Name
Title | ||
|---|---|---|
Edge-Assisted Spectrum Sharing for Freshness-Aware Industrial Wireless Networks: A Learning-Based Approach |
Abstract | ||
|---|---|---|
Information freshness is essential to industrial wireless networks (IWNs) and can be quantified by the age-of-information (AoI) metric. This paper addresses an AoI-aware spectrum sharing (AgeS) problem in IWNs, where multiple device-to-device (D2D) links opportunistically access the spectrum to satisfy their AoI constraints while maximizing primal links’ throughput. Particularly, we orchestrate the access of D2D links in a distributed manner. Since distributed scheduling results in incomplete observation, D2D links share the spectrum with uncertainty on the transmission environment. Therefore, we propose a distributed scheduling scheme, called D-age, to deal with the transmission uncertainty in the AgeS problem, where an adaptation of actor-critic method is adopted with AoI constraints tackled in the dual domain. To address the non-stationary environment and multi-agent credit assignment issue, cooperative multi-agent reinforcement learning (MARL) approach is developed, where multiple local actors are designed to guide D2D links to make real-time decisions via distributed scheduling policies, which are evaluated by an edge-assisted global critic with action-aware advantage functions. Integrated with graph attention networks (GATs), the critic selectively learns contextual information by assigning different importances to neighboring links, which enables the evaluation of scheduling policies in a scalable and computation-efficient manner. Theoretical guarantee of the time-averaged AoI constraints is provided and the effectiveness of D-age in terms of both AoI violation ratio and the capacity of primal links is demonstrated by simulation. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TWC.2022.3160857 | IEEE Transactions on Wireless Communications |
Keywords | DocType | Volume |
Age of information,spectrum sharing,edge-assisted IWNs,multi-agent reinforcement learning | Journal | 21 |
Issue | ISSN | Citations |
9 | 1536-1276 | 0 |
PageRank | References | Authors |
0.34 | 29 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingyan Li | 1 | 0 | 0.34 |
| Cai-Lian Chen | 2 | 831 | 98.98 |
| Huaqing Wu | 3 | 0 | 0.34 |
| Xinping Guan | 4 | 2791 | 253.38 |
| Xuemin Shen | 5 | 0 | 0.34 |