On the Robustness of Age for Learning-Based Wireless Scheduling in Unknown Environments
This addresses stability issues in wireless networks for applications like IoT or mobile communications, but it is incremental as it modifies an existing algorithmic approach.
The paper tackles the problem of wireless scheduling under unknown and abruptly changing channel conditions by proposing a learning-based policy that uses head-of-line age instead of virtual queue length, showing it matches state-of-the-art performance under i.i.d. conditions and ensures stability and rapid recovery from constraint infeasibility.
The constrained combinatorial multi-armed bandit model has been widely employed to solve problems in wireless networking and related areas, including the problem of wireless scheduling for throughput optimization under unknown channel conditions. Most work in this area uses an algorithm design strategy that combines a bandit learning algorithm with the virtual queue technique to track the throughput constraint violation. These algorithms seek to minimize the virtual queue length in their algorithm design. However, in networks where channel conditions change abruptly, the resulting constraints may become infeasible, leading to unbounded growth in virtual queue lengths. In this paper, we make the key observation that the dynamics of the head-of-line age, i.e. the age of the oldest packet in the virtual queue, make it more robust when used in algorithm design compared to the virtual queue length. We therefore design a learning-based scheduling policy that uses the head-of-line age in place of the virtual queue length. We show that our policy matches state-of-the-art performance under i.i.d. network conditions. Crucially, we also show that the system remains stable even under abrupt changes in channel conditions and can rapidly recover from periods of constraint infeasibility.