Zobrazeno 1 - 10
of 1 045
pro vyhledávání: '"Vucetic, Branka"'
Machine learning has been considered a promising approach for indoor localization. Nevertheless, the sample efficiency, scalability, and generalization ability remain open issues of implementing learning-based algorithms in practical systems. In this
Externí odkaz:
http://arxiv.org/abs/2405.13339
Inter-carrier interference (ICI) caused by mobile reflectors significantly degrades the conventional orthogonal frequency division multiplexing (OFDM) performance in high-mobility environments. The orthogonal time frequency space (OTFS) modulation sy
Externí odkaz:
http://arxiv.org/abs/2404.05191
This paper investigates guesswork over ordered statistics and formulates the complexity of ordered statistics decoding (OSD) in binary additive white Gaussian noise (AWGN) channels. It first develops a new upper bound of guesswork for independent seq
Externí odkaz:
http://arxiv.org/abs/2403.18488
In addressing wireless networked control systems (WNCS) subject to unexpected packet loss and uncertainties, this paper presents a practical Model Predictive Control (MPC) based control scheme with considerations of of packet dropouts, latency, proce
Externí odkaz:
http://arxiv.org/abs/2403.08398
Network slicing (NS) is a promising technology that supports diverse requirements for next-generation low-latency wireless communication networks. However, the tampering attack is a rising issue of jeopardizing NS service-provisioning. To resist tamp
Externí odkaz:
http://arxiv.org/abs/2403.09693
This paper considers opportunistic scheduler (OS) design using statistical channel state information~(CSI). We apply max-weight schedulers (MWSs) to maximize a utility function of users' average data rates. MWSs schedule the user with the highest wei
Externí odkaz:
http://arxiv.org/abs/2402.08238
Restricted access window (RAW) in Wi-Fi 802.11ah networks manages contention and interference by grouping users and allocating periodic time slots for each group's transmissions. We will find the optimal user grouping decisions in RAW to maximize the
Externí odkaz:
http://arxiv.org/abs/2402.00879
In this paper, we develop a deep learning-based bandwidth allocation policy that is: 1) scalable with the number of users and 2) transferable to different communication scenarios, such as non-stationary wireless channels, different quality-of-service
Externí odkaz:
http://arxiv.org/abs/2401.10253
This paper designs a graph neural network (GNN) to improve bandwidth allocations for multiple legitimate wireless users transmitting to a base station in the presence of an eavesdropper. To improve the privacy and prevent eavesdropping attacks, we pr
Externí odkaz:
http://arxiv.org/abs/2312.14958
This paper proposes a blockchain-secured deep reinforcement learning (BC-DRL) optimization framework for {data management and} resource allocation in decentralized {wireless mobile edge computing (MEC)} networks. In our framework, {we design a low-la
Externí odkaz:
http://arxiv.org/abs/2312.08016