consider uplink grant-free massive access for an IoT network with multiple channels. To be specific, the IoT devices generate short packets and have grant-free non-orthogonal access to a channel to transmit the generated packets to a base station (BS). With the aim of keeping the information fresh at the BS, we first derive the age of information (AoI) for grant-free short-packet communications, and then formulate the AoI minimization problem. However, the problem is challenging as the number of users involved is large, and to tackle this problem efficiently, we propose a mean-field evolutionary game-based approach. In this approach, the average behavior of the IoT devices is considered rather than their individual behaviors, and the dynamics of the strategies of the IoT devices are modeled by an evolutionary process. Simulation results verify the effectiveness of the proposed mean-field evolutionary game-based approach.

Original languageEnglish (US)
Pages (from-to)7806-7820
Number of pages15
JournalIEEE Transactions on Communications
Issue number11
StatePublished - Nov 1 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


  • Age of information
  • Internet-of-Things networks
  • grant-free massive access
  • mean-field evolutionary game
  • short-packet communications


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