Spectrum Occupancy Prediction in Coexisting Wireless Systems Using Deep Learning

Omotere, Oluwaseyi; Fuller, John; Qian, Lijun; Han, Zhu

doi:10.1109/vtcfall.2018.8690575

Cited by 30 publications

(13 citation statements)

References 18 publications

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“…The reason why the number of STAs can be predictable and the STAs are identifiable is that the spectrum usage identification can be considered as a multi-class classification problem, which can be well solved by training a deep neural network [27], [28]. In this paper, the AP only needs to identify the number and ID of the STAs involving collisions.…”

Section: ) Collision Detection and Identificationmentioning

confidence: 99%

Improving Medium Access Efficiency With Intelligent Spectrum Learning

et al. 2020

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Through machine learning, this paper changes the fundamental assumption of the traditional medium access control (MAC) layer design. It obtains the capability of retrieving the information even the packets collide by training a deep neural network offline with the historical radio frequency (RF) traces and inferring the STAs involved collisions online in near-real-time. Specifically, we propose a MAC protocol based on intelligent spectrum learning for the future wireless local area networks (WLANs), called SL-MAC. In the proposed MAC, an access point (AP) is installed with a pre-trained convolutional neural network (CNN) model to identify the stations (STAs) involved in the collisions. In contrast to the conventional contention-based random medium access methods, e.g., IEEE 802.11 distributed coordination function (DCF), the proposed SL-MAC protocol seeks to schedule data transmissions from the STAs suffering from the collisions. To achieve this goal, we develop a two-step offline training algorithm enabling the AP to sense the spectrum with the aid of the CNN. In particular, on receiving the overlapped signal(s), the AP firstly predicts the number of STAs involving collisions and then further identifies the STAs' ID. Furthermore, we analyze the upper bound of throughput gain brought by the CNN predictor and investigate the impact of the inference error on the achieved throughput. Extensive simulations show the superiority of the proposed SL-MAC and allow us to gain insights on the trade-off between performance gain and the inference accuracy. INDEX TERMS Medium access control (MAC), spectrum sensing, deep learning, convolutional neural network (CNN).

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Section: ) Collision Detection and Identificationmentioning

confidence: 99%

Improving Medium Access Efficiency With Intelligent Spectrum Learning

et al. 2020

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“…As far as it is known to authors, the CNN algorithm for spectrum occupancy prediction are usually used as a sensing or prediction tool for two dimensional data in cooperative sensing [11,12,21], where data collected from each of sensing SUs is merged into a set of input information for CNN algo-rithm. In [16] also NN, RNN and CNN are applied to predict the type, form and number of transmitting users in a frequency band, but data used for detection and prediction is a one-dimensional time-series data. The long-term prediction that has been based on spatial-spectral-temporal data has been addressed in [19], where a hybrid convolutional long short-term memory has been proposed for future spectrum state prediction.…”

Section: Introductionmentioning

confidence: 99%

Spectrum Sensing and Prediction for 5G Radio

Wasilewska

Bogucka

Kliks

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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In future wireless networks, it is crucial to find a way to precisely evaluate the degree of spectrum occupation and the exact parameters of free spectrum band at a given moment. This approach enables a secondary user (SU) to dynamically access the spectrum without interfering primary user's (PU) transmission. The known methods of signal detection or spectrum sensing (SS) enable making decision on spectrum occupancy by SU. The machine learning (ML), especially deep learning (DL) algorithms have already proved their ability to improve classic SS methods. However, SS can be insufficient to use the free spectrum efficiently. As an answer to this issue, the prediction of future spectrum state has been introduced. In this paper, three DL algorithms, namely NN, RNN and CNN have been proposed to accurately predict the 5G spectrum occupation in the time and frequency domain with the accuracy of a single resource block (RB). The results have been obtained for two different datasets: the 5G downlink signal with representation of daily traffic fluctuations and the sensor-network uplink signal characteristic for IoT. The obtained results prove DL algorithms usefulness for spectrum occupancy prediction and show significant improvement in detection and prediction for both low signal-to-noise ratio (SNR) and for high SNR compared with reference detection/prediction method discussed in the paper. Keywords: Spectrum sensing • Spectrum prediction • Machine learning • 5G • LTE • Convolutional neural network • Recurrent neural network • Neural network • Deep learning This work was supported by the DAINA project no. 2017/27/L/ST7/03166 "Cognitive Engine for Radio environmenT Awareness In Networks of the future" (CERTAIN) funded by the National Science Centre, Poland.

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“…Furthermore, deep neural networks, LSTM, and CNN-based models were designed. Then, their capabilities in spectrum occupancy prediction were compared [ 22 ]. In our previous work [ 23 ], time and frequency correlations were exploited to predict spectrum occupancy over real-world measurements.…”

Section: Introductionmentioning

confidence: 99%

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

Aygül

Nazzal

Saglam

et al. 2020

Sensors

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In cognitive radio systems, identifying spectrum opportunities is fundamental to efficiently use the spectrum. Spectrum occupancy prediction is a convenient way of revealing opportunities based on previous occupancies. Studies have demonstrated that usage of the spectrum has a high correlation over multidimensions, which includes time, frequency, and space. Accordingly, recent literature uses tensor-based methods to exploit the multidimensional spectrum correlation. However, these methods share two main drawbacks. First, they are computationally complex. Second, they need to re-train the overall model when no information is received from any base station for any reason. Different than the existing works, this paper proposes a method for dividing the multidimensional correlation exploitation problem into a set of smaller sub-problems. This division is achieved through composite two-dimensional (2D)-long short-term memory (LSTM) models. Extensive experimental results reveal a high detection performance with more robustness and less complexity attained by the proposed method. The real-world measurements provided by one of the leading mobile network operators in Turkey validate these results.

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Spectrum Occupancy Prediction in Coexisting Wireless Systems Using Deep Learning

Cited by 30 publications

References 18 publications

Improving Medium Access Efficiency With Intelligent Spectrum Learning

Improving Medium Access Efficiency With Intelligent Spectrum Learning

Spectrum Sensing and Prediction for 5G Radio

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

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