Zobrazeno 1 - 10
of 953
pro vyhledávání: '"Moonen Marc"'
Publikováno v:
Proceedings of the 2024 18th International Workshop on Acoustic Signal Enhancement (IWAENC)
A one-shot algorithm called iterationless DANSE (iDANSE) is introduced to perform distributed adaptive node-specific signal estimation (DANSE) in a fully connected wireless acoustic sensor network (WASN) deployed in an environment with non-overlappin
Externí odkaz:
http://arxiv.org/abs/2408.03752
Cell-free massive multiple-input multiple-output (CFmMIMO) is a paradigm that can improve users' spectral efficiency (SE) far beyond traditional cellular networks. Increased spatial diversity in CFmMIMO is achieved by spreading the antennas into smal
Externí odkaz:
http://arxiv.org/abs/2407.21754
A low-rank approximation-based version of the topology-independent distributed adaptive node-specific signal estimation (TI-DANSE) algorithm is introduced, using a generalized eigenvalue decomposition (GEVD) for application in ad-hoc wireless acousti
Externí odkaz:
http://arxiv.org/abs/2407.14172
In many speech recording applications, the recorded desired speech is corrupted by both noise and acoustic echo, such that combined noise reduction (NR) and acoustic echo cancellation (AEC) is called for. A common cascaded design corresponds to NR fi
Externí odkaz:
http://arxiv.org/abs/2406.08974
Fronthaul quantization causes a significant distortion in cell-free massive MIMO networks. Due to the limited capacity of fronthaul links, information exchange among access points (APs) must be quantized significantly. Furthermore, the complexity of
Externí odkaz:
http://arxiv.org/abs/2405.01303
Cell-free massive multiple-input multiple-output (MIMO) is an emerging technology that will reshape the architecture of next-generation networks. This paper considers the sequential fronthaul, whereby the access points (APs) are connected in a daisy
Externí odkaz:
http://arxiv.org/abs/2312.05622
Distributed learning and adaptation have received significant interest and found wide-ranging applications in machine learning and signal processing. While various approaches, such as shared-memory optimization, multi-task learning, and consensus-bas
Externí odkaz:
http://arxiv.org/abs/2311.04604
A Deep Learning Based Resource Allocator for Communication Systems with Dynamic User Utility Demands
Deep learning (DL) based resource allocation (RA) has recently gained significant attention due to its performance efficiency. However, most related studies assume an ideal case where the number of users and their utility demands, e.g., data rate con
Externí odkaz:
http://arxiv.org/abs/2311.04600
Autor:
Blochberger, Matthias, Elvander, Filip, Ali, Randall, Østergaard, Jan, Jensen, Jesper, Moonen, Marc, van Waterschoot, Toon
Distributed signal-processing algorithms in (wireless) sensor networks often aim to decentralize processing tasks to reduce communication cost and computational complexity or avoid reliance on a single device (i.e., fusion center) for processing. In
Externí odkaz:
http://arxiv.org/abs/2303.00832
Autor:
Ranjbar, Vida, Girycki, Adam, Rahman, Md Arifur, Pollin, Sofie, Moonen, Marc, Vinogradov, Evgenii
Publikováno v:
IEEE Communications Standards Magazine (Volume: 6, Issue: 1, March 2022)
To keep supporting next-generation requirements, the radio access infrastructure will increasingly densify. Cell-free (CF) network architectures are emerging, combining dense deployments with extreme flexibility in allocating resources to users. In p
Externí odkaz:
http://arxiv.org/abs/2301.10429