A cloud-based approach to spectrum monitoring

Cooklev, Todor; Darabi, James; Mcintosh, Charles; Mosaheb, Mahdis

doi:10.1109/mim.2015.7066682

Cited by 21 publications

(10 citation statements)

References 12 publications

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Section: State Of the Art Of Radio Spectrum Monitoringmentioning

confidence: 99%

“…The national spectrum monitoring system, which is combined with fixed, remote, unmanned and mobile monitoring stations and transportable, portable radio monitoring equipments, is the strong technical support to fulfill spectrum management functions in China (J. J. Yang et al, ). Existing problems of current spectrum monitoring systems include (Chen et al, ; Cooklev et al, ; J. J. Yang et al, ) the following: (1) mainly consisting of large and expensive monitoring equipments, with which to build large‐scale spectrum monitoring systems would be high cost and inefficient; (2) not information systems but communication systems, and complying with RMTP just for data transmission, in which facilities transfer monitoring data originally without processing; and (3) little effort to dig large amounts of spectrum data acquired by monitoring equipments for all benefits, especially to utilize the time and location dimensions of the radio frequency.…”

Section: State Of the Art Of Radio Spectrum Monitoringmentioning

confidence: 99%

“…The main conclusion is that spectrum monitoring system must be integrated spectrum management and spectrum monitoring on a large scale, but such a system does not currently exist. They describe the interface to the cloud as an important enabler and propose a solution that allows ontology descriptions to be used for both spectrum management and monitoring (Cooklev et al, ).…”

Section: Challenges Of Radio Spectrum Monitoringmentioning

confidence: 99%

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State of the Art and Challenges of Radio Spectrum Monitoring in China

Yang

Jin

et al. 2017

Radio Science

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This paper provides an overview of radio spectrum monitoring in China. First, research background, the motivation is described and then train of thought, the prototype system, and the accomplishments are presented. Current radio spectrum monitoring systems are man‐machine communication systems, which are unable to detect and process the radio interference automatically. In order to realize intelligent radio monitoring and spectrum management, we proposed an Internet of Things‐based spectrum sensing approach using information system architecture and implemented a pilot program; then some very interesting results were obtained.

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Section: State Of the Art Of Radio Spectrum Monitoringmentioning

confidence: 99%

Section: State Of the Art Of Radio Spectrum Monitoringmentioning

confidence: 99%

Section: Challenges Of Radio Spectrum Monitoringmentioning

confidence: 99%

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State of the Art and Challenges of Radio Spectrum Monitoring in China

Yang

Jin

et al. 2017

Radio Science

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“…The creation of next generations of wireless networks that can support a variety of new applications and technologies will require transformation in a variety of ways. One such way is to use cloud technology based resource controllers that use dedicated radio frequency (RF) monitoring modules to collect/analyze data for better resource provisioning decisions [3], [4].…”

Section: Introductionmentioning

confidence: 99%

FPGA-Assisted Real-Time RF Wireless Data Analytics System: Design, Implementation, and Statistical Analyses

Khan

Lehtomäki

2020

IEEE Access

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A wide range of new ultra reliable low latency communication (URLLC) applications in next generation (NG) wireless systems demand real-time radio frequency (RF) data analytics of channel utilization (CU) that can help in making proactive resource allocation decisions. However, such real-time RF data analytics require processing of tens of millions of in-phase and quadrature (IQ) samples per second and sending huge quantities of samples to a resource allocating entity is not practical. We present design and implementation of an RF data analytics system which utilizes field-programmable gate arrays (FPGAs) at the network edge to process real-time streaming IQ samples from RF transceiver. FPGAs process millions of samples per second and output low-overhead descriptive statistics of wireless CU, such as mean CU values, maximum CU values, and entire histograms to obtain probability distribution of CU values, to a resource controller server where a quantile estimation based technique is used to detect congestion in CU in real-time. The FPGA-based modules are implemented on Xilinx's Zynq-7000 devices mounted with RF transceivers. We evaluate the performance of the implemented analytics system using extensive measurements, testing, and statistical analyses that are performed in both laboratory and over-the-air environments.

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“…To better manage the radio spectrum, many novel schemes have been proposed for the design of monitoring systems. Examples of state‐of‐the‐art schemes include the permanent cloud‐based system of systems for spectrum monitoring (Cooklev, ), the intelligent radio regulatory architecture based on edge computing (Huang et al, ), and “Electrosense” (Rajendran et al, ), which adopts a crowdsourcing paradigm to collect and analyze spectrum data using low‐cost sensors. In addition, artificial intelligence (AI) has experienced rapid development in the last decade, and performance gains from using deep models have fostered progress.…”

Section: Introductionmentioning

confidence: 99%

FM Broadcast Monitoring Using Artificial Intelligence

et al. 2020

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In recent decades, artificial intelligence has demonstrated successful applications in computer vision and natural language processing due to its breakthrough performance. In this paper, we propose a deep convolutional neural network (DCNN)-based method, characterized by acoustic features, for FM broadcast monitoring. The results show that the proposed DCNN model trained with the FBank feature extracted from FM audio files is capable of achieving nearly 100% accuracy in FM station identification tasks. Illegal broadcasting signals are then detected through a comparison with a radio frequency station database. Integrated with the trained DCNN model, an online FM broadcast monitoring system has been successfully implemented and deployed to help reduce human labor and automate the task of FM broadcast monitoring.

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A cloud-based approach to spectrum monitoring

Cited by 21 publications

References 12 publications

State of the Art and Challenges of Radio Spectrum Monitoring in China

State of the Art and Challenges of Radio Spectrum Monitoring in China

FPGA-Assisted Real-Time RF Wireless Data Analytics System: Design, Implementation, and Statistical Analyses

FM Broadcast Monitoring Using Artificial Intelligence

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