Zobrazeno 1 - 10
of 250
pro vyhledávání: '"Piechocki, Robert"'
The radio access network (RAN) is a critical component of modern telecom infrastructure, currently undergoing significant transformation towards disaggregated and open architectures. These advancements are pivotal for integrating intelligent, data-dr
Externí odkaz:
http://arxiv.org/abs/2409.02849
The advancements of machine learning-based (ML) decision-making algorithms created various research and industrial opportunities. One of these areas is ML-based near-real-time network management applications (xApps) in Open-Radio Access Network (O-RA
Externí odkaz:
http://arxiv.org/abs/2407.03068
Feedback holds a pivotal role in practical communication schemes, even though it does not enhance channel capacity. Its main attribute includes adaptability in transmission that allows for a higher rate of convergence of the error probability to zero
Externí odkaz:
http://arxiv.org/abs/2403.14360
Autor:
Mavromatis, Ioannis, Spyridopoulos, Theodoros, Carnelli, Pietro, Chin, Woon Hau, Khalil, Ahmed, Chakravarty, Jennifer, Kun, Lucia Cipolina, Piechocki, Robert J., Robbins, Colin, Cunnington, Daniel, Chase, Leigh, Chiazor, Lamogha, Preston, Chris, Rahul, Khan, Aftab
The way we travel is changing rapidly, and Cooperative Intelligent Transportation Systems (C-ITSs) are at the forefront of this evolution. However, the adoption of C-ITSs introduces new risks and challenges, making cybersecurity a top priority for en
Externí odkaz:
http://arxiv.org/abs/2312.14687
The Open Radio Access Network (O-RAN) is a burgeoning market with projected growth in the upcoming years. RAN has the highest CAPEX impact on the network and, most importantly, consumes 73% of its total energy. That makes it an ideal target for optim
Externí odkaz:
http://arxiv.org/abs/2310.11131
In this paper we introduce learnable lattice vector quantization and demonstrate its effectiveness for learning discrete representations. Our method, termed LL-VQ-VAE, replaces the vector quantization layer in VQ-VAE with lattice-based discretization
Externí odkaz:
http://arxiv.org/abs/2310.09382
Autor:
Iacob, Alex, Gusmão, Pedro P. B., Lane, Nicholas D., Koupai, Armand K., Bocus, Mohammud J., Santos-Rodríguez, Raúl, Piechocki, Robert J., McConville, Ryan
Human Activity Recognition (HAR) training data is often privacy-sensitive or held by non-cooperative entities. Federated Learning (FL) addresses such concerns by training ML models on edge clients. This work studies the impact of privacy in federated
Externí odkaz:
http://arxiv.org/abs/2305.12134
This paper presents an achievability bound that evaluates the exact probability of error of an ensemble of random codes that are decoded by a minimum distance decoder. Compared to the state-of-the-art which demands exponential computation time, this
Externí odkaz:
http://arxiv.org/abs/2305.09450
This paper presents a novel approach for multimodal data fusion based on the Vector-Quantized Variational Autoencoder (VQVAE) architecture. The proposed method is simple yet effective in achieving excellent reconstruction performance on paired MNIST-
Externí odkaz:
http://arxiv.org/abs/2302.12636
One key metric for physical layer security is the secrecy capacity. This is the maximum rate that a system can transmit with perfect secrecy. For a Multiple Input Multiple Output (MIMO) system (a newer technology for 5G, 6G and beyond) the secrecy ca
Externí odkaz:
http://arxiv.org/abs/2209.15318