Zobrazeno 1 - 10
of 124
pro vyhledávání: '"Mao, Yuyi"'
Passenger counting is crucial for public transport vehicle scheduling and traffic capacity evaluation. However, most existing methods are either costly or with low counting accuracy, leading to the recent use of Wi-Fi signals for this purpose. In thi
Externí odkaz:
http://arxiv.org/abs/2410.11400
Future wireless networks are envisioned to support both sensing and artificial intelligence (AI) services. However, conventional integrated sensing and communication (ISAC) networks may not be suitable due to the ignorance of diverse task-specific da
Externí odkaz:
http://arxiv.org/abs/2409.00405
One of the most critical challenges for deploying distributed learning solutions, such as federated learning (FL), in wireless networks is the limited battery capacity of mobile clients. While it is a common belief that the major energy consumption o
Externí odkaz:
http://arxiv.org/abs/2407.13703
Inter-cell interference (ICI) suppression is critical for multi-cell multi-user networks. In this paper, we investigate advanced precoding techniques for coordinated multi-point (CoMP) with downlink coherent joint transmission, an effective approach
Externí odkaz:
http://arxiv.org/abs/2403.19127
In order to control the inter-cell interference for a multi-cell multi-user multiple-input multiple-output network, we consider the precoder design for coordinated multi-point with downlink coherent joint transmission. To avoid costly information exc
Externí odkaz:
http://arxiv.org/abs/2403.09958
Grant-free random access (RA) has been recognized as a promising solution to support massive connectivity due to the removal of the uplink grant request procedures. While most endeavours assume perfect synchronization among users and the base station
Externí odkaz:
http://arxiv.org/abs/2402.17996
Orthogonal time frequency space (OTFS) modulation has emerged as a promising solution to support high-mobility wireless communications, for which, cost-effective data detectors are critical. Although graph neural network (GNN)-based data detectors ca
Externí odkaz:
http://arxiv.org/abs/2402.10071
Artificial intelligence (AI) technologies have emerged as pivotal enablers across a multitude of industries largely due to their significant resurgence over the past decade. The transformative power of AI is primarily derived from the utilization of
Externí odkaz:
http://arxiv.org/abs/2312.00333
Because of its privacy-preserving capability, federated learning (FL) has attracted significant attention from both academia and industry. However, when being implemented over wireless networks, it is not clear how much communication error can be tol
Externí odkaz:
http://arxiv.org/abs/2310.16652
Federated learning (FL) is a distributed learning paradigm that maximizes the potential of data-driven models for edge devices without sharing their raw data. However, devices often have non-independent and identically distributed (non-IID) data, mea
Externí odkaz:
http://arxiv.org/abs/2308.15786