Spatio-temporal Spectrum Load Prediction using Convolutional Neural Network and Bayesian Estimation

Ren, Xiangyu; Mosavat-Jahromi, Hamed; Cai, Lin; Kidston, David

doi:10.1109/globecom42002.2020.9348001

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…When the transmission beam of the PU is well identified, SU may exploit the remaining space in order to transmit the data on a non-overlapping beams base [125,199,200]. SUs, in this case, should be equipped with a multi-antenna system in order to adjust their beam far from the primary receiver to avoid interference.…”

Section: Beamforming-based Communicationmentioning

confidence: 99%

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

Nasser

Hassan

Chaaya

et al. 2021

Sensors

127

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Spectrum Sensing (SS) plays an essential role in Cognitive Radio (CR) networks to diagnose the availability of frequency resources. In this paper, we aim to provide an in-depth survey on the most recent advances in SS for CR. We start by explaining the Half-Duplex and Full-Duplex paradigms, while focusing on the operating modes in the Full-Duplex. A thorough discussion of Full-Duplex operation modes from collision and throughput points of view is presented. Then, we discuss the use of learning techniques in enhancing the SS performance considering both local and cooperative sensing scenarios. In addition, recent SS applications for CR-based Internet of Things and Wireless Sensors Networks are presented. Furthermore, we survey the latest achievements in Spectrum Sensing as a Service, where the Internet of Things or the Wireless Sensor Networks may play an essential role in providing the CR network with the SS data. We also discuss the utilisation of CR for the 5th Generation and Beyond and its possible role in frequency allocation. With the advancement of telecommunication technologies, additional features should be ensured by SS such as the ability to explore different available channels and free space for transmission. As such, we highlight important future research axes and challenging points in SS for CR based on the current and emerging techniques in wireless communications.

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Section: Beamforming-based Communicationmentioning

confidence: 99%

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

Nasser

Hassan

Chaaya

et al. 2021

Sensors

127

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“…In [30], a new network, ConvLSTM, which combines CNNs and LSTM, was used to learn time-frequency-space multi-dimensional correlations and long-term predictions. In addition, a network combining a CNN and ResNet was used to jointly predict in the time and spatial domains [31].…”

Section: Literature Reviewmentioning

confidence: 99%

Big Data Analysis and Prediction of Electromagnetic Spectrum Resources: A Graph Approach

et al. 2022

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In the field of wireless communication, the increasing number of devices makes limited spectrum resources more scarce and accelerates the complexity of the electromagnetic environment, posing a serious threat to the sustainability of the industry’s development. Therefore, new effective technical methods are needed to mine and analyze the activity rules of spectrum resources to reduce the risk of frequency conflict. This paper introduces the idea of graphs and proposes a spectrum resource analysis and prediction architecture based on big data. In this architecture, a spatial correlation model of spectrum activities is constructed through feature extraction. In addition, based on this correlation model, a depth learning network based on graph convolution is designed, which uses the prior information of spatial activity to achieve the efficient prediction of spectrum resources. Numerical experiments were carried out on two datasets with different spatial scales. Compared with the best baseline model, the prediction error is reduced by 8.3% on the small-scale dataset and 11.7% on the large-scale dataset. This shows that the proposed method is applicable to different spatial scales and has more obvious advantages in complex scenes with large spatial scales. It can effectively use the results of spatial domain analysis to improve the prediction accuracy of spectrum resources.

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“…The authors show that the proposed clusterization strategy can improve prediction accuracy. Ren et al 10 propose the use of Convolutional Neural networks (CNN) to forecast future RSS and its correlation to the PU spacial position. A single channel scenario is evaluated where a 100 sensors are uniformly distributed in a given geographical area.…”

Section: Related Workmentioning

confidence: 99%

“…When evaluating signal power level correlations, SU's may explore parameters such as range of interference in order to determine channel quality within a given geographic area. Beyond channel status determination, such approaches allow SU's to establish spatial‐temporal correlations regarding PU activity 5,10,11 . That is, the monitoring task aims to identify the PU's transmission pattern over time, allowing the SU to improve transmission success rates.…”

Section: Introductionmentioning

confidence: 99%

“…Beyond channel status determination, such approaches allow SU's to establish spatial-temporal correlations regarding PU activity. 5,10,11 That is, the monitoring task aims to identify the PU's transmission pattern over time, allowing the SU to improve transmission success rates. Statistical and machine learning techniques have been used to forecast spectrum opportunities based on acquired PU transmission data.…”

Section: Introductionmentioning

confidence: 99%

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Predicting consecutive spectrum opportunities with hidden Markov models

Bezerra

Bordim

2022

Concurrency and Computation

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Summary The increasing demands for wireless channels to accommodate the surge of internet of things devices and the associated services exacerbated the need for flexible channel allocation strategies. Opportunistic spectrum sharing is expected to provide a more reasonable use of the limited radio frequencies by allowing the coexistence of licensed users and unlicensed users in the same frequency. This arrangement is called opportunistic channel allocation, where unlicensed users explore the channel when the licensed user is not transmitting. The challenge in opportunistic spectrum allocation is to find transmission opportunities. Statistical and machine learning techniques have been used to forecast spectrum opportunities on time slotted channels based on acquired transmission data from licensed users. However, opportunity forecast is usually limited to one slot ahead of time. This work explores the use of Hidden Markov Model (HMM) training and predicting procedures to forecast a consecutive sequence of idle slots that can be explored for opportunistic transmission. To improve prediction accuracy and speedup the training process, the proposed training procedures reduce the classification alphabet, which is achieved by balancing the number of training sequences to limit the influence of outliers and provide opportunity forecast even when the training process is executed over a limited number of observed sequences. While an aggressive predictor may explore more opportunities as compared to a conservative predictor, the former may incur in higher collision rates while the latter may explore fewer opportunities. Hence, this work proposes a metric to assist in the process of selecting the best predictor's parameters to balance collision and opportunity rates. The proposed HMM predictors are evaluated on both synthetic and real traffic traces. The results on synthetic traffic shows that, for channel load from 30%$$ 30\% $$ up to 70%$$ 70\% $$, the proposed predictors identified over 80%$$ 80\% $$ of channel opportunities with the collision rates below 10%$$ 10\% $$. On real traffic traces, where the licensed users' transmissions have deterministic inter‐packet delays, the HMM predictors identified over 95%$$ 95\% $$ of the channel opportunities with collision rates below 1%$$ 1\% $$.

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Spatio-temporal Spectrum Load Prediction using Convolutional Neural Network and Bayesian Estimation

Cited by 6 publications

References 10 publications

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

Big Data Analysis and Prediction of Electromagnetic Spectrum Resources: A Graph Approach

Predicting consecutive spectrum opportunities with hidden Markov models

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