1 citations · 1 across the 5 of their papers we have counts for
7 papers
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…
Vertical Federated Learning for Failure-Cause Identification in Disaggregated Microwave Networks
Fatih Temiz, Memedhe Ibrahimi, Francesco Musumeci +2
Machine Learning (ML) has proven to be a promising solution to provide novel scalable and efficient fault management solutions in modern 5G-and-beyond communication networks. In th…