Age-of-Information Revisited: Two-way Delay and Distribution-oblivious Online Algorithm

Tsai, Cho-Hsin; Wang, Chih-Chun

doi:10.1109/isit44484.2020.9174306

Cited by 23 publications

(22 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The optimal sampling policy may wait before transmission only if it receives positive feedback. This is quite different from most of the previous works assuming reliable channels, e.g., [4], [5], [8], [9], where all samples may wait for some time before transmissions. Second, by (16), in each epoch, the optimal waiting time for the first sample has a simple threshold type on the current age Y i−1,Mi−1 + X i,1 .…”

Section: Optimal Sampling Policycontrasting

confidence: 60%

“…According to Theorem 1, when the forward channel is reliable, i.e., M i = 1 or α = 0 for all i, then the result reduces to the study in [9]. Note that when M i = 1, Assumption 1 is negligible.…”

Section: Proof See Appendix Kmentioning

confidence: 95%

“…The optimal sampling solution is a deterministic or randomized threshold policy based on the objective value and the sampling rate constraint. In [9], the paper considers a two-way delay model, where both the forward and backward directions have a random delay, and provides a low complexity algorithm with a quadratic convergence rate to compute the optimal threshold. An optimal joint cost-and-AoI minimization solution is provided in [8] for multiple coexisting sourcedestination pairs with heterogeneous AoI penalty functions.…”

Section: Introductionmentioning

confidence: 99%

“…Early studies on optimizing sampling assuming reliable channels with random delays have shown that the sampling problem is decomposed into a per-sample problem. The per-sample problem can be further solved by convex optimization (e.g., [2], [4], [5], [9]) or optimal stopping rules (e.g., [6], [10], [15]). Our study assuming an unreliable channel is equivalent to a per-epoch problem containing multiple samples until the successful packet delivery.…”

Section: Introductionmentioning

confidence: 99%

“…Our study assuming an unreliable channel is equivalent to a per-epoch problem containing multiple samples until the successful packet delivery. Therefore, the reduced problem is still a Markov Decision Process (MDP) with uncountable state spaces, which is the key difference with past works, e.g., [2], [4]- [6], [9], [10], [15] and faces the curse of dimensionality. We compare our technical differences with past works in Section V-C.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Optimal Sampling for Data Freshness: Unreliable Transmissions with Random Two-way Delay

Pan¹,

Bedewy²,

Yin³

et al. 2022

Preprint

View full text Add to dashboard Cite

In this paper, we study a sampling problem, in which freshly sampled data is sent to a remote destination via an unreliable channel, and acknowledgments are sent back on a feedback channel. Both the forward and feedback channels are subject to random transmission times. We optimize the sampling strategy at the source (e.g., a sensor), aiming to enhance the freshness of data samples delivered to the destination (e.g., an estimator). This sampling problem is motivated by a distributed sensing system, where an estimator estimates a signal by combining noisy signal observations collected from a local sensor and accurate signal samples received from a remote sensor. We show that the optimal estimation error is an increasing function of the age of received signal samples. The optimal sampling problem for general non-decreasing age functions is formulated as an MDP with an uncountable state space. An exact solution to this problem is derived, which has a simple thresholdtype structure. The threshold can be calculated by low-complexity bisection search and fixed-point iterations. We find that, after a successful packet delivery, the optimal sampler may wait before taking the next sample and sending it out, whereas no waiting time should be added if the previous transmission failed. 1 This assumption arises from the stop-and-wait Automatic Repeat Request (ARQ) mechanism. When the backward delay X i,j = 0, the policy that samples ahead of receiving feedback is always suboptimal. Because such a policy takes a new sample when the channel is busy and can be replaced by a policy that samples at exact time of receiving feedback [5]. When X i,j = 0, however, it may be optimal to transmit before receiving acknowledgement, which is out of the scope of this paper.

show abstract

Section: Optimal Sampling Policycontrasting

confidence: 60%

Section: Proof See Appendix Kmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%