Multi-Source AoI-Constrained Resource Minimization under HARQ: Heterogeneous Sampling Processes

We consider a multi-source hybrid automatic repeat request (HARQ) based system, where a transmitter sends status update packets of random arrival (i.e., uncontrollable sampling) and generate-at-will (i.e., controllable sampling) sources to a destination through an error-prone channel. We develop tra...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology Vol. 73; no. 1; pp. 1 - 15
Main Authors: Vilni, Saeid Sadeghi, Moltafet, Mohammad, Leinonen, Markus, Codreanu, Marian
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
AoI
Automatic repeat request
CMDP
Constraints
Controllability
Decoding
Dynamic programming
Iterative algorithms
Iterative methods
Lagrangian
Lyapunov
Machine learning
Markov processes
Monitoring
multi-source status update
Optimization
Policies
Process control
Protocols
Receivers
Sampling
Transmitters
Unknown environments
Wireless communication
Protocols
constrained Markov decision process (CMDP)
Lagrangian
Process control
Receivers
dynamic programming
Decoding
Age of information (AoI)
machine learning
Lyapunov
Wireless communication
Transmitters
multi-source status update
Monitoring
ISSN:0018-9545, 1939-9359, 1939-9359
Online Access:Get full text
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Summary:We consider a multi-source hybrid automatic repeat request (HARQ) based system, where a transmitter sends status update packets of random arrival (i.e., uncontrollable sampling) and generate-at-will (i.e., controllable sampling) sources to a destination through an error-prone channel. We develop transmission scheduling policies to minimize the average number of transmissions subject to an average age of information (AoI) constraint. First, we consider known environment (i.e., known system statistics) and develop a near-optimal deterministic transmission policy and a low-complexity dynamic transmission (LC-DT) policy. The former policy is derived by casting the main problem into a constrained Markov decision process (CMDP) problem, which is then solved using the Lagrangian relaxation, relative value iteration algorithm, and bisection. The LC-DT policy is developed via the drift-plus-penalty (DPP) method by transforming the main problem into a sequence of per-slot problems. Finally, we consider unknown environment and devise a learning-based transmission policy by relaxing the CMDP problem into an MDP problem using the DPP method and then adopting the deep Q-learning algorithm. Numerical results show that the proposed policies achieve near-optimal performance and illustrate the benefits of HARQ in status updating.
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ISSN:0018-9545
1939-9359
1939-9359
DOI:10.1109/TVT.2023.3310190