scholarly journals Electromagnetic Environment Portrait Based on Big Data Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Lantu Guo ◽  
Meiyu Wang ◽  
Yun Lin
Keyword(s):  
Smart Cities ◽  
Spectrum Management ◽  
Electromagnetic Spectrum ◽  
Dimensional Model ◽  
Spectrum Utilization ◽  
Electromagnetic Environment ◽  
Channel Utilization ◽  
Storage And Retrieval ◽  
Big Data Mining ◽  
Wireless Service

With the development of IoT in smart cities, the electromagnetic environment (EME) in cities is becoming more and more complex. A full understanding of the characteristics of past spectrum resource utilization is the key to improving the efficiency of spectrum management. In order to explore the characteristics of spectrum utilization more comprehensively, this paper designs an EME portrait model. By checking the statistical information of the spectrum data, including changes in the noise floor and channel utilization in each individual wireless service, the correlation between the spectrum and time or space of different channels and the information is merged into a high-dimensional model through consistency transformation to form the EME portrait. The portrait model is not only convenient for storage and retrieval but also beneficial for transfer and expansion, which will become an important foundation for intelligent electromagnetic spectrum management.

scholarly journals A Novel Resource Allocation and Spectrum Defragmentation Mechanism for IoT-Based Big Data in Smart Cities

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3443 ◽  
Author(s):  
Yuhuai Peng ◽  
Jiaying Wang ◽  
Aiping Tan ◽  
Jingjing Wu
Keyword(s):  
Resource Allocation ◽  
Big Data ◽  
Smart Cities ◽  
Blocking Probability ◽  
Spectrum Utilization ◽  
High Definition ◽  
Protection Mechanism ◽  
Backbone Networks ◽  
Efficient Resource ◽  
Routing And Spectrum Allocation

People’s demand for high-traffic applications and the need for Internet of Things (IoT) are enormous in smart cities. The amount of data generated by virtual reality, high-definition video, and other IoT applications is growing at an exponential rate that far exceeds our expectations, and the types of data are becoming more diverse. It has become critical to reliably accommodate IoT-based big data with reasonable resource allocation in optical backbone networks for smart cities. For the problem of reliable transmission and efficient resource allocation in optical backbone networks, a novel resource allocation and spectrum defragmentation mechanism for massive IoT traffic is presented in this paper. Firstly, a routing and spectrum allocation algorithm based on the distance-adaptive sharing protection mechanism (DASP) is proposed, to obtain sufficient protection and reduce the spectrum consumption. The DASP algorithm advocates applying different strategies to the resource allocation of the working and protection paths. Then, the protection path spectrum defragmentation algorithm based on OpenFlow is designed to improve spectrum utilization while providing shared protection for traffic demands. The lowest starting slot-index first (LSSF) algorithm is employed to remove and reconstruct the optical paths. Numerical results indicate that the proposal can effectively alleviate spectrum fragmentation and reduce the bandwidth-blocking probability by 44.68% compared with the traditional scheme.

scholarly journals Intelligent Spectrum Management and Trajectory Design for UAV-Assisted Cognitive Ambient Backscatter Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jiazhou Liu ◽  
Sa Xiao ◽  
Huayan Guo ◽  
Xiangwei Zhou ◽  
Shixin He
Keyword(s):  
Data Center ◽  
Smart Cities ◽  
Spectrum Management ◽  
Trajectory Design ◽  
Management Problem ◽  
Step Method ◽  
Time Scheduling ◽  
Aerial Vehicle ◽  
Frequency Source ◽  
Ambient Backscatter

In this paper, we consider a novel Internet of Things (IoT) system in smart city called unmanned aerial vehicle- (UAV-) assisted cognitive backscatter network, where a UAV is employed as both a relay and a radio frequency source to help the data transmission between ground IoT backscatter devices (BDs) and a remote data center (DC). However, since the IoT applications are usually not assigned dedicated spectrum resource in smart cities, these data transmissions from BDs to the DC should share the licensed spectrum of cellular users (CUs). Therefore, we aim to maximize the minimum uplink throughput among all BDs while avoiding severe interference to CUs via joint spectrum management and UAV trajectory design. To solve the problem, we propose an iterative method utilizing block coordinated decent to partition the variables into two blocks. For the spectrum management problem, we first prove its convexity with the transmit power and time scheduling and then propose a two-step method to solve the two variables sequentially. For the UAV trajectory design problem, we resort to the fractional programming method to handle it. Simulation results demonstrate that the proposed algorithm can significantly increase the average max-min rate of the BDs while guaranteeing the acceptable interference to CUs with a fast convergence speed.

scholarly journals Multi-task Learning Approach for Automatic Modulation and Wireless Signal Classification

2021 ◽  
Author(s):  
Anu Jagannath ◽  
Jithin Jagannath
Keyword(s):  
State Of The Art ◽  
Spectrum Management ◽  
Signal Classification ◽  
Electromagnetic Spectrum ◽  
Signal Recognition ◽  
Single Task ◽  
Communication Signals ◽  
Spectrum Monitoring ◽  
Task Learning ◽  
Wireless Signal

Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum management, and secure communications. Consequently, it will become a key enabler with the emerging fifth-generation (5G) and beyond 5G communications, Internet of Things networks, among others. State-of-the-art studies in wireless signal recognition have only focused on a single task which in many cases is insufficient information for a system to act on. In this work, for the first time in the wireless communication domain, we exploit the potential of deep neural networks in conjunction with multi-task learning (MTL) framework to simultaneously learn modulation and signal classification tasks. The proposed MTL architecture benefits from the mutual relation between the two tasks in improving the classification accuracy as well as the learning efficiency with a lightweight neural network model. Additionally, we consider the problem of heterogeneous wireless signals such as radar and communication signals in the electromagnetic spectrum. Accordingly, we have shown how the proposed MTL model outperforms several state-of-the-art single-task learning classifiers while maintaining a lighter architecture and performing two signal characterization tasks simultaneously. Finally, we also release the only known open heterogeneous wireless signals dataset that comprises of radar and communication signals with multiple labels.

Active Vibration Control of Storage and Retrieval Machines

2008 ◽  
Author(s):  
Daniel Go¨rges ◽  
Jens Kroneis ◽  
Steven Liu
Keyword(s):  
Feedback Control ◽  
Vibration Control ◽  
Trajectory Planning ◽  
Active Vibration Control ◽  
Beam Theory ◽  
State Feedback Control ◽  
Dimensional Model ◽  
Euler Beam ◽  
Storage And Retrieval ◽  
Active Vibration

In this paper a novel concept for active vibration control of storage and retrieval machines is presented. The storage and retrieval machine is modeled based on the Bernoulli-Euler beam theory, yielding an infinite-dimensional model, and the assumed modes method in order to obtain a finite-dimensional model. The resulting model is of low order, a fourth-order model regarding the first and the second eigenfrequency describes the dynamics sufficiently. The model is verified on an experimental storage and retrieval machine. Several active vibration control strategies are studied, including trajectory planning approaches like higher-order trajectory planning, feedforward control approaches like trajectory filtering and input shaping, and feedback control approaches like state-feedback control. The strategies are evaluated by simulation and compared via performance measures.

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