Deep learning for beam hopping in multibeam satellite systems
Data-driven approaches, eg, deep learning (DL), have been widely studied in terrestrial
wireless communications fields, proving the benefits and potentials of such techniques. In …
wireless communications fields, proving the benefits and potentials of such techniques. In …
Deep convolutional self-attention network for energy-efficient power control in NOMA networks
In this letter, we propose an end-to-end multi-modal based convolutional self-attention
network to perform power control in non-orthogonal multiple access (NOMA) networks. We …
network to perform power control in non-orthogonal multiple access (NOMA) networks. We …
A Q-learning-based resource allocation for downlink non-orthogonal multiple access systems considering QoS
Q Zhai, M Bolić, Y Li, W Cheng, C Liu - IEEE Access, 2021 - ieeexplore.ieee.org
As a technology that can accommodate more users and significantly improve spectral
efficiency, non-orthogonal multiple access (NOMA) has attracted the attention of many …
efficiency, non-orthogonal multiple access (NOMA) has attracted the attention of many …
Learning-based resource allocation: Efficient content delivery enabled by convolutional neural network
In practical content delivery, when the time-frequency resources are limited, it is a
challenging task to satisfy terminals' data demand in a heavy-traffic and mutual-interfered …
challenging task to satisfy terminals' data demand in a heavy-traffic and mutual-interfered …
Joint user clustering and multi-dimensional resource allocation in downlink MIMO–NOMA networks
X Zhang, X Zhu, H Zhu - IEEE Access, 2019 - ieeexplore.ieee.org
In this paper, a novel joint design of user clustering and multi-dimensional resource
allocation based on effective capacity (EC) is proposed for the multi-cell downlink multiple …
allocation based on effective capacity (EC) is proposed for the multi-cell downlink multiple …
TDMA-NOMA based computation offloading for cognitive capacity harvesting networks with transmission order optimization
B Lu, S Lin, J Shi - IEEE Transactions on Communications, 2022 - ieeexplore.ieee.org
In this paper, we investigate the resource allocation of mobile edge computing (MEC) in
cognitive capacity harvesting networks (CCHNs) when non-orthogonal multiple-access …
cognitive capacity harvesting networks (CCHNs) when non-orthogonal multiple-access …
Mixed-criticality industrial data scheduling on 5G NR
X Jin, Y Tian, C Xu, C Xia, D Li… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
Compared to industrial wired networks, 5G can improve device mobility and reduce the cost
of networking. However, the real-time performance and reliability of 5G new radio (NR) still …
of networking. However, the real-time performance and reliability of 5G new radio (NR) still …
On fairness optimization for NOMA-enabled multi-beam satellite systems
In a multi-beam satellite communication system, traffic requests are typically asymmetric
across beams and highly heterogeneous among terminals. In practical operations, it is …
across beams and highly heterogeneous among terminals. In practical operations, it is …
Satisfying strict deadlines for cellular Internet of Things through hybrid multiple access
OB Gamgam, E Karasan - Transactions on Emerging …, 2023 - Wiley Online Library
Latency‐constrained aspects of cellular Internet of Things (IoT) applications rely on Ultra‐
Reliable and Low Latency Communications (URLLC), which highlight research on satisfying …
Reliable and Low Latency Communications (URLLC), which highlight research on satisfying …
A deep learning based acceleration of complex satellite resource management problem
Demand-based algorithms have been widely studied in the satellite community, where the
satellite's radio resources are allocated according to the on-ground users' demands. Hence …
satellite's radio resources are allocated according to the on-ground users' demands. Hence …