DeepNOMA: A Unified Framework for NOMA Using Deep Multi-Task Learning

Ye, Neng; Li, Xiangming; Yu, Hanxiao; Zhao, Lian; Liu, Wenjia; Hou, Xiaolin

doi:10.1109/twc.2019.2963185

Cited by 135 publications

(56 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the complex channel environment, NOMA can be used to optimize access efficiency, reduce mutual interference, and improve transmission robustness. 112 In terms of channel modeling, with the introduction of von-mises-fisher (VMF) scattering distribution, a new 3D MIMO channel model was proposed by Bi et al 113 Simulation results have shown that the model can accurately evaluate A2G UAV channel. Ding and Xiu 114 proposed a scheme called block turbo-coded orthogonal frequency-division multiplexing (OFDM) for high-speed UAV data link, which combines block turbo codes (BTCs) with OFDM.…”

Section: Communication Technologies Of Uav Iotmentioning

confidence: 99%

A review on non-terrestrial wireless technologies for Smart City Internet of Things

Wang

Miao

et al. 2020

International Journal of Distributed Sensor Networks

Self Cite

View full text Add to dashboard Cite

Smart City Internet of Things will become a fundamental infrastructure to support massive machine-type communications between the widely deployed sensors serving big cities. Since there exists many location constraints for the existing terrestrial Internet of Things, the non-terrestrial Internet of Things sheds light on breaking these limits. Therefore, this article conducts a comprehensive survey on non-terrestrial Internet of Things technologies for Smart City, which is an important complement to terrestrial Internet of Things. We first present the application scenarios of Internet of Things and point out where the existing terrestrial Internet of Things cannot work perfectly. Two non-terrestrial Internet of Things technical proposals are then introduced, namely satellite Internet of Things and unmanned aerial vehicle Internet of Things. However, the focuses of these non-terrestrial Internet of Things are distinct, that is, the major problems of satellite and unmanned aerial vehicle Internet of Things are the high dynamic nature of channel and high maneuverability of unmanned aerial vehicles, respectively. The key technologies for satellite and unmanned aerial vehicle Internet of Things are then reviewed separately. Both physical and non-physical layer technologies are surveyed for satellite Internet of Things, and the route planning is mainly investigated for the unmanned aerial vehicle Internet of Things. Finally, we draw a conclusion and give some potential research directions of non-terrestrial Internet of Things.

show abstract

Section: Communication Technologies Of Uav Iotmentioning

confidence: 99%

A review on non-terrestrial wireless technologies for Smart City Internet of Things

Wang

Miao

et al. 2020

International Journal of Distributed Sensor Networks

Self Cite

View full text Add to dashboard Cite

show abstract

“…Results showed that it is possible to considerably reduce the complexity and the required time for the convergence with negligible sacrifice of the estimation accuracy. In [38], multi-task deep ANN framework for non-orthogonal multiple access (NOMA), namely DeepNOMA, has been proposed for treating non-orthogonal transmissions as multiple distinctive but correlated tasks.…”

Section: Introductionmentioning

confidence: 99%

Distributed and Multi-Task Learning at the Edge for Energy Efficient Radio Access Networks

et al. 2021

View full text Add to dashboard Cite

“…The growing popularity of the mobile Internet and the Internet of things [1][2][3] as well as the increasing perceptual services [4][5][6] has led to a continued high demand for network capacity, in which more spectrum is needed to support [7]. In order to relieve the pressure of spectrum resource supply, various schemes for efficiently exploiting spectrum have been proposed [8][9][10][11], but they still cannot satisfy people's thirst for high capacity and massive connectivity [12,13]. Since spectrum resources in the traditional cellular frequency bands have dried up, millimeter wave (mmWave) band has become a new source of spectrum supply [14], which has rich spectrum resources and thus can support the wireless transmissions with high data rate.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Radio Access Resource Management Scheme Based on Priority Strategy in Dense mmWave Cellular Networks

Gui¹,

Liu²

2020

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

In millimeter wave (mmWave) communication systems, beamforming-enabled directional transmission and network densification are usually used to overcome severe signal path loss problem and improve signal coverage quality. The combination of directional transmission and network densification poses a challenge to radio access resource management. The existing work presented an effective solution for dense mmWave wireless local area networks (WLANs). However, this scheme cannot adapt to network expansion when it is applied directly to dense mmWave cellular networks. In addition, there is still room for improvement in terms of energy efficiency and throughput. Therefore, we firstly propose an efficient hierarchical beamforming training (BFT) mechanism to establish directional links, which allows all the small cell base stations (SBSs) to participate in the merging of training frames to adapt to network expansion. Then, we design a BFT information-aided radio access resource allocation algorithm to improve the downlink energy efficiency of the entire mmWave cellular network by reasonably selecting beam directions and optimizing transmission powers and beam widths. Simulation results show that the proposed hierarchical BFT mechanism has the smaller overhead of BFT than the existing BFT mechanism, and the proposed BFT information-aided radio access resource allocation algorithm outperforms the existing corresponding algorithm in terms of average energy efficiency and throughput per link.

show abstract

DeepNOMA: A Unified Framework for NOMA Using Deep Multi-Task Learning

Cited by 135 publications

References 38 publications

A review on non-terrestrial wireless technologies for Smart City Internet of Things

A review on non-terrestrial wireless technologies for Smart City Internet of Things

Distributed and Multi-Task Learning at the Edge for Energy Efficient Radio Access Networks

An Efficient Radio Access Resource Management Scheme Based on Priority Strategy in Dense mmWave Cellular Networks

Contact Info

Product

Resources

About