3,004 research outputs found
Age-Optimal Updates of Multiple Information Flows
In this paper, we study an age of information minimization problem, where
multiple flows of update packets are sent over multiple servers to their
destinations. Two online scheduling policies are proposed. When the packet
generation and arrival times are synchronized across the flows, the proposed
policies are shown to be (near) optimal for minimizing any time-dependent,
symmetric, and non-decreasing penalty function of the ages of the flows over
time in a stochastic ordering sense
Delay Performance of MISO Wireless Communications
Ultra-reliable, low latency communications (URLLC) are currently attracting
significant attention due to the emergence of mission-critical applications and
device-centric communication. URLLC will entail a fundamental paradigm shift
from throughput-oriented system design towards holistic designs for guaranteed
and reliable end-to-end latency. A deep understanding of the delay performance
of wireless networks is essential for efficient URLLC systems. In this paper,
we investigate the network layer performance of multiple-input, single-output
(MISO) systems under statistical delay constraints. We provide closed-form
expressions for MISO diversity-oriented service process and derive
probabilistic delay bounds using tools from stochastic network calculus. In
particular, we analyze transmit beamforming with perfect and imperfect channel
knowledge and compare it with orthogonal space-time codes and antenna
selection. The effect of transmit power, number of antennas, and finite
blocklength channel coding on the delay distribution is also investigated. Our
higher layer performance results reveal key insights of MISO channels and
provide useful guidelines for the design of ultra-reliable communication
systems that can guarantee the stringent URLLC latency requirements.Comment: This work has been submitted to the IEEE for possible publication.
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A Survey on Delay-Aware Resource Control for Wireless Systems --- Large Deviation Theory, Stochastic Lyapunov Drift and Distributed Stochastic Learning
In this tutorial paper, a comprehensive survey is given on several major
systematic approaches in dealing with delay-aware control problems, namely the
equivalent rate constraint approach, the Lyapunov stability drift approach and
the approximate Markov Decision Process (MDP) approach using stochastic
learning. These approaches essentially embrace most of the existing literature
regarding delay-aware resource control in wireless systems. They have their
relative pros and cons in terms of performance, complexity and implementation
issues. For each of the approaches, the problem setup, the general solution and
the design methodology are discussed. Applications of these approaches to
delay-aware resource allocation are illustrated with examples in single-hop
wireless networks. Furthermore, recent results regarding delay-aware multi-hop
routing designs in general multi-hop networks are elaborated. Finally, the
delay performance of the various approaches are compared through simulations
using an example of the uplink OFDMA systems.Comment: 58 pages, 8 figures; IEEE Transactions on Information Theory, 201
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)
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