Enabling LTE in TVWS with radio environment maps: From an architecture design towards a system level prototype

Gavrilovska, Liljana; Beek, Jaap van de; Xie, Yong; Lidstrom, Erik; Riihijärvi, Janne; Mähönen, Petri; Atanasovski, Vladimir; Denkovski, Daniel; Rakovic, Valentin

doi:10.1016/j.comcom.2014.07.008

Cited by 10 publications

(8 citation statements)

References 11 publications

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“…In reference to the regulator's framework, the TVWS system is fully dependent on the geolocation database to access the available spectrum chunks. However, the author in [16] proved that the proposed framework does not provide enough protection for the primary users, as the in-use database is being built using fixed propagation models. Consequently, the QoS of the secondary system will be greatly affected, as the communication link will be ceased immediately if any interference is caused to primary users due to inaccurately provided operational parameters (e.g.…”

Section: Figure 5: Tvws Availability Versus Allowed Transmissionmentioning

confidence: 99%

“…The unavailability of database interoperability also represents a challenge for trains travelling between different countries. However, there are ongoing trials to enhance the accuracy of the regulator's database framework by using Radio Environmental Maps (REM) that provide more accurate and local channel propagation information [16].…”

Section: Tvws Evaluation Summarymentioning

confidence: 99%

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Assessing the usage feasibility of TV White Spaces for railway communication applications

Samra

Chen

Roberts

et al. 2016

2016 IEEE International Conference on Intelligent Rail Transportation (ICIRT)

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Since telecommunications represent a strategic asset for the railway industry and a focal point for its evolution, it is important to consider emerging standards and technologies responding to the rapidly growing demand for mobile communication solutions in the railway domain. The prospect of opportunistic access to an inefficiently utilized frequency spectrum, known as TV whitespaces, was mainly proposed to solve the spectrum scarcity problem with desirable railway propagation characteristics. This research investigates the requirements of various railway communication applications against TV whitespaces characteristics, considering Rail Remote Condition Monitoring as an initial case study. The main objective is to specify and enhance the QoS parameters for the spectrum secondary users (rail network) in terms of throughput, availability, reliability and performance at speed while ensuring full protection for the spectrum primary users. A simulation scenario using BRaVE and OMNET++ was introduced for a train moving at maximum speed of 80 Km per hour between Selly-Oak and University Station, Birmingham, United Kingdom. Initial results indicate that TV White Spaces system can deliver average throughput of 1.47 Mbps per train. Coverage radius of 12 Km combined with perfectly aligned directional antenna in rural areas can ensure full protection for TV users while allowing higher transmission power for the rail TVWS network.

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Section: Figure 5: Tvws Availability Versus Allowed Transmissionmentioning

confidence: 99%

Section: Tvws Evaluation Summarymentioning

confidence: 99%

Assessing the usage feasibility of TV White Spaces for railway communication applications

Samra

Chen

Roberts

et al. 2016

2016 IEEE International Conference on Intelligent Rail Transportation (ICIRT)

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“…Spectrum monitoring combined with Radio Environmental Maps (REM) can also produce a better method of identifying unused Spectral bands [8]. Additionally, in [8] it is presented that the geo-location based database scheme typically results in an underutilized spectrum sharing scenario hence, better spectrum measurement scheme shall be incorporated that includes joint spectrum monitoring network and REM schemes [8] [9].…”

Section: Introductionmentioning

confidence: 99%

Opportunistic use of Spectral Holes in Karachi using Convolutional Neural Networks

Shaikh¹,

Naqvi²,

Ali³

et al. 2020

IJACSA

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Wireless services appearing in the next generation wireless standard i.e. 6G include Internet of Everything (IoE), Holographic communications, smart transportation and smart cities require exponential rise in the bandwidth in addition to other requirements. The current static spectrum allocation policy does not allow any new entrant to exploit already grid-locked Radio Frequency (RF) spectrum. Hence, quest for larger bandwidth can be fulfilled through other technologies. These include exploiting sub-Terahertz band, Visible Light Communication and Cognitive Radio scheme or exploiting of RF bands in opportunistic fashion. Cognitive Radio is one of those engines to exploit the RF spectrum in secondary style. Cognitive Radio can use artificial intelligence driven algorithms to complete the task. Several intelligent algorithms can be used for better forecasting of spectral holes. Convolutional Neural Network (CNN) is a Deep Learning algorithm that can be used to predict the presence of a spectral hole that can be opportunistically exploited for efficient utilization of RF spectrum in secondary fashion. This paper investigates the performance of CNN for metropolitan Karachi city of Pakistan so that the users can be provided with uninterrupted access to the network even under busy hours. Dataset for the proposed setup is collected for 1805 MHz frequency band through NI 2901 Universal Software Radio Peripheral (USRP) devices. The root mean square error (RMSE) for the predicted results using CNN appears to be 81.02 at epoch of 200 and mini-batch loss of 3281.8. Based on the predicted results, it was concluded that CNN can be useful for investigating the possible opportunistic usage of RF spectrum; however, further investigation is required with different datasets.

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“…Spectrum sharing frameworks reliant on Geo-location Databases (GLDBs) tend to leave spectrum underutilized. The reasons behind this are well understood and pointed out in the TV White Space (TVWS) and Citizens Broadband Radio Service (CBRS) literature [1], [2]. GLDBs, in their current form, cannot cope with the mobility of incumbents, and rely on theoretical propagation models that do not efficiently account for factors like terrain elevation and other obstructions [3].…”

Section: Introductionmentioning

confidence: 99%

“…Spectrum monitoring networks combined with Radio Environment Maps (REMs) may prove to be an important tool for addressing the aforementioned issues [1]. As REMs rely on real-time RF measurements, they can provide a more exact picture of the current spectrum occupancy across time, frequency and space.…”

Section: Introductionmentioning

confidence: 99%

Spectrum Monitoring for Radar Bands Using Deep Convolutional Neural Networks

Selim

Paisana

Arokkiam

et al. 2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

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In this paper, we present a spectrum monitoring framework for the detection of radar signals in spectrum sharing scenarios. The core of our framework is a deep convolutional neural network (CNN) model that enables Measurement Capable Devices (MCDs) to identify the presence of radar signals in the radio spectrum, even when these signals are overlapped with other sources of interference, such as commercial Long-Term Evolution (LTE) and Wireless Local Area Network (WLAN). We collected a large dataset of RF measurements, which include the transmissions of multiple radar pulse waveforms, downlink LTE, WLAN, and thermal noise. We propose a pre-processing data representation that leverages the amplitude and phase shifts of the collected samples. This representation allows our Convolutional Neural Network (CNN) model to achieve a classification accuracy of 99.6% on our testing dataset. The trained CNN model is then tested under various SNR values, outperforming other models, such as spectrogram-based CNN models.

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Enabling LTE in TVWS with radio environment maps: From an architecture design towards a system level prototype

Cited by 10 publications

References 11 publications

Assessing the usage feasibility of TV White Spaces for railway communication applications

Assessing the usage feasibility of TV White Spaces for railway communication applications

Opportunistic use of Spectral Holes in Karachi using Convolutional Neural Networks

Spectrum Monitoring for Radar Bands Using Deep Convolutional Neural Networks

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