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1. How Useful is Delayed Feedback in AoI Minimization - A Study on Systems With Queues in Both Forward and Backward Directions

   https://doi.org/10.1109/ISIT50566.2022.9834697  

   Wang, Chih-Chun (July 2022, Proc. IEEE International Symposium on Information Theory)

   One canonical example of Age-Of-Information (AoI) minimization is the update-through-queues models. Existing results fall into two categories: The open-loop setting for which the sender is oblivious of the actual packet departure time, versus the closed-loop setting for which the decision is based on instantaneous Acknowledgement (ACK). Neither setting perfectly reflects modern networked systems, which almost always rely on feedback that experiences some delay. Motivated by this observation, this work subjects the ACK traffic to an independent queue so that the closed-loop decision is made based on delayed feedback. Near-optimal schedulers have been devised, which smoothly transition from the instantaneous-ACK to the open loop schemes depending on how long the feedback delay is. The results thus quantify the benefits of delayed feedback for AoI minimization in the update-through-queues systems. 

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2. Age-Optimal Low-Power Status Update over Time-Correlated Fading Channel

   https://doi.org/10.1109/TMC.2022.3160050  

   Yao, Guidan; Bedewy, Ahmed; Shroff, Ness B. (March 2023, IEEE Transactions on Mobile Computing)

   In this paper, we consider transmission scheduling in a status update system, where updates are generated periodically and transmitted over a Gilbert-Elliott fading channel. The goal is to minimize the long-run average age of information (AoI) under a long-run average energy constraint. We consider two practical cases to obtain channel state information (CSI): (i) without channel sensing and (ii) with delayed channel sensing. For (i), CSI is revealed by the feedback (ACK/NACK) of a transmission, but when no transmission occurs, CSI is not revealed. Thus, we have to balance tradeoffs across energy, AoI, channel exploration, and channel exploitation. The problem is formulated as a constrained partially observable Markov decision process (POMDP). We show that the optimal policy is a randomized mixture of no more than two stationary deterministic policies each of which is of a threshold-type in the belief on the channel. For (ii), (delayed) CSI is available via channel sensing. Then, the tradeoff is only between the AoI and energy. The problem is formulated as a constrained MDP. The optimal policy is shown to have a similar structure as in (i) but with an AoI associated threshold. With these, we develop an optimal structure-aware algorithm for each case. 

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3. Jointly Minimizing AoI Penalty and Network Cost Among Coexisting Source-Destination Pairs

   https://doi.org/10.1109/ISIT45174.2021.9518170  

   Tsai, Cho-Hsin; Wang, Chih-Chun (July 2021, 2021 IEEE International Symposium on Information Theory (ISIT))

   One main objective of ultra-low-latency communications is to minimize the data staleness at the receivers, recently characterized by a metric called Age-of-Information (AoI). While the question of when to send the next update packet has been the central subject of AoI minimization, each update packet also incurs the cost of transmission that needs to be jointly considered in a practical design. With the exponential growth of interconnected devices and the increasing risk of excessive resource consumption in mind, this work derives an optimal joint cost-and-AoI minimization solution for multiple coexisting source-destination (S-D) pairs. The results admit a new AoI-market-price-based interpretation and are applicable to the setting of (a) general heterogeneous AoI penalty functions and Markov delay distributions for each S-D pair, and (b) a general network cost function of aggregate throughput of all S-D pairs. Extensive simulation is used to demonstrate the superior performance of the proposed scheme. 

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4. Anti-Aging Scheduling in Single-Server Queues: A Systematic and Comparative Study

   https://doi.org/10.1109/INFOCOMWKSHPS50562.2020.9162767  

   Liu, Zhongdong; Huang, Liang; Li, Bin; Ji, Bo (July 2020, IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS))

   The Age-of-Information (AoI) is a new performance metric recently proposed for measuring the freshness of information in information-update systems. In this work, we conduct a systematic and comparative study to investigate the impact of scheduling policies on the AoI performance in single-server queues and provide useful guidelines for the design of AoI-efficient scheduling policies. Specifically, we first perform extensive simulations to demonstrate that the update-size information can be leveraged for achieving a substantially improved AoI compared to non-size-based (or arrival-time-based) policies. Then, by utilizing both the update-size and arrival-time information, we propose three AoI-based policies. Observing improved AoI performance of policies that allow service preemption and that prioritize informative updates, we further propose preemptive, informative, AoI-based scheduling policies. Our simulation results show that such policies empirically achieve the best AoI performance among all the considered policies. Interestingly, we also prove sample-path equivalence between some size-based policies and AoI-based policies. This provides an intuitive explanation for why some size-based policies, such as Shortest-Remaining-Processing-Time (SRPT), achieve a very good AoI performance. 

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5. Goal-Oriented Communications for Remote Inference Under Two-Way Delay with Memory

   https://doi.org/10.1109/ISIT57864.2024.10619298  

   Ari, Cagri; Shisher, Md_Kamran Chowdhury; Uysal, Elif; Sun, Yin (July 2024, IEEE)

   We study the design of a goal-oriented sampling and scheduling strategy through a channel with highly variable two-way random delay, which can exhibit memory (e.g., Delay and Disruption Tolerant Networks). The objective of the communication is to optimize the performance of remote inference, where an inference algorithm (e.g., a trained neural network) on the receiver side predicts a time-varying target signal using the data samples transmitted by a sensor. Previous formulations to this problem either assumed a channel with IID transmission delay, neglecting feedback delay or considered the monotonic relation that the performance only gets worse as the input information ages. We show how, with delayed feedback, one can effectively exploit the knowledge about delay memory through an index-based threshold policy. This policy minimizes the expected time-average inference error that can be monotone or non-monotone in age. The index function is expressed in terms of the Age of Information (AoI) on the receiver side and a parameter regarding the distribution of subsequent transmission delay, both of which can readily be tracked. 

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