Convolutional network codes for reliable point-to-point wireless communication

2012 ◽  
Author(s):  
Samantha R. Summerson ◽  
Anuj Batra
Keyword(s):  
Wireless Communication ◽  
Convolutional Network ◽  
Network Codes ◽  
Point To Point

scholarly journals Multicast Convolutional Network Codes via Local Encoding Kernels

IEEE Access ◽  
2017 ◽  
pp. 1-1 ◽  
Author(s):  
Morteza Rekab-Eslami ◽  
Morteza Esmaeili ◽  
T. Aaron Gulliver
Keyword(s):  
Convolutional Network ◽  
Network Codes

Time-Variant Decoding of Convolutional Network Codes

2012 ◽  
Vol 16 (10) ◽  
pp. 1656-1659 ◽  
Author(s):  
Wangmei Guo ◽  
Ning Cai ◽  
Qifu Tyler Sun
Keyword(s):  
Convolutional Network ◽  
Network Codes

scholarly journals A Deep Convolutional Network for Multitype Signal Detection and Classification in Spectrogram

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Weihao Li ◽  
Keren Wang ◽  
Ling You
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Wireless Communication ◽  
Signal Detection ◽  
Rapid Development ◽  
Point Estimation ◽  
Detection Methods ◽  
Convolutional Network ◽  
Signal Region ◽  
Real Time Applications

Wideband signal detection is an important problem in wireless communication. With the rapid development of deep learning (DL) technology, some DL-based methods are applied to wireless communication and have shown great potential. In this paper, we present a novel neural network for detecting signals and classifying signal types in wideband spectrograms. Our network utilizes the key point estimation to locate the rough centerline of the signal region and recognize its class. Then, several regressions are carried out to obtain properties, including the local offset and the border offsets of a bounding box, which are further synthesized for a more fine location. Experimental results demonstrate that our method performs more accurate than other DL-based object detection methods previously employed for the same task. In addition, our method runs obviously faster than existing methods, and it abandons the candidate anchors, which make it more favorable for real-time applications.

Parallel Computing on a Mobile Device

2010 ◽  
pp. 566-583
Author(s):  
Daniel C. Doolan ◽  
Sabin Tabirca ◽  
Laurence T. Yang
Keyword(s):  
Parallel Computing ◽  
Wireless Communication ◽  
Mobile Phones ◽  
Message Passing ◽  
High Speed ◽  
Message Passing Interface ◽  
Parallel Machines ◽  
Gigabit Ethernet ◽  
Point To Point ◽  
Global Communications

The Message Passing Interface (MPI) was published as a standard in 1992. Since then, many implementations have been developed. The MPICH library is one of the most well-known and freely available implementations. These libraries allow for the simplification of parallel computing on clusters and parallel machines. The system provides the developer with an easy-to-use set of functions for point-to-point and global communications. The details of how the actual communication takes place are hidden from the programmers, allowing them to focus on the domain-specific problem at hand. Communication between nodes on such systems is carried out via high-speed cabled interconnects (Gigabit Ethernet and upwards). The world of mobile computing, especially mobile phones, is now a ubiquitous technology. Mobile devices do not have any facility to allow for connections using traditional high-speed cabling; therefore, it is necessary to make use of wireless communication mechanisms to achieve interdevice communication. The majority of medium- to high-end phones are Bluetooth-enabled as standard, allowing for wireless communication to take place. The Mobile Message Passing Interface (MMPI) provides the developer with an intuitive set of functions to allow for communications between nodes (mobile phones) across a Bluetooth network. This chapter looks at the MMPI library and how it may be used for parallel computing on mobile phones (Smartphones).

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