scholarly journals A deep learning‐based low complexity approach for joint transceiver beamforming

2021 ◽  
Author(s):  
Yibiao Wang ◽  
Junchao Shi ◽  
Wenjin Wang ◽  
Xiqi Gao ◽  
Ye Wu
Keyword(s):  
Deep Learning ◽  
Low Complexity

Low-Complexity Deep Learning-Based Beamforming in MISO Systems

2021 ◽  
Author(s):  
Nann Win Moe Thet ◽  
Khaled Walid Elgammal ◽  
Hasan Fehmi Ates ◽  
Mehmet Kemal Ozdemir
Keyword(s):  
Deep Learning ◽  
Low Complexity

scholarly journals Multi-Server Multi-User Multi-Task Computation Offloading for Mobile Edge Computing Networks

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1446 ◽  
Author(s):  
Liang Huang ◽  
Xu Feng ◽  
Luxin Zhang ◽  
Liping Qian ◽  
Yuan Wu
Keyword(s):  
Deep Learning ◽  
Low Complexity ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Wireless Devices ◽  
Cloud Server ◽  
Convergence Performance ◽  
Multi Server

This paper studies mobile edge computing (MEC) networks where multiple wireless devices (WDs) offload their computation tasks to multiple edge servers and one cloud server. Considering different real-time computation tasks at different WDs, every task is decided to be processed locally at its WD or to be offloaded to and processed at one of the edge servers or the cloud server. In this paper, we investigate low-complexity computation offloading policies to guarantee quality of service of the MEC network and to minimize WDs’ energy consumption. Specifically, both a linear programing relaxation-based (LR-based) algorithm and a distributed deep learning-based offloading (DDLO) algorithm are independently studied for MEC networks. We further propose a heterogeneous DDLO to achieve better convergence performance than DDLO. Extensive numerical results show that the DDLO algorithms guarantee better performance than the LR-based algorithm. Furthermore, the DDLO algorithm generates an offloading decision in less than 1 millisecond, which is several orders faster than the LR-based algorithm.

scholarly journals Improved D2D Millimeter Wave Communications for 5G Networks Using Deep Learning.pdf

2020 ◽  
Author(s):  
Ahmed abdelreheem ◽  
Ahmed S. A. Mubarak ◽  
Osama A. Omer ◽  
Hamada Esmaiel ◽  
Usama S. Mohamed
Keyword(s):  
Deep Learning ◽  
Millimeter Wave ◽  
Mode Selection ◽  
Low Complexity ◽  
5G Networks ◽  
Coverage Area ◽  
High Efficient ◽  
Optimal Mode ◽  
Low Coverage ◽  
Deep Learning Model

Mode selection is normally used in conjunction with Device-to-Device (D2D) millimeter wave (mmWave) communications in 5G networks to overcome the low coverage area, poor reliability and vulnerable to path blocking of mmWave transmissions. Thus, producing a high-efficient D2D mmWave using mode selection based on select the optimal mode with low complexity turns to be a big challenge towards ubiquitous D2D mmWave communications. In this paper, low complexity and high-efficient mode selection in D2D mmWave communications based on deep learning is introduced utilizing the artificial intelligence. In which, deep learning is used to estimate the optimal mode y in the case of blocking of mmWave transmission or low coverage area of mmWave communications. Then, the proposed deep learning model is based on training the model with almost use cases in offline phase to predict the optimal mode for data relaying high-reliability communication in online phase. In mode selection process, the potential D2D transmitter select the mode to transmit the data either based on dedicated D2D communication or through the cellular uplink using the base station (BS) as a relay based on several criteria. The proposed deep learning model is developed to overcome the challenges of selected the optimal mode with low complexity and high efficiency. The simulation analysis show that the proposed mode selection algorithms outperform the conventional techniques in D2D mmWave communication in the spectral efficiency, energy efficiency and coverage probability.

scholarly journals Deep learning based digital backpropagation demonstrating SNR gain at low complexity in a 1200 km transmission link

2020 ◽  
Vol 28 (20) ◽  
pp. 29318 ◽  
Author(s):  
Bertold Ian Bitachon ◽  
Amirhossein Ghazisaeidi ◽  
Marco Eppenberger ◽  
Benedikt Baeuerle ◽  
Masafumi Ayata ◽  
...  
Keyword(s):  
Deep Learning ◽  
Low Complexity ◽  
Transmission Link ◽  
Snr Gain

scholarly journals Best Paper Selection

2018 ◽  
Vol 27 (01) ◽  
pp. 113-113
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Deep Learning ◽  
Neural Network Model ◽  
Skeletal Maturity ◽  
Low Complexity ◽  
Noise Detection ◽  
Healthcare Monitoring ◽  
Deep Learning Neural Network ◽  
Hand Radiographs

Bote JM, Recas J, Rincon F, Atienza D, Hermida R. A modular low-complexity ECG delineation algorithm for real-time embedded systems. IEEE J Biomed Health Inform 2018;22(2):429-41 https://dx.doi.org/10.1109/JBHI.2017.2671443 Grossmann P, Stringfield O, El-Hachem N, Bui MM, Rios Velazquez E, Parmar C, Leijenaar RT, Haibe-Kains B, Lambin P, Gillies RJ, Aerts HJ. Defining the biological basis of radiomic phenotypes in lung cancer. ELife 2017;6:e23421 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/28731408/ Larson DB, Chen MC, Lungren MP, Halabi SS, Stence NV, Langlotz CP. Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology 2018; 287(1):313-22 http://pubs.rsna.org/doi/10.1148/radiol.2017170236?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed Satija U, Ramkumar B, Manikandan MS. Automated ECG noise detection and classification system for unsupervised healthcare monitoring. IEEE J Biomed Health Inform 2018;22(3):722-32 https://dx.doi.org/10.1109/JBHI.2017.2686436

Deep learning based digital backpropagation enabling SNR gain at low complexity

2021 ◽  
Author(s):  
Bertold Ian Bitachon ◽  
Amirhossein Ghazisaeidi ◽  
Marco Eppenberger ◽  
Benedikt Baeuerle ◽  
Masafumi Ayata ◽  
...  
Keyword(s):  
Deep Learning ◽  
Low Complexity ◽  
Snr Gain
Sign in / Sign up

Export Citation Format

Share Document