Efficient Spectrum Occupancy Prediction Exploiting Multidimensional Correlations through Composite 2D-LSTM Models

Aygül, Mehmet Ali; Nazzal, Mahmoud; Saglam, Mehmet Izzet; Costa, Daniel Benevides da; Ates, Hasan F.; Arslan, Hüseyin

doi:10.3390/s21010135

Cited by 14 publications

(3 citation statements)

References 38 publications

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“…Time, frequency, and space/location information can be used to predict the occupancy of the future spectrum [33] based on their correlation. The validity of this can be intuitively deduced.…”

Section: Spectrum Occupancy Predictionmentioning

confidence: 99%

Wireless sensing – enabler of future wireless technologies

Türkmen¹,

Solaija²,

Tusha³

et al. 2021

Turk J Elec Eng & Comp Sci

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With the completion of the 5G standardization efforts, the wireless communication world has now turned to the road ahead, the future wireless communication visions. One common vision is that future networks will be flexible, or able to accommodate an even richer variety of services with stringent, often conflicting requirements. This ambitious feat can only be accomplished with a ubiquitous awareness of the radio and physical environment. To this end, this paper highlights the importance of wireless sensing as a means for radio environment awareness and surveys wireless sensing methods under different domains. Then, a review of wireless sensing from a standardization perspective is given.These standardization efforts will provide the initial landscape upon which research into future wireless sensing methods will be built upon. Therefore, the paper is concluded by outlining imperative standardization requirements and future directions in wireless sensing.

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Section: Spectrum Occupancy Predictionmentioning

confidence: 99%

Wireless sensing – enabler of future wireless technologies

Türkmen¹,

Solaija²,

Tusha³

et al. 2021

Turk J Elec Eng & Comp Sci

Self Cite

View full text Add to dashboard Cite

show abstract

“…Nevertheless, the spectrum states are interrelated in the time and frequency domains. MA Aygül et al [ 21 , 22 ] exploited correlation over time and frequency of spectrum data through a 2D-LSTM with better performance than 1D-LSTM, but this method had a weak ability to extract frequency relationships.…”

Section: Introductionmentioning

confidence: 99%

A Deep Long-Term Joint Temporal–Spectral Network for Spectrum Prediction

Wang,

Hu,

Jiang

et al. 2024

Sensors

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Spectrum prediction is a promising technique to release spectrum resources and plays an essential role in cognitive radio networks and spectrum situation generating. Traditional algorithms normally focus on one-dimensional or predict spectrum values in a slot-by-slot manner and thus cannot fully perceive the spectrum states in complex environments and lack timeliness. In this paper, a deep learning-based prediction method with a simple structure is developed for temporal–spectral and multi-slot spectrum prediction simultaneously. Specifically, we first analyze and construct spectrum data suitable for the model to simultaneously achieve long-term and multi-dimensional spectrum prediction. Then, a hierarchical spectrum prediction system is developed that takes advantage of the advanced Bi-ConvLSTM and the seq2seq framework. The Bi-ConvLSTM captures time–frequency characteristics of spectrum data, and the seq2seq framework is used for long-term spectrum prediction. Furthermore, the attention mechanism is used to address the limitations of the seq2seq framework that compresses all inputs into fixed-length vectors, resulting in information loss. Finally, the experimental results have shown that the proposed model has a significant advantage over the benchmark schemes. Especially, the proposed spectrum prediction model achieves 6.15%, 0.7749, 1.0978, and 0.9628 in MAPE, MAE, RMSE, and R2, respectively, which is better than all the baseline deep learning models.

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“…In other approaches, the frequency range used or intended to be used in the selected application is subjected to measurements and analysis [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. In this case, researchers give particular attention to 2.4 GHz and 5 GHz ISM bands, widely used by WLAN devices [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Channel Occupancy Measurements in 868 MHz ISM Band in Residential Areas

Kozlowski

Kurek

2021

Sensors

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The rapid development of Internet of Things (IoT) has led to more and more devices using ISM frequency bands. Because they are not time synchronized, medium access collisions are unavoidable. The probability of such a collision is usually reasonably low; however, it increases with the number of transmitters competing for the same frequency channel. For this reason, ISM bands’ occupancy is regularly monitored by researchers. This paper presents the results of the measurement campaign during which a selected part of the 868 MHz ISM frequency band was monitored for the presence of transmissions in six locations in various residential areas in Warsaw, Poland. For the purpose of the campaign, a dedicated measurement set-up comprising a software-defined radio (SDR) module was assembled. The measurements results showed that the channel occupancy is in most cases lower than 1% with a maximum observed value of 2%. The paper presents selected characteristics of the detected signals. Additionally, distribution over time of the detected signals was used together with the Monte Carlo simulations to analyze how long idle time blocks are available for new transmitters that could be deployed in the band under testing.

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Efficient Spectrum Occupancy Prediction Exploiting Multidimensional Correlations through Composite 2D-LSTM Models

Cited by 14 publications

References 38 publications

Wireless sensing – enabler of future wireless technologies

Wireless sensing – enabler of future wireless technologies

A Deep Long-Term Joint Temporal–Spectral Network for Spectrum Prediction

Channel Occupancy Measurements in 868 MHz ISM Band in Residential Areas

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