157 citations · 473 across the 49 of their papers we have counts for
33 papers · 1 filter
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…
LocAttMamba: A Low-Complexity Mamba Framework with Attention-Based Multi-AP Fusion for Indoor Localization
MohammadMahdi Ghadaksaz, Mohammad Farzanullah, Melike Erol-Kantarci
Accurate and low-complexity indoor localization is important for location-based services in fifth generation (5G) and sixth generation (6G) networks, where positioning devices oper…
Distributed Attacks over Federated Reinforcement Learning-enabled Cell Sleep Control
Han Zhang, Hao Zhou, Medhat Elsayed +4
Federated learning (FL) is particularly useful in wireless networks due to its distributed implementation and privacy-preserving features. However, as a distributed learning system…
Channel Selection for Wi-Fi 7 Multi-Link Operation via Optimistic-Weighted VDN and Parallel Transfer Reinforcement Learning
Pedro Enrique Iturria-Rivera, Marcel Chenier, Bernard Herscovici +2
Dense and unplanned IEEE 802.11 Wireless Fidelity(Wi-Fi) deployments and the continuous increase of throughput and latency stringent services for users have led to machine learning…
Intent-driven Intelligent Control and Orchestration in O-RAN Via Hierarchical Reinforcement Learning
Md Arafat Habib, Hao Zhou, Pedro Enrique Iturria-Rivera +5
rApps and xApps need to be controlled and orchestrated well in the open radio access network (O-RAN) so that they can deliver a guaranteed network performance in a complex multi-ve…
Heterogeneous GNN-RL Based Task Offloading for UAV-aided Smart Agriculture
Turgay Pamuklu, Aisha Syed, W. Sean Kennedy +1
Having unmanned aerial vehicles (UAVs) with edge computing capability hover over smart farmlands supports Internet of Things (IoT) devices with low processing capacity and power to…
On-Device Intelligence for 5G RAN: Knowledge Transfer and Federated Learning enabled UE-Centric Traffic Steering
Han Zhang, Hao Zhou, Medhat Elsayed +4
Traffic steering (TS) is a promising approach to support various service requirements and enhance transmission reliability by distributing network traffic loads to appropriate base…
RL meets Multi-Link Operation in IEEE 802.11be: Multi-Headed Recurrent Soft-Actor Critic-based Traffic Allocation
Pedro Enrique Iturria Rivera, Marcel Chenier, Bernard Herscovici +2
IEEE 802.11be -Extremely High Throughput-, commercially known as Wireless-Fidelity (Wi-Fi) 7 is the newest IEEE 802.11 amendment that comes to address the increasingly throughput h…
To Risk or Not to Risk: Learning with Risk Quantification for IoT Task Offloading in UAVs
Anne Catherine Nguyen, Turgay Pamuklu, Aisha Syed +2
A deep reinforcement learning technique is presented for task offloading decision-making algorithms for a multi-access edge computing (MEC) assisted unmanned aerial vehicle (UAV) n…
Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learning
Marco Skocaj, Pedro Enrique Iturria Rivera, Roberto Verdone +1
Federated Learning (FL) has emerged as a promising framework for distributed training of AI-based services, applications, and network procedures in 6G. One of the major challenges…
Hierarchical Reinforcement Learning Based Traffic Steering in Multi-RAT 5G Deployments
Md Arafat Habib, Hao Zhou, Pedro Enrique Iturria-Rivera +5
In 5G non-standalone mode, an intelligent traffic steering mechanism can vastly aid in ensuring smooth user experience by selecting the best radio access technology (RAT) from a mu…
Hierarchical Deep Q-Learning Based Handover in Wireless Networks with Dual Connectivity
Pedro Enrique Iturria Rivera, Medhat Elsayed, Majid Bavand +3
5G New Radio proposes the usage of frequencies above 10 GHz to speed up LTE's existent maximum data rates. However, the effective size of 5G antennas and consequently its repercuss…
Traffic Steering for 5G Multi-RAT Deployments using Deep Reinforcement Learning
Md Arafat Habib, Hao Zhou, Pedro Enrique Iturria Rivera +5
In 5G non-standalone mode, traffic steering is a critical technique to take full advantage of 5G new radio while optimizing dual connectivity of 5G and LTE networks in multiple rad…
Knowledge Transfer and Reuse: A Case Study of AI-enabled Resource Management in RAN Slicing
Hao Zhou, Melike Erol-Kantarci, Vincent Poor
An efficient resource management scheme is critical to enable network slicing in 5G networks and in envisioned 6G networks, and artificial intelligence (AI) techniques offer promis…
Cooperate or not Cooperate: Transfer Learning with Multi-Armed Bandit for Spatial Reuse in Wi-Fi
Pedro Enrique Iturria-Rivera, Marcel Chenier, Bernard Herscovici +2
The exponential increase of wireless devices with highly demanding services such as streaming video, gaming and others has imposed several challenges to Wireless Local Area Network…
Reinforcement Learning Based Resource Allocation for Network Slices in O-RAN Midhaul
Nien Fang Cheng, Turgay Pamuklu, Melike Erol-Kantarci
Network slicing envisions the 5th generation (5G) mobile network resource allocation to be based on different requirements for different services, such as Ultra-Reliable Low Latenc…
IoT-Aerial Base Station Task Offloading with Risk-Sensitive Reinforcement Learning for Smart Agriculture
Turgay Pamuklu, Anne Catherine Nguyen, Aisha Syed +2
Aerial base stations (ABSs) allow smart farms to offload processing responsibility of complex tasks from internet of things (IoT) devices to ABSs. IoT devices have limited energy a…
Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networks
Anne Catherine Nguyen, Turgay Pamuklu, Aisha Syed +2
The fifth and sixth generations of wireless communication networks are enabling tools such as internet of things devices, unmanned aerial vehicles (UAVs), and artificial intelligen…
Federated Deep Reinforcement Learning for Resource Allocation in O-RAN Slicing
Han Zhang, Hao Zhou, Melike Erol-Kantarci
Recently, open radio access network (O-RAN) has become a promising technology to provide an open environment for network vendors and operators. Coordinating the x-applications (xAP…
Reinforcement Learning-Based Deadline and Battery-Aware Offloading in Smart Farm IoT-UAV Networks
Anne Catherine Nguyen, Turgay Pamuklu, Aisha Syed +2
Unmanned aerial vehicles (UAVs) with mounted base stations are a promising technology for monitoring smart farms. They can provide communication and computation services to extensi…
Team Learning-Based Resource Allocation for Open Radio Access Network (O-RAN)
Han Zhang, Hao Zhou, Melike Erol-Kantarci
Recently, the concept of open radio access network (O-RAN) has been proposed, which aims to adopt intelligence and openness in the next generation radio access networks (RAN). It p…
Knowledge Transfer based Radio and Computation Resource Allocation for 5G RAN Slicing
Hao Zhou, Melike Erol-Kantarci
To implement network slicing in 5G, resource allocation is a key function to allocate limited network resources such as radio and computation resources to multiple slices. However,…
Competitive Multi-Agent Load Balancing with Adaptive Policies in Wireless Networks
Pedro Enrique Iturria Rivera, Melike Erol-Kantarci
Using Machine Learning (ML) techniques for the next generation wireless networks have shown promising results in the recent years, due to high learning and adaptation capability of…
Dynamic CU-DU Selection for Resource Allocation in O-RAN Using Actor-Critic Learning
Shahram Mollahasani, Melike Erol-Kantarci, Rodney Wilson
Recently, there has been tremendous efforts by network operators and equipment vendors to adopt intelligence and openness in the next generation radio access network (RAN). The goa…