In this paper,we investigate the minimization of age of information(AoI),a metric that measures the information freshness,at the network edge with unreliable wireless communications.Particularly,we consider a set of u...In this paper,we investigate the minimization of age of information(AoI),a metric that measures the information freshness,at the network edge with unreliable wireless communications.Particularly,we consider a set of users transmitting status updates,which are collected by the user randomly over time,to an edge server through unreliable orthogonal channels.It begs a natural question:with random status update arrivals and obscure channel conditions,can we devise an intelligent scheduling policy that matches the users and channels to stabilize the queues of all users while minimizing the average AoI?To give an adequate answer,we define a bipartite graph and formulate a dynamic edge activation problem with stability constraints.Then,we propose an online matching while learning algorithm(MatL)and discuss its implementation for wireless scheduling.Finally,simulation results demonstrate that the MatL is reliable to learn the channel states and manage the users’buffers for fresher information at the edge.展开更多
With the rapid development of marine activities,there has been an increasing use of Internet-of-Things(IoT) devices for maritime applications.This leads to a growing demand for high-speed and ultra-reliable maritime c...With the rapid development of marine activities,there has been an increasing use of Internet-of-Things(IoT) devices for maritime applications.This leads to a growing demand for high-speed and ultra-reliable maritime communications.Current maritime communication networks (MCNs) mainly rely on satellites and on-shore base stations (BSs).The former generally provides limited transmission rate while the latter lacks wide-area coverage capability.As a result,the development of current MCN lags far behind the terrestrial fifth-generation (5G) network.展开更多
基金supported in part by Shanghai Pujiang Program under Grant No.21PJ1402600in part by Natural Science Foundation of Chongqing,China under Grant No.CSTB2022NSCQ-MSX0375+4 种基金in part by Song Shan Laboratory Foundation,under Grant No.YYJC022022007in part by Zhejiang Provincial Natural Science Foundation of China under Grant LGJ22F010001in part by National Key Research and Development Program of China under Grant 2020YFA0711301in part by National Natural Science Foundation of China under Grant 61922049。
文摘In this paper,we investigate the minimization of age of information(AoI),a metric that measures the information freshness,at the network edge with unreliable wireless communications.Particularly,we consider a set of users transmitting status updates,which are collected by the user randomly over time,to an edge server through unreliable orthogonal channels.It begs a natural question:with random status update arrivals and obscure channel conditions,can we devise an intelligent scheduling policy that matches the users and channels to stabilize the queues of all users while minimizing the average AoI?To give an adequate answer,we define a bipartite graph and formulate a dynamic edge activation problem with stability constraints.Then,we propose an online matching while learning algorithm(MatL)and discuss its implementation for wireless scheduling.Finally,simulation results demonstrate that the MatL is reliable to learn the channel states and manage the users’buffers for fresher information at the edge.
文摘With the rapid development of marine activities,there has been an increasing use of Internet-of-Things(IoT) devices for maritime applications.This leads to a growing demand for high-speed and ultra-reliable maritime communications.Current maritime communication networks (MCNs) mainly rely on satellites and on-shore base stations (BSs).The former generally provides limited transmission rate while the latter lacks wide-area coverage capability.As a result,the development of current MCN lags far behind the terrestrial fifth-generation (5G) network.
