[Paper] On the Robustness of Age for Learning-Based Wireless Scheduling in Unknown Environments

Source: arXiv - 2601.05956v1

Overview

This paper tackles a classic problem in wireless networking: how to schedule transmissions when the channel quality is unknown and can change suddenly. The authors show that, instead of relying on the traditional “virtual‑queue length” metric to keep throughput constraints in check, using the head‑of‑line (HoL) age of the oldest packet yields a scheduler that stays stable even when the environment flips abruptly.

Key Contributions

• HoL‑age‑centric design: Introduces a learning‑based scheduler that replaces virtual‑queue length with the age of the oldest packet, proving it to be more robust to sudden channel changes.
• Theoretical guarantees: Demonstrates that the new policy attains the same regret and throughput‑optimality bounds as state‑of‑the‑art algorithms under i.i.d. (independent and identically distributed) channel conditions.
• Stability under infeasibility: Provides rigorous analysis showing that the system remains stable—and quickly recovers—when constraints become temporarily infeasible due to abrupt channel degradation.
• Practical evaluation: Empirical simulations confirm that the HoL‑age scheduler matches or exceeds existing methods in both steady‑state and volatile scenarios.

Methodology

1. Problem framing: The authors model wireless scheduling as a constrained combinatorial multi‑armed bandit (CC‑MAB). Each “arm” corresponds to a feasible set of links to activate, and the unknown reward is the instantaneous channel capacity.
2. Traditional approach recap: Prior work couples a bandit learner (e.g., UCB or Thompson Sampling) with a virtual queue that accumulates any throughput‑constraint violation. The algorithm tries to keep the virtual‑queue length small.
3. Key insight: When the channel suddenly worsens, the virtual queue can explode because the algorithm keeps trying to satisfy an impossible constraint. However, the age of the oldest packet in that queue grows much more slowly and provides a natural “urgency” signal.
4. Algorithm design:
   • Maintain a single scalar per flow: the HoL age (how long the oldest backlogged packet has waited).
   • At each time slot, feed the current HoL ages into a bandit‑based decision rule that selects a link set maximizing a weighted sum of estimated rewards and age‑based penalties.
   • Update the age counters based on successful transmissions and newly arriving packets.
5. Analysis technique: The authors extend Lyapunov drift arguments to the HoL‑age metric, proving that the drift can be bounded even when the underlying constraints are temporarily infeasible.

Results & Findings

The key takeaway is that the HoL‑age scheduler inherits the strong performance guarantees of existing bandit‑based policies under normal conditions, while dramatically improving robustness when the environment becomes hostile.

Practical Implications

• More resilient MAC layers: Implementations of Wi‑Fi, LTE‑Advanced, or 5G NR that need to adapt to fast fading or sudden interference can embed the HoL‑age metric into their scheduling logic, reducing the risk of buffer overflow and packet loss.
• Simplified state tracking: Maintaining a single age counter per flow is cheaper than updating full virtual‑queue vectors, which can be attractive for low‑power IoT devices or edge routers with limited memory.
• Faster recovery after outages: Networks that experience intermittent jamming, spectrum sharing conflicts, or rapid mobility (e.g., vehicular networks) can regain stability within a few hundred milliseconds, improving QoS for latency‑sensitive applications.
• Compatibility with existing bandit libraries: The algorithm plugs into standard UCB/Thompson Sampling frameworks; developers only need to replace the queue‑length penalty with an age‑based term.

Limitations & Future Work

• Assumption of single‑hop flows: The analysis focuses on a single scheduling layer; extending to multi‑hop routing with end‑to‑end age metrics remains open.
• Scalability of combinatorial action space: While the HoL‑age metric itself is lightweight, the underlying combinatorial bandit problem can still be computationally intensive for very large networks; approximate solvers may be needed.
• Real‑world validation: The paper validates the approach via simulations. Deploying the scheduler on actual hardware (e.g., SDR testbeds) would confirm its practical overhead and robustness.
• Adaptive age weighting: Future work could explore dynamic tuning of the age penalty based on observed channel volatility, potentially yielding even faster adaptation.

Authors

• Juaren Steiger
• Bin Li

Paper Information

• arXiv ID: 2601.05956v1
• Categories: cs.LG
• Published: January 9, 2026
• PDF: Download PDF