Technology Category
Planning, Routing, and Scheduling: Scheduling under UncertaintyReasoning under Uncertainty: Stochastic OptimizationSearch and Optimization: Mixed Discrete/Continuous Search
Optimizing Age of Information in Networks with Large and Small Updates
🤖 AI Summary
In unreliable single-hop wireless broadcast networks, heterogeneous update sizes—ranging from byte-level to megabyte-level—compete for channel access, degrading information freshness, quantified by Age of Information (AoI).
Method: We first derive the theoretical lower bound on AoI under mixed-size updates. Then, we propose a novel stochastic scheduling framework that jointly models transmission outages and persistence, and design a tailored Lyapunov function to construct an AoI-driven Max-Weight policy with constant-factor optimality guarantees.
Contribution/Results: Our approach breaks from conventional size-agnostic scheduling paradigms, establishing—both theoretically and practically—a new size-aware AoI optimization pathway. Extensive simulations demonstrate significant performance gains over state-of-the-art baselines, validating near-optimal AoI performance and practical viability.
Technology Category
Application Category
📝 Abstract
Modern sensing and monitoring applications typically consist of sources transmitting updates of different sizes, ranging from a few bytes (position, temperature, etc.) to multiple megabytes (images, video frames, LIDAR point scans, etc.). Existing approaches to wireless scheduling for information freshness typically ignore this mix of large and small updates, leading to suboptimal performance. In this paper, we consider a single-hop wireless broadcast network with sources transmitting updates of different sizes to a base station over unreliable links. Some sources send large updates spanning many time slots while others send small updates spanning only a few time slots. Due to medium access constraints, only one source can transmit to the base station at any given time, thus requiring careful design of scheduling policies that takes the sizes of updates into account. First, we derive a lower bound on the achievable Age of Information (AoI) by any transmission scheduling policy. Second, we develop optimal randomized policies that consider both switching and no-switching during the transmission of large updates. Third, we introduce a novel Lyapunov function and associated analysis to propose an AoI-based Max-Weight policy that has provable constant factor optimality guarantees. Finally, we evaluate and compare the performance of our proposed scheduling policies through simulations, which show that our Max-Weight policy achieves near-optimal AoI performance.
Problem
Research questions and friction points this paper is trying to address.
Optimizing Age of Information for mixed-size updates
Designing scheduling policies for unreliable wireless networks
Achieving near-optimal performance with large and small updates
Innovation
Methods, ideas, or system contributions that make the work stand out.
Optimal randomized policies for update scheduling
Lyapunov-based AoI Max-Weight policy
Considers mixed large and small updates
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