21 citations · 32 across the 10 of their papers we have counts for
10 papers
FedCritic-MIMO: Communication-Efficient Serverless Federated Critic Learning for Massive-MIMO Resource Control in Open and Disaggregated 6G RANs
Amin Farajzadeh, Melike Erol-Kantarci
This paper proposes FedCritic-MIMO, a communication-efficient serverless federated multi-agent reinforcement learning framework for AI-native resource control across independently…
FedCritic: Serverless Federated Critic Learning-based Resource Allocation for Multi-Cell OFDMA in 6G
Amin Farajzadeh, Melike Erol-Kantarci
In sixth-generation (6G) ultra-dense networks, aggressive frequency reuse amplifies inter-cell interference (ICI), making multi-cell orthogonal frequency-division multiple access (…
Data-Driven Spectrum Demand Prediction: A Spatio-Temporal Framework with Transfer Learning
Amin Farajzadeh, Hongzhao Zheng, Sarah Dumoulin +3
Accurate spectrum demand prediction is crucial for informed spectrum allocation, effective regulatory planning, and fostering sustainable growth in modern wireless communication ne…
Federated Learning in NTNs: Design, Architecture and Challenges
Amin Farajzadeh, Animesh Yadav, Halim Yanikomeroglu
Non-terrestrial networks (NTNs) are emerging as a core component of future 6G communication systems, providing global connectivity and supporting data-intensive applications. In th…
Tackling Non-IIDness in HAPS-Aided Federated Learning
Amin Farajzadeh, Animesh Yadav, Halim Yanikomeroglu
High-altitude platform stations (HAPS) enable large-scale federated learning (FL) in non-terrestrial networks (NTN) by providing wide-area coverage and predominantly line-of-sight…
Multi-Tier Hierarchical Federated Learning-assisted NTN for Intelligent IoT Services
Amin Farajzadeh, Animesh Yadav, Halim Yanikomeroglu
In the ever-expanding landscape of the IoT, managing the intricate network of interconnected devices presents a fundamental challenge. This leads us to ask: "What if we invite the…
FLSTRA: Federated Learning in Stratosphere
Amin Farajzadeh, Animesh Yadav, Omid Abbasi +2
We propose a federated learning (FL) in stratosphere (FLSTRA) system, where a high altitude platform station (HAPS) facilitates a large number of terrestrial clients to collaborati…
Self-Evolving Integrated Vertical Heterogeneous Networks
Amin Farajzadeh, Mohammad G. Khoshkholgh, Halim Yanikomeroglu +1
6G and beyond networks tend towards fully intelligent and adaptive design in order to provide better operational agility in maintaining universal wireless access and supporting a w…
Mobility-assisted Over-the-Air Computation for Backscatter Sensor Networks
Amin Farajzadeh, Ozgur Ercetin, Halim Yanikomeroglu
Future intelligent systems will consist of a massive number of battery-less sensors, where quick and accurate aggregation of sensor data will be of paramount importance. Over-the-a…
UAV Data Collection over NOMA Backscatter Networks: UAV Altitude and Trajectory Optimization
Amin Farajzadeh, Ozgur Ercetin, Halim Yanikomeroglu
The recent evolution of ambient backscattering technology has the potential to provide long-range and low-power wireless communications. In this work, we study the unmanned aerial…