| Title | Age of Information Minimization for Grant-Free Non-Orthogonal Massive Access Using Mean-Field Games |
| Authors | Zhang, Hongliang Kang, Yuhan Song, Lingyang Han, Zhu Poor, H. Vincent |
| Affiliation | Princeton Univ, Dept Elect & Comp Engn, Princeton, NJ 08544 USA Univ Houston, Elect & Comp Engn Dept, Houston, TX 77004 USA Peking Univ, Dept Elect, Beijing 100871, Peoples R China Kyung Hee Univ, Dept Comp Sci & Engn, Seoul 446701, South Korea |
| Keywords | MULTIPLE-ACCESS INTERNET THINGS NETWORKS IOT |
| Issue Date | Nov-2021 |
| Publisher | IEEE TRANSACTIONS ON COMMUNICATIONS |
| Abstract | 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. |
| URI | http://hdl.handle.net/20.500.11897/629508 |
| ISSN | 0090-6778 |
| DOI | 10.1109/TCOMM.2021.3103244 |
| Indexed | SCI(E) |
| Appears in Collections: | 信息科学技术学院 |
