Scheduling to Minimize Age of Synchronization in Wireless Broadcast Networks With Random Updates

Abstract

In this work, a wireless broadcast network with a base station (BS) sending random time-sensitive information updates to multiple users with interference constraints is considered. The Age of Synchronization (AoS), namely the amount of time elapsed since the information stored at the network user becomes desynchronized, is adopted to measure data freshness from the perspective of network users.Compared with the more widely used metric-the Age of Information (AoI), AoS accounts for the freshness of the randomly changing content. The AoS minimization scheduling problem is formulated into a discrete time Markov decision process and the optimal solution is approximated through structural finite state policy iteration. An index based heuristic scheduling policy based on restless multi-arm bandit (RMAB) is provided to further reduce computational complexity. Simulation results show that the proposed index policy can achieve compatible performance with the MDP and close to the AoS lower bound. Moreover, theoretic analysis and simulations reveal the differences between AoS and AoI.AoI minimization scheduling policy cannot guarantee a good AoS performance. Index TermsAge of information, Age of synchronization, Markov decision processes, Whittle's index.The authors are with the DRAFT 3 unreliable wireless broadcast network with random information updates. Similar models with error-free transmissions are considered in [14]-[16]. To the best of the authors' knowledge, this is the first time that AoS is considered as the performance measure of such model. Our contributions are summarized as follows:• We formulate the problem of minimizing AoS in a broadcast wireless network with unreliable communication links. The theoretic lower bound of average AoS over the entire network is derived.• The problem is reformulated into a Markov decision process (MDP). Based on the switching structure that is exploited in the paper, we approximate the optimal solution to the MDP by using structural finite state policy iteration.• To overcome the computational load by the MDP solution, we propose an index based heuristic algorithm based on restless multi-arm bandit (RMAB). We prove that the problem is indexable and derive the closed form expression of the Whittle's index. Simulation resultsshow that the Whittle's index policy can achieve AoS performance close to the MDP solution and the AoS lower bound.The remainder of this paper is organized as follows. The network model and the two metrics, AoI and AoS are introduced and compared in Sec. II. The AoS lower bound is derived in Sec. III. In Sec. IV, we reformulate the problem into a Markov decision process and propose a finite state structural policy iteration to approximate the MDP solution. In Sec. V, we propose an index based algorithm based on restless multi-arm bandit. The scheduling strategies are evaluated through simulations and analyzed in Sec. VI. Sec. VII draws the conclusion.Notations: Vectors are written in boldface letters. The probability of event A given condition B is denoted as...

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“…• Timeliness is an emerging time-domain performance requirement to future communication systems. Typical metrics of timeliness include the well-known age-of-information (AoI) [55], and its recently proposed variants such like age-of-task (AoT) [56] and age-ofsynchronization (AoS) [57]. Differing from the classical memoryless metric of latency, which focuses on the overall delay experienced by all data packets or service sessions throughout their delivery process, the concept of timeliness emphasizes the freshness of the latest data and service that are successfully delivered to the end user.…”

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“…• Timeliness is an emerging time-domain performance requirement to future communication systems. Typical metrics of timeliness include the well-known ageof-information (AoI) [45], and its recently proposed variants such like age-of-task (AoT) [46] and age-ofsynchronization (AoS) [47]. Differing from the classical memoryless metric of latency, which focuses on the overall delay experienced by all data packets or service sessions throughout their delivery process, the concept of timeliness emphasizes the freshness of the latest data and service that are successfully delivered to the end user.…”

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“…• Timeliness is identified as a critical time-based performance criterion for future communication systems. This concept is quantified through metrics such as the age of information (AoI) [18], as well as newer variations like age-of-task (AoT) [19] and age-of-synchronization (AoS) [20]. Diverging from the traditional metric of latency, which focuses on the total delay encountered by data packets or service sessions in transit, timeliness emphasizes the relevance of the most recently received data and services to the user.…”