Balancing Data Freshness and Distortion in Real-time Status Updating with Lossy Compression

Hu, Shaoling; Chen, Wei

doi:10.1109/infocomwkshps50562.2020.9162945

Cited by 13 publications

(2 citation statements)

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“…By substituting Eq. ( 33) into optimization problem (32), we can obtain the equivalence of optimization problem (32) and optimization problem (34). Therefore, we just need to prove that optimization problem ( 34) is a linear optimization problem of x.…”

Section: Discussionmentioning

confidence: 99%

“…This section introduces how to obtain the optimal scheduling policy. From Theorem 5, we obtain a linear programming problem, which is much easier compared to optimization problem (32). However, due to the infinite number of variables, the optimal tradeoff between the AoI and the average power consumption remains hard to obtain.…”

Section: B Semi-threshold Policy Based Algorithmmentioning

confidence: 99%

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Adaptive Power and Rate Control for Real-time Status Updating over Fading Channels

Wang

Chen

2020

Preprint

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Age of Information (AoI) has attracted much attention recently due to its capability of characterizing the freshness of information. To improve information freshness over fading channels, efficient scheduling methods are highly desired for wireless transmissions. However, due to the channel instability and arrival randomness, optimizing AoI is very challenging. In this paper, we are interested in the AoI-optimal transmissions with rate-adaptive transmission schemes in a buffer-aware system. More specifically, we utilize a probabilistic scheduling method to minimize the AoI while satisfying an average power constraint. By characterizing the probabilistic scheduling policy with a Constrained Markov Decision Process (CMDP), we formulate a Linear Programming (LP) problem. Further, a low complexity algorithm is presented to obtain the optimal scheduling policy, which is proved to belong to a set of semi-threshold-based policies. Numerical results verify the reduction in computational complexity and the optimality of semithreshold-based policy, which indicates that we can achieve well real-time service with a low calculating complexity.

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Section: Discussionmentioning

confidence: 99%

Section: B Semi-threshold Policy Based Algorithmmentioning

confidence: 99%