Corpus ID: 237940101

Optimizing Age-of-Information in Adversarial Environments with Channel State Information

@article{Mandal2021OptimizingAI,
  title={Optimizing Age-of-Information in Adversarial Environments with Channel State Information},
  author={Avijit Mandal and Rajarshi Bhattacharjee and Abhishek Sinha},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.12353}
}
This paper considers a multi-user downlink scheduling problem with access to the channel state information at the transmitter (CSIT) to minimize the Age-of-Information (AoI) in a non-stationary environment. The non-stationary environment is modelled using a novel adversarial framework. In this setting, we propose a greedy scheduling policy, called MA-CSIT, that takes into account the current channel state information. We establish a finite upper bound on the competitive ratio achieved by the MA… Expand

References

SHOWING 1-10 OF 14 REFERENCES
Optimizing the Age-of-Information for Mobile Users in Adversarial and Stochastic Environments
TLDR
A large-deviation optimality result is established achieved by the greedy policy for minimizing the peak age of information for static users situated at a single cell. Expand
Competitive Algorithms for Minimizing the Maximum Age-of-Information
TLDR
Surprisingly, it is shown that there exists a simple online distributed scheduling policy with a finite competitive ratio for maximizing the freshness of information in this model and it is proved that the proposed policy is competitively optimal up to an O(logN) factor. Expand
Scheduling Policies for Minimizing Age of Information in Broadcast Wireless Networks
TLDR
This is the first work to derive performance guarantees for scheduling policies that attempt to minimize AoI in wireless networks with unreliable channels and shows that both the Max-Weight and Whittle’s Index policies outperform the other scheduling policies in every configuration simulated, and achieve near optimal performance. Expand
Optimizing Age of Information in Wireless Networks with Throughput Constraints
TLDR
This paper develops three low-complexity transmission scheduling policies that attempt to minimize AoI subject to minimum throughput requirements and evaluates their performance against the optimal policy, and develops a randomized policy, a Max-Weight policy and a Whittle's Index policy. Expand
Dynamic rate control algorithms for HDR throughput optimization
  • S. Borst, P. Whiting
  • Computer Science
  • Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213)
  • 2001
TLDR
It is shown that the 'best' user may be identified as the maximum-rate user when the feasible rates are weighed with some appropriately determined coefficients, and the optimal strategy may be viewed as a revenue-based policy. Expand
Convergence of proportional-fair sharing algorithms under general conditions
TLDR
It is shown that the ordinary differential equation (ODE) has a unique equilibrium and that it is characterized as optimizing a concave utility function, which shows that PFS is not ad-hoc, but actually corresponds to a reasonable maximization problem. Expand
Real-time status: How often should one update?
TLDR
A time-average age metric is employed for the performance evaluation of status update systems and the existence of an optimal rate at which a source must generate its information to keep its status as timely as possible at all its monitors is shown. Expand
Asymptotic analysis of proportional fair algorithm
  • J. Holtzman
  • Mathematics, Computer Science
  • 12th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications. PIMRC 2001. Proceedings (Cat. No.01TH8598)
  • 2001
TLDR
A more general result for two classes of users with different fading characteristics is given-the user class with more fading variability gets more throughput with a lower fraction of time transmitting. Expand
Competitive Online Algorithms
Preface I would like to thank BRICS and, in particular, Erik Meineche Schmidt for giving me the opportunity to teach a mini-course on competitive online algorithms at Aarhus University, August 27{29,Expand
Extreme URLLC: Vision, Challenges, and Key Enablers
TLDR
The intent of this article is to spearhead beyond-5G/6G mission-critical applications by laying out a holistic vision of xURLLC, its research challenges and enabling technologies, while providing key insights grounded in selected use cases. Expand
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