6 citations · 8 across the 11 of their papers we have counts for
6 papers · 2 filters
Deadline-Aware Multi-Agent Reinforcement Learning for TSN-Based Vehicular Edge Networks
Bernardo A. C. Pereira, Marcos Carvalho, Fatih Temiz +5
Vehicular edge computing (VEC) enables latency-sensitive applications by bringing computing and networking resources closer to vehicles. However, existing approaches often overlook…
WiSDoM: Wireless Sparse Decision Transformer with Mixture-of-Experts for Multi-Task Mobile Network Optimization
Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci
Emerging 6G wireless networks are expected to operate across diverse deployment scenarios, where variations in network topology, user mobility, traffic demand, and radio conditions…
Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application
Marcos Carvalho, Fatih Temiz, Shavbo Salehi +2
Time-sensitive networking (TSN) is increasingly integrated into mobile edge computing (MEC) to support applications with stringent latency requirements, such as extended reality (X…
Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks
Marcos Carvalho, Fatih Temiz, Shavbo Salehi +2
Time-Sensitive Networking (TSN) and Mobile Edge Computing (MEC) hold strong potential for enabling ultra-reliable low-latency communication for time-sensitive applications, such as…
Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers
Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci
Future wireless networks demand rapid adaptation to highly heterogeneous environments and dynamic task configurations, necessitating a shift from conventional rule-based and optimi…
Edge Learning via Federated Split Decision Transformers for Metaverse Resource Allocation
Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci
Mobile edge computing (MEC) based wireless metaverse services offer an untethered, immersive experience to users, where the superior quality of experience (QoE) needs to be achieve…