scholarly journals Asynchronous Communication: Capacity Bounds and Suboptimality of Training

2013 ◽  
Vol 59 (3) ◽  
pp. 1227-1255 ◽  
Author(s):  
Aslan Tchamkerten ◽  
Venkat Chandar ◽  
Gregory W. Wornell
Keyword(s):  
Asynchronous Communication ◽  
Communication Capacity ◽  
Capacity Bounds

Study on asynchronous communication-oriented page classification

2008 ◽  
Vol 28 (2) ◽  
pp. 545-548
Author(s):  
Yu-lian FEI ◽  
Bo JIANG ◽  
Yuan LI
Keyword(s):  
Asynchronous Communication ◽  
Page Classification

scholarly journals Deep-learning-based high-resolution recognition of fractional-spatial-mode-encoded data for free-space optical communications

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Youngbin Na ◽  
Do-Kyeong Ko
Keyword(s):  
Deep Learning ◽  
Communication Systems ◽  
Degrees Of Freedom ◽  
Image Transmission ◽  
Data Traffic ◽  
Free Space Optical ◽  
Learning Classifier ◽  
Communication Capacity ◽  
Data Rates ◽  
Mode Space

AbstractStructured light with spatial degrees of freedom (DoF) is considered a potential solution to address the unprecedented demand for data traffic, but there is a limit to effectively improving the communication capacity by its integer quantization. We propose a data transmission system using fractional mode encoding and deep-learning decoding. Spatial modes of Bessel-Gaussian beams separated by fractional intervals are employed to represent 8-bit symbols. Data encoded by switching phase holograms is efficiently decoded by a deep-learning classifier that only requires the intensity profile of transmitted modes. Our results show that the trained model can simultaneously recognize two independent DoF without any mode sorter and precisely detect small differences between fractional modes. Moreover, the proposed scheme successfully achieves image transmission despite its densely packed mode space. This research will present a new approach to realizing higher data rates for advanced optical communication systems.

Robust Networking: Dynamic Topology Evolution Learning for Internet of Things

2021 ◽  
Vol 17 (3) ◽  
pp. 1-23
Author(s):  
Ning Chen ◽  
Tie Qiu ◽  
Mahmoud Daneshmand ◽  
Dapeng Oliver Wu
Keyword(s):  
Internet Of Things ◽  
Smart Cities ◽  
Evolutionary Process ◽  
Small Error ◽  
Cyber Attacks ◽  
Communication Capacity ◽  
Error Ratio ◽  
The Internet Of Things ◽  
Evolution Learning

The Internet of Things (IoT) has been extensively deployed in smart cities. However, with the expanding scale of networking, the failure of some nodes in the network severely affects the communication capacity of IoT applications. Therefore, researchers pay attention to improving communication capacity caused by network failures for applications that require high quality of services (QoS). Furthermore, the robustness of network topology is an important metric to measure the network communication capacity and the ability to resist the cyber-attacks induced by some failed nodes. While some algorithms have been proposed to enhance the robustness of IoT topologies, they are characterized by large computation overhead, and lacking a lightweight topology optimization model. To address this problem, we first propose a novel robustness optimization using evolution learning (ROEL) with a neural network. ROEL dynamically optimizes the IoT topology and intelligently prospects the robust degree in the process of evolutionary optimization. The experimental results demonstrate that ROEL can represent the evolutionary process of IoT topologies, and the prediction accuracy of network robustness is satisfactory with a small error ratio. Our algorithm has a better tolerance capacity in terms of resistance to random attacks and malicious attacks compared with other algorithms.

scholarly journals Capacity Bounds for AF Dual-hop Relaying in ${\cal G}$ Fading Channels

2012 ◽  
Vol 61 (4) ◽  
pp. 1730-1740 ◽  
Author(s):  
Caijun Zhong ◽  
Michail Matthaiou ◽  
George K. Karagiannidis ◽  
Aiping Huang ◽  
Zhaoyang Zhang
Keyword(s):  
Fading Channels ◽  
Capacity Bounds
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