369 research outputs found
Minimizing the AoI in Resource-Constrained Multi-Source Relaying Systems: Dynamic and Learning-based Scheduling
We consider a multi-source relaying system where the independent sources
randomly generate status update packets which are sent to the destination with
the aid of a relay through unreliable links. We develop transmission scheduling
policies to minimize the sum average age of information (AoI) subject to
transmission capacity and long-run average resource constraints. We formulate a
stochastic control optimization problem. To solve the problem, a constrained
Markov decision process (CMDP) approach and a drift-plus-penalty method are
proposed. The CMDP problem is solved by transforming it into an MDP problem
using the Lagrangian relaxation method. We theoretically analyze the structure
of optimal policies for the MDP problem and subsequently propose a
structure-aware algorithm that returns a practical near-optimal policy. By the
drift-plus-penalty method, we devise a dynamic near-optimal low-complexity
policy. We also develop a model-free deep reinforcement learning policy, which
does not require the full knowledge of system statistics. To do so, we employ
the Lyapunov optimization theory and a dueling double deep Q-network.
Simulation results are provided to assess the performance of our policies and
validate the theoretical results. The results show up to 91% performance
improvement compared to a baseline policy.Comment: 30 Pages, preliminary results of this paper were presented at IEEE
Globecom 2021, https://ieeexplore.ieee.org/document/968594
Age of Semantics in Cooperative Communications: To Expedite Simulation Towards Real via Offline Reinforcement Learning
The age of information metric fails to correctly describe the intrinsic
semantics of a status update. In an intelligent reflecting surface-aided
cooperative relay communication system, we propose the age of semantics (AoS)
for measuring semantics freshness of the status updates. Specifically, we focus
on the status updating from a source node (SN) to the destination, which is
formulated as a Markov decision process (MDP). The objective of the SN is to
maximize the expected satisfaction of AoS and energy consumption under the
maximum transmit power constraint. To seek the optimal control policy, we first
derive an online deep actor-critic (DAC) learning scheme under the on-policy
temporal difference learning framework. However, implementing the online DAC in
practice poses the key challenge in infinitely repeated interactions between
the SN and the system, which can be dangerous particularly during the
exploration. We then put forward a novel offline DAC scheme, which estimates
the optimal control policy from a previously collected dataset without any
further interactions with the system. Numerical experiments verify the
theoretical results and show that our offline DAC scheme significantly
outperforms the online DAC scheme and the most representative baselines in
terms of mean utility, demonstrating strong robustness to dataset quality.Comment: This work has been submitted to the IEEE for possible publicatio
Update or Wait: How to Keep Your Data Fresh
In this work, we study how to optimally manage the freshness of information
updates sent from a source node to a destination via a channel. A proper metric
for data freshness at the destination is the age-of-information, or simply age,
which is defined as how old the freshest received update is since the moment
that this update was generated at the source node (e.g., a sensor). A
reasonable update policy is the zero-wait policy, i.e., the source node submits
a fresh update once the previous update is delivered and the channel becomes
free, which achieves the maximum throughput and the minimum delay.
Surprisingly, this zero-wait policy does not always minimize the age. This
counter-intuitive phenomenon motivates us to study how to optimally control
information updates to keep the data fresh and to understand when the zero-wait
policy is optimal. We introduce a general age penalty function to characterize
the level of dissatisfaction on data staleness and formulate the average age
penalty minimization problem as a constrained semi-Markov decision problem
(SMDP) with an uncountable state space. We develop efficient algorithms to find
the optimal update policy among all causal policies, and establish sufficient
and necessary conditions for the optimality of the zero-wait policy. Our
investigation shows that the zero-wait policy is far from the optimum if (i)
the age penalty function grows quickly with respect to the age, (ii) the packet
transmission times over the channel are positively correlated over time, or
(iii) the packet transmission times are highly random (e.g., following a
heavy-tail distribution)
Statistical Age-of-Information Optimization for Status Update over Multi-State Fading Channels
Age of information (AoI) is a powerful metric to evaluate the freshness of
information, where minimization of average statistics, such as the average AoI
and average peak AoI, currently prevails in guiding freshness optimization for
related applications. Although minimizing the statistics does improve the
received information's freshness for status update systems in the sense of
average, the time-varying fading characteristics of wireless channels often
cause uncertain yet frequent age violations. The recently-proposed statistical
AoI metric can better characterize more features of AoI dynamics, which
evaluates the achievable minimum peak AoI under the certain constraint on age
violation probability. In this paper, we study the statistical AoI minimization
problem for status update systems over multi-state fading channels, which can
effectively upper-bound the AoI violation probability but introduce the
prohibitively-high computing complexity. To resolve this issue, we tackle the
problem with a two-fold approach. For a small AoI exponent, the problem is
approximated via a fractional programming problem. For a large AoI exponent,
the problem is converted to a convex problem. Solving the two problems
respectively, we derive the near-optimal sampling interval for diverse status
update systems. Insightful observations are obtained on how sampling interval
shall be tuned as a decreasing function of channel state information (CSI).
Surprisingly, for the extremely stringent AoI requirement, the sampling
interval converges to a constant regardless of CSI's variation. Numerical
results verify effectiveness as well as superiority of our proposed scheme
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