Improving Age of Information in Wireless Networks With Perfect Channel State Information

Talak, Rajat; Karaman, Sertaç; Modiano, Eytan

doi:10.1109/tnet.2020.2996237

Cited by 46 publications

(13 citation statements)

References 59 publications

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“…To evaluate the efficiency of the proposed algorithms, the age of information (AoI) is investigated at the receivers. AoI is a metric used to measure the information freshness when it reaches the destination [12]. It is defined as the time elapsed since the latest useful piece of information, that reached its intended destination, has been generated at its source.…”

Section: Prerequisites and System Componentsmentioning

confidence: 99%

Uncoordinated Spectrum Access for Massive Connectivity in IoT Applications

Doumit,

Youssef,

Nour

et al. 2024

IEEE Internet Things M.

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Boosted by the Internet of Things (IoT), future cellular communication networks will be able to support new services with high social impact. Corresponding applications are expected to span almost every domain, especially the most critical ones, such as the medical field or smart factories. However, their adoption is conditioned by strict compliance with stringent quality of service (QoS) requirements for the underlying network. In particular, the achieved throughput and latency are of utmost importance for efficient monitoring with multiple sensors in a variety of application areas. In this paper, we propose an intelligent self-configurable system structure, achieved through resource allocation techniques using machine learning, and capable of flexibly fulfilling such requirements. In addition, special care was taken to minimize the transmit power of the IoT devices while efficiently supporting different application-dependent traffic types, an essential prerequisite for any wide adoption of such systems with massive network access.

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Section: Prerequisites and System Componentsmentioning

confidence: 99%

Uncoordinated Spectrum Access for Massive Connectivity in IoT Applications

Doumit,

Youssef,

Nour

et al. 2024

IEEE Internet Things M.

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“…Conversely, if a device was selected in the previous round, the probability for its reselection should decrease. In order to evaluate the staleness, the concept of AoI is introduced [20], [21]. The information age of any device n in round t, denoted by…”

Section: Device Selectionmentioning

confidence: 99%

Asynchronous Federated Learning Over Wireless Communication Networks

Wang

Zhang

Tian

et al. 2022

IEEE Trans. Wireless Commun.

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In this paper, federated learning (FL) over wireless networks is investigated. In each communication round, a subset of devices is selected to participate in the aggregation with limited time and energy. In order to minimize the convergence time, global loss and latency are jointly considered in a Stackelberg game based framework. Specifically, age of information (AoI) based device selection is considered at leader-level as a global loss minimization problem, while sub-channel assignment, computational resource allocation, and power allocation are considered at follower-level as a latency minimization problem. By dividing the follower-level problem into two sub-problems, the best response of the follower is obtained by a monotonic optimization based resource allocation algorithm and a matching based subchannel assignment algorithm. By deriving the upper bound of convergence rate, the leader-level problem is reformulated, and then a list based device selection algorithm is proposed to achieve Stackelberg equilibrium. Simulation results indicate that the proposed device selection scheme outperforms other schemes in terms of the global loss, and the developed algorithms can significantly decrease the time consumption of computation and communication.

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“…The peak AoI and average AoI minimizing scheduling of multi-channel networks was investigated in [21]. In [22], [23], the authors constructed a feasible scheduling set by traversal and deduced an average-AoI minimizing scheduling strategy.…”

Section: B Related Workmentioning

confidence: 99%