Filters








58 Hits in 4.8 sec

A Review of Deep Learning in 5G Research: Channel Coding, Massive MIMO, Multiple Access, Resource Allocation, and Network Security

Amanda Ly, Yu-Dong Yao
2021 IEEE Open Journal of the Communications Society  
Some more specific types of DNNs are convolutional neural network and recurrent neural network (RNN). 1) CONVOLUTIONAL NEURAL NETWORK -CNN CNNs use images and videos since CNNs are applied in image classification  ...  An important aspect of neural networks is the activation function, which allows the network to learn complex patterns in the data.  ... 
doi:10.1109/ojcoms.2021.3058353 fatcat:vqyfhhm4gnb4po4nhtjch7dlpe

Deep Learning for Wireless Physical Layer: Opportunities and Challenges [article]

Tianqi Wang, Chao-Kai Wen, Hanqing Wang, Feifei Gao, Tao Jiang, Shi Jin
2017 arXiv   pre-print
recognition, channel decoding, and detection) and replace the communication system with a radically new architecture based on an autoencoder.  ...  Machine learning (ML) has been widely applied to the upper layers of wireless communication systems for various purposes, such as deployment of cognitive radio and communication network.  ...  CNN convolutional neural network RNN recurrent neural network LSTM long short-term memory SNR signal-to-noise ratio LLR log-likelihood ratio BP belief propagation HDPC high-density parity  ... 
arXiv:1710.05312v2 fatcat:veeuomcadvcedghvy5om4rzgpm

Wireless AI: Enabling an AI-Governed Data Life Cycle [article]

Dinh C. Nguyen, Peng Cheng, Ming Ding, David Lopez-Perez, Pubudu N. Pathirana, Jun Li, Aruna Seneviratne, Yonghui Li, H. Vincent Poor
2020 arXiv   pre-print
network evolution.  ...  Specifically, we first propose a novel Wireless AI architecture that covers five key data-driven AI themes in wireless networks, including Sensing AI, Network Device AI, Access AI, User Device AI and Data-provenance  ...  (LSTM) Networks. 1) Convolutional Neural Network (CNN): CNN is a type of neural network used mainly for image processing and recognition with large pixel datasets [45] .  ... 
arXiv:2003.00866v1 fatcat:dofctwtag5ewhhzaseeukhtxbe

Deep Learning in Mobile and Wireless Networking: A Survey

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 IEEE Communications Surveys and Tutorials  
Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains.  ...  We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking.  ...  recognition Edge-based Binarized-LSTM Okita and Inoue [269] Multiple overlapping activities recognition Cloud-based CNN+LSTM Alsheikh et al  ... 
doi:10.1109/comst.2019.2904897 fatcat:xmmrndjbsfdetpa5ef5e3v4xda

Human Silhouette and Skeleton Video Synthesis Through Wi-Fi signals

Danilo Avola, Marco Cascio, Luigi Cinque, Alessio Fagioli, Gian Luca Foresti
2022 International Journal of Neural Systems  
On this account, this paper presents a novel two-branch generative neural network that effectively maps radio data into visual features, following a teacher–student design that exploits a cross-modality  ...  The increasing availability of wireless access points (APs) is leading toward human sensing applications based on Wi-Fi signals as support or alternative tools to the widespread visual sensors, where the  ...  Similarly, only for the latter, Kefayati et al. 33 introduce a network ar-chitecture comprising three CNN-based sub-models: CSI encoder, domain translator, and frame decoder.  ... 
doi:10.1142/s0129065722500150 pmid:35209810 fatcat:csabic3ejjcqlec6zzrw4qkzai

Deep Learning in Mobile and Wireless Networking: A Survey [article]

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 arXiv   pre-print
Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains.  ...  We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking.  ...  recognition Edge-based Binarized-LSTM Okita and Inoue [266] Multiple overlapping activities recognition Cloud-based CNN+LSTM Alsheikh et al  ... 
arXiv:1803.04311v3 fatcat:awuvyviarvbr5kd5ilqndpfsde

Human Action Recognition from Various Data Modalities: A Review [article]

Zehua Sun, Qiuhong Ke, Hossein Rahmani, Mohammed Bennamoun, Gang Wang, Jun Liu
2021 arXiv   pre-print
Human Action Recognition (HAR) aims to understand human behavior and assign a label to each action.  ...  Specifically, we review the current mainstream deep learning methods for single data modalities and multiple data modalities, including the fusion-based and the co-learning-based frameworks.  ...  ), Recurrent Neural Network (RNN) and 3D CNN-based methods, as shown in Table 2 .  ... 
arXiv:2012.11866v4 fatcat:twjnaur2jzahznci6clkadylay

A Survey of Deep Learning for Data Caching in Edge Network

Yantong Wang, Vasilis Friderikos
2020 Informatics  
We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure.  ...  In addition to model-based caching schemes, learning-based edge caching optimizations have recently attracted significant attention, and the aim hereafter is to capture these recent advances for both model-based  ...  Auto Encoder Auto Encoder is a stack of two neural networks (NNs), named encoder and decoder, respectively, where the former tries to learn the representative characteristics of input and generate a related  ... 
doi:10.3390/informatics7040043 fatcat:dx7xbqf32rganf6u5gpe3eorhq

When 5G Meets Deep Learning: A Systematic Review

Guto Leoni Santos, Patricia Takako Endo, Djamel Sadok, Judith Kelner
2020 Algorithms  
Differently from the current literature, we examine data from the last decade and the works that address diverse 5G specific problems, such as physical medium state estimation, network traffic prediction  ...  , user device location prediction, self network management, among others.  ...  ), and convolutional neural network (CNN) (adopted by only 9 articles).  ... 
doi:10.3390/a13090208 fatcat:bw3evog5xbc5jjf3bbdorda7zq

A Deep Learning Approach for MIMO-NOMA Downlink Signal Detection

Chuan Lin, Qing Chang, Xianxu Li
2019 Sensors  
In contrast to implementing well-designed detection algorithms, MIMO-NOMA-DL searches for the optimal solution via a neural network (NN).  ...  Toward this end, we explore a high-performance, high-efficiency tool—deep learning (DL).  ...  The major evolution of deep learning mainly involves deep neural networks (DNNs) in pattern classification and recognition, conventional neutral networks (CNNs) in image processing, and recurrent neural  ... 
doi:10.3390/s19112526 fatcat:fx7haddb4zcw7lxzpuspo2cegu

Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation

Hojjat Navidan, Parisa Fard Moshiri, Mohammad Nabati, Reza Shahbazian, Seyed Ali Ghorashi, Vahid Shah-Mansouri, David Windridge
2021 Computer Networks  
The need for a comprehensive survey of such activity is therefore urgent.  ...  Given their relative ease of use, it is therefore natural that researchers in the field of networking (which has seen extensive application of deep learning methods) should take an interest in GAN-based  ...  [92] applied leave-one-subject-out validation to CSI-based activity recognition to address the performance degradation problem.  ... 
doi:10.1016/j.comnet.2021.108149 fatcat:4ekgil24ijha3evmzruez63tdq

What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence [article]

Qiao Lan, Dingzhu Wen, Zezhong Zhang, Qunsong Zeng, Xu Chen, Petar Popovski, Kaibin Huang
2021 arXiv   pre-print
The first part of the article introduces SemCom principles including encoding, system architecture, and layer-coupling and end-to-end design approaches.  ...  M2M SemCom refers to effectiveness techniques for efficiently connecting machines such that they can effectively execute a specific computation task in a wireless network.  ...  Coupling advanced SemCom and 6G technologies paves the way towards the disappearance of the boundary between the physical and virtual worlds.  ... 
arXiv:2110.00196v1 fatcat:33rbcxbpovfqtgt7gxjxdfzivy

Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A Review

Nasser Kimbugwe, Tingrui Pei, Moses Ntanda Kyebambe
2021 Energies  
The role of the Internet of Things (IoT) networks and systems in our daily life cannot be underestimated.  ...  We provide a detailed explanation of QoS in IoT and an overview of commonly used DL-based algorithms in enhancing QoS.  ...  Through encoding and decoding techniques, AE can regenerate the original data input.  ... 
doi:10.3390/en14196384 fatcat:y6i4vh7fzzahbpa2avfi7em5rm

A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies [article]

Anish Shastri, Neharika Valecha, Enver Bashirov, Harsh Tataria, Michael Lentmaier, Fredrik Tufvesson, Michele Rossi, Paolo Casari
2021 arXiv   pre-print
This paper surveys the state of the art in device-based localization and device-free sensing using mmWave communication and radar devices, with a focus on indoor deployments.  ...  and radar devices is starting to improve the penetration of such technologies in consumer markets, paving the way for large-scale and dense deployments in fifth-generation (5G)-and-beyond as well as 6G networks  ...  After localizing the throat and receiving the data, voice reconstruction is achieved by a neural network based on an encoder-decoder (autoencoder) architecture.  ... 
arXiv:2112.05593v1 fatcat:tbsdro6ps5espeny5p2o32tqqa

RF-net

Shuya Ding, Zhe Chen, Tianyue Zheng, Jun Luo
2020 Proceedings of the 18th Conference on Embedded Networked Sensor Systems  
Radio-Frequency (RF) based device-free Human Activity Recognition (HAR) rises as a promising solution for many applications.  ...  The results motivate us to innovate in two designs: i) a dual-path base HAR network, where both time and frequency domains are dedicated to learning powerful RF features including spatial and attention-based  ...  Essentially, we employ the base network to perform both activity recognition and feature extraction, and exploit them to (meta)-train the distance metric via a residual classification module.  ... 
doi:10.1145/3384419.3430735 dblp:conf/sensys/DingCZL20 fatcat:ias7sh2lfnhnriyf7wnqkzoz74
« Previous Showing results 1 — 15 out of 58 results