23 citations · 58 across the 52 of their papers we have counts for
20 papers · 1 filter
Age Optimal Sampling for Unreliable Channels under Unknown Channel Statistics
Hongyi He, Haoyue Tang, Jiayu Pan +3
In this paper, we study a system in which a sensor forwards status updates to a receiver through an error-prone channel, while the receiver sends the transmission results back to t…
SANDWICH: Towards an Offline, Differentiable, Fully-Trainable Wireless Neural Ray-Tracing Surrogate
Yifei Jin, Ali Maatouk, Sarunas Girdzijauskas +3
Wireless ray-tracing (RT) is emerging as a key tool for three-dimensional (3D) wireless channel modeling, driven by advances in graphical rendering. Current approaches struggle to…
SplitLLM: Collaborative Inference of LLMs for Model Placement and Throughput Optimization
Akrit Mudvari, Yuang Jiang, Leandros Tassiulas
Large language models (LLMs) have been a disruptive innovation in recent years, and they play a crucial role in our daily lives due to their ability to understand and generate huma…
Long Sequence Modeling with Attention Tensorization: From Sequence to Tensor Learning
Aosong Feng, Rex Ying, Leandros Tassiulas
As the demand for processing extended textual data grows, the ability to handle long-range dependencies and maintain computational efficiency is more critical than ever. One of the…
Throughput-Optimal Scheduling via Rate Learning
Panagiotis Promponas, Víctor Valls, Konstantinos Nikolakakis +2
We study the problem of designing scheduling policies for communication networks. This problem is often addressed with max-weight-type approaches since they are throughput-optimal.…
Over-the-Air Federated Learning via Weighted Aggregation
Seyed Mohammad Azimi-Abarghouyi, Leandros Tassiulas
This paper introduces a new federated learning scheme that leverages over-the-air computation. A novel feature of this scheme is the proposal to employ adaptive weights during aggr…
LitFM: A Retrieval Augmented Structure-aware Foundation Model For Citation Graphs
Jiasheng Zhang, Jialin Chen, Ali Maatouk +6
With the advent of large language models (LLMs), managing scientific literature via LLMs has become a promising direction of research. However, existing approaches often overlook t…
Tele-LLMs: A Series of Specialized Large Language Models for Telecommunications
Ali Maatouk, Kenny Chirino Ampudia, Rex Ying +1
The emergence of large language models (LLMs) has significantly impacted various fields, from natural language processing to sectors like medicine and finance. However, despite the…
On the Capacity of the Quantum Switch with and without Entanglement Decoherence
Víctor Valls, Panagiotis Promponas, Leandros Tassiulas
This paper studies the capacity of the quantum switch for two decoherence models: when link-level entanglements last (i) for a time slot, or (ii) until they are used to serve a req…
A Brief Introduction to Quantum Network Control
Víctor Valls, Panagiotis Promponas, Leandros Tassiulas
Quantum networking is an emerging area with the potential to transform information processing and communications. In this paper, we present a brief introduction to quantum network…
A Genetic Approach to Minimising Gate and Qubit Teleportations for Multi-Processor Quantum Circuit Distribution
Oliver Crampton, Panagiotis Promponas, Richard Chen +3
Distributed Quantum Computing (DQC) provides a means for scaling available quantum computation by interconnecting multiple quantum processor units (QPUs). A key challenge in this d…
Compiler for Distributed Quantum Computing: a Reinforcement Learning Approach
Panagiotis Promponas, Akrit Mudvari, Luca Della Chiesa +3
The practical realization of quantum programs that require large-scale qubit systems is hindered by current technological limitations. Distributed Quantum Computing (DQC) presents…
Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks
Bing Luo, Wenli Xiao, Shiqiang Wang +2
Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial participation) when the number of participants is large and the server's communicatio…
Predictive Handover Strategy in 6G and Beyond: A Deep and Transfer Learning Approach
Ioannis Panitsas, Akrit Mudvari, Ali Maatouk +1
Next-generation cellular networks will evolve into more complex and virtualized systems, employing machine learning for enhanced optimization and leveraging higher frequency bands…
From Similarity to Superiority: Channel Clustering for Time Series Forecasting
Jialin Chen, Jan Eric Lenssen, Aosong Feng +5
Time series forecasting has attracted significant attention in recent decades. Previous studies have demonstrated that the Channel-Independent (CI) strategy improves forecasting pe…
Machine Learning on Blockchain Data: A Systematic Mapping Study
Georgios Palaiokrassas, Sarah Bouraga, Leandros Tassiulas
Context: Blockchain technology has drawn growing attention in the literature and in practice. Blockchain technology generates considerable amounts of data and has thus been a topic…
Efficient High-Resolution Time Series Classification via Attention Kronecker Decomposition
Aosong Feng, Jialin Chen, Juan Garza +5
The high-resolution time series classification problem is essential due to the increasing availability of detailed temporal data in various domains. To tackle this challenge effect…
Constrained Reinforcement Learning for Adaptive Controller Synchronization in Distributed SDN
Ioannis Panitsas, Akrit Mudvari, Leandros Tassiulas
In software-defined networking (SDN), the implementation of distributed SDN controllers, with each controller responsible for managing a specific sub-network or domain, plays a cri…
An Item is Worth a Prompt: Versatile Image Editing with Disentangled Control
Aosong Feng, Weikang Qiu, Jinbin Bai +5
Building on the success of text-to-image diffusion models (DPMs), image editing is an important application to enable human interaction with AI-generated content. Among various edi…
Cyber-Twin: Digital Twin-boosted Autonomous Attack Detection for Vehicular Ad-Hoc Networks
Yagmur Yigit, Ioannis Panitsas, Leandros Maglaras +2
The rapid evolution of Vehicular Ad-hoc NETworks (VANETs) has ushered in a transformative era for intelligent transportation systems (ITS), significantly enhancing road safety and…