Search Results for author:
Found 22 papers, 1 papers with code
Cooperative Multi-Agent Learning for Navigation via Structured State Abstraction
Learning a navigation policy along with a communication protocol in a MARL environment is highly complex due to the huge state space to be explored.
Federated Learning Games for Reconfigurable Intelligent Surfaces via Causal Representations
Equivalently, this corresponds to a game played between multiple RISs, as learning agents, in heterogeneous environments.
Codesign of Edge Intelligence and Automated Guided Vehicle Control
This work presents a harmonic design of autonomous guided vehicle (AGV) control, edge intelligence, and human input to enable autonomous transportation in industrial environments.
How Can Optical Communications Shape the Future of Deep Space Communications? A Survey
In this survey, we discuss the pros and cons of deep space optical communications (DSOC) and review physical and networking characteristics.
Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs
An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semi-autonomous IoT applications, examples of which include highly automated manufacturing cells or autonomously interacting harvesting machines.
Federated Distributionally Robust Optimization for Phase Configuration of RISs
In this article, we study the problem of robust reconfigurable intelligent surface (RIS)-aided downlink communication over heterogeneous RIS types in the supervised learning setting.
Joint Client Scheduling and Resource Allocation under Channel Uncertainty in Federated Learning
In this article we investigate the problem of client scheduling and resource block (RB) allocation to enhance the performance of model training using FL, over a pre-defined training duration under imperfect channel state information (CSI) and limited local computing resources.
Robust Reconfigurable Intelligent Surfaces via Invariant Risk and Causal Representations
In this paper, the problem of robust reconfigurable intelligent surface (RIS) system design under changes in data distributions is investigated.
Predictive Ultra-Reliable Communication: A Survival Analysis Perspective
Results show that the accuracy of detecting channel blocking events is higher using the model-based method for low to moderate reliability targets requiring low sample complexity.
Survival Analysis
Networking and Internet Architecture
Vehicular Cooperative Perception Through Action Branching and Federated Reinforcement Learning
Simulation results show the ability of the RL agents to efficiently learn the vehicles' association, RB allocation, and message content selection while maximizing vehicles' satisfaction in terms of the received sensory information.
BayGo: Joint Bayesian Learning and Information-Aware Graph Optimization
This article deals with the problem of distributed machine learning, in which agents update their models based on their local datasets, and aggregate the updated models collaboratively and in a fully decentralized manner.
Phase Configuration Learning in Wireless Networks with Multiple Reconfigurable Intelligent Surfaces
Reconfigurable Intelligent Surfaces (RISs) are recently gaining remarkable attention as a low-cost, hardware-efficient, and highly scalable technology capable of offering dynamic control of electro-magnetic wave propagation.
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications
Machine learning (ML) is a promising enabler for the fifth generation (5G) communication systems and beyond.
6G White Paper on Edge Intelligence
We present edge computing along with other 6G enablers as a key component to establish the future 2030 intelligent Internet technologies as shown in this series of 6G White Papers.
Deep Learning Assisted CSI Estimation for Joint URLLC and eMBB Resource Allocation
Multiple-input multiple-output (MIMO) is a key for the fifth generation (5G) and beyond wireless communication systems owing to higher spectrum efficiency, spatial gains, and energy efficiency.
Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocation
In this work, we propose a novel joint client scheduling and resource block (RB) allocation policy to minimize the loss of accuracy in federated learning (FL) over wireless compared to a centralized training-based solution, under imperfect channel state information (CSI).
Networking and Internet Architecture
Reinforcement Learning Based Vehicle-cell Association Algorithm for Highly Mobile Millimeter Wave Communication
Vehicle-to-everything (V2X) communication is a growing area of communication with a variety of use cases.
Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approach
Therefore, to effectively allocate power and RBs, the proposed approach allows the network to actively learn its dynamics by balancing a tradeoff between minimizing the probability that the vehicles' AoI exceeds a predefined threshold and maximizing the knowledge about the network dynamics.
Wireless Network Intelligence at the Edge
), requires a novel paradigm change calling for distributed, low-latency and reliable ML at the wireless network edge (referred to as edge ML).
Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications
In this paper, the problem of joint power and resource allocation (JPRA) for ultra-reliable low-latency communication (URLLC) in vehicular networks is studied.
Information Theory Information Theory
Federated Learning for Ultra-Reliable Low-Latency V2V Communications
It is shown that FL enables the proposed distributed method to estimate the tail distribution of queues with an accuracy that is very close to a centralized solution with up to 79\% reductions in the amount of data that need to be exchanged.
Backhaul-Aware Interference Management in the Uplink of Wireless Small Cell Networks
In this paper, a novel, backhaul-aware approach to interference management in wireless small cell networks is proposed.
