Deep Reinforcement Learning for Resource Constrained Multiclass Scheduling in Wireless Networks
This work aims to improve resource allocation and scheduling efficiency for wireless network operators facing dynamic and heterogeneous traffic conditions, offering an incremental improvement over existing methods.
This paper addresses resource-constrained multiclass scheduling in dynamic wireless networks with heterogeneous traffic and time-varying service rates. The authors propose a distributional Deep Deterministic Policy Gradient (DDPG) algorithm combined with Deep Sets and a novel Dueling Network, demonstrating consistent performance gains over state-of-the-art conventional methods on both synthetic and real data.
The problem of resource constrained scheduling in a dynamic and heterogeneous wireless setting is considered here. In our setup, the available limited bandwidth resources are allocated in order to serve randomly arriving service demands, which in turn belong to different classes in terms of payload data requirement, delay tolerance, and importance/priority. In addition to heterogeneous traffic, another major challenge stems from random service rates due to time-varying wireless communication channels. Various approaches for scheduling and resource allocation can be used, ranging from simple greedy heuristics and constrained optimization to combinatorics. Those methods are tailored to specific network or application configuration and are usually suboptimal. To this purpose, we resort to deep reinforcement learning (DRL) and propose a distributional Deep Deterministic Policy Gradient (DDPG) algorithm combined with Deep Sets to tackle the aforementioned problem. Furthermore, we present a novel way to use a Dueling Network, which leads to further performance improvement. Our proposed algorithm is tested on both synthetic and real data, showing consistent gains against state-of-the-art conventional methods from combinatorics, optimization, and scheduling metrics.