1,151 research outputs found
A Whittle Index Approach to Minimizing Functions of Age of Information
We consider a setting where multiple active sources send real-time updates
over a single-hop wireless broadcast network to a monitoring station. Our goal
is to design a scheduling policy that minimizes the time-average of general
non-decreasing cost functions of Age of Information.
We use a Whittle index based approach to find low complexity scheduling
policies that have good performance, for reliable as well as unreliable
channels. We prove that for a system with two sources, having possibly
different cost functions and reliable channels, the Whittle index policy is
exactly optimal. For reliable channels, we also derive structural properties of
an optimal policy, that suggest that the performance of the Whittle index
policy may be close to optimal in general. These results might also be of
independent interest in the study of restless multi-armed bandit problems with
similar underlying structure. Finally, we provide simulations comparing the
Whittle index policy with optimal scheduling policies found using dynamic
programming, which support our results.Comment: Accepted for Allerton'1
Learning and Communications Co-Design for Remote Inference Systems: Feature Length Selection and Transmission Scheduling
In this paper, we consider a remote inference system, where a neural network
is used to infer a time-varying target (e.g., robot movement), based on
features (e.g., video clips) that are progressively received from a sensing
node (e.g., a camera). Each feature is a temporal sequence of sensory data. The
learning performance of the system is determined by (i) the timeliness and (ii)
the temporal sequence length of the features, where we use Age of Information
(AoI) as a metric for timeliness. While a longer feature can typically provide
better learning performance, it often requires more channel resources for
sending the feature. To minimize the time-averaged inference error, we study a
learning and communication co-design problem that jointly optimizes feature
length selection and transmission scheduling. When there is a single
sensor-predictor pair and a single channel, we develop low-complexity optimal
co-designs for both the cases of time-invariant and time-variant feature
length. When there are multiple sensor-predictor pairs and multiple channels,
the co-design problem becomes a restless multi-arm multi-action bandit problem
that is PSPACE-hard. For this setting, we design a low-complexity algorithm to
solve the problem. Trace-driven evaluations suggest that the proposed
co-designs can significantly reduce the time-averaged inference error of remote
inference systems.Comment: 41 pages, 8 figures. The manuscript has been submitted to IEEE
Journal on Selected Areas in Information Theor
An Index Policy for Minimizing the Uncertainty-of-Information of Markov Sources
This paper focuses on the information freshness of finite-state Markov
sources, using the uncertainty of information (UoI) as the performance metric.
Measured by Shannon's entropy, UoI can capture not only the transition dynamics
of the Markov source but also the different evolutions of information quality
caused by the different values of the last observation. We consider an
information update system with M finite-state Markov sources transmitting
information to a remote monitor via m communication channels. Our goal is to
explore the optimal scheduling policy to minimize the sum-UoI of the Markov
sources. The problem is formulated as a restless multi-armed bandit (RMAB). We
relax the RMAB and then decouple the relaxed problem into M single bandit
problems. Analyzing the single bandit problem provides useful properties with
which the relaxed problem reduces to maximizing a concave and piecewise linear
function, allowing us to develop a gradient method to solve the relaxed problem
and obtain its optimal policy. By rounding up the optimal policy for the
relaxed problem, we obtain an index policy for the original RMAB problem.
Notably, the proposed index policy is universal in the sense that it applies to
general RMABs with bounded cost functions.Comment: 55 page
Scheduling Policies for Minimizing Age of Information in Broadcast Wireless Networks
We consider a wireless broadcast network with a base station sending
time-sensitive information to a number of clients through unreliable channels.
The Age of Information (AoI), namely the amount of time that elapsed since the
most recently delivered packet was generated, captures the freshness of the
information. We formulate a discrete-time decision problem to find a
transmission scheduling policy that minimizes the expected weighted sum AoI of
the clients in the network.
We first show that in symmetric networks a Greedy policy, which transmits the
packet with highest current age, is optimal. For general networks, we develop
three low-complexity scheduling policies: a randomized policy, a Max-Weight
policy and a Whittle's Index policy, and derive performance guarantees as a
function of the network configuration. To the best of our knowledge, this is
the first work to derive performance guarantees for scheduling policies that
attempt to minimize AoI in wireless networks with unreliable channels.
Numerical results show that both Max-Weight and Whittle's Index policies
outperform the other scheduling policies in every configuration simulated, and
achieve near optimal performance
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