Machine Learning for LTE Energy Detection Performance Improvement

Wasilewska, Małgorzata; Bogucka, Hanna

doi:10.3390/s19194348

Cited by 19 publications

(11 citation statements)

References 30 publications

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“…Although the most intricate deep learning algorithms are popular in processing data of this complexity, simple algorithms can be employed in finding space/time/frequency dependencies as well. Wasilewska et al [54], [166] focused on signal sensing for which there occur strong correlations in time, frequency and space. In [54] sensing is performed separately for different locations in space, but in [166] the learning is performed including localization information as ML input.…”

Section: Sensing/prediction Of Signals With Time Frequency and Spatial Dependenciesmentioning

confidence: 99%

“…Wasilewska et al [54], [166] focused on signal sensing for which there occur strong correlations in time, frequency and space. In [54] sensing is performed separately for different locations in space, but in [166] the learning is performed including localization information as ML input. In both of the papers, simple ML algorithms have been employed, namely, kNN and random forest.…”

Section: Sensing/prediction Of Signals With Time Frequency and Spatial Dependenciesmentioning

confidence: 99%

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Artificial Intelligence for Radio Communication Context-Awareness

et al. 2021

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This paper surveys Artificial Intelligence (AI) methods for acquiring and managing contextof-operation awareness of radio communication nodes, links, and networks. The meaning and significance of context information and suitability of Machine Learning (ML) methods for the enrichment of context information is discussed. A number of context features are considered in this regard and thorough analysis on which ML methods are suitable to which part of context learning is provided. The added value of the paper is the presentation of a synthesized framework of context-information processing, sharing, and management in a radio communication network by delineating a network-embedded subsystem for this management. Recommendations for a future AI/ML-based radio communication system architectures are also provided.

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Section: Sensing/prediction Of Signals With Time Frequency and Spatial Dependenciesmentioning

confidence: 99%

Section: Sensing/prediction Of Signals With Time Frequency and Spatial Dependenciesmentioning

confidence: 99%

Artificial Intelligence for Radio Communication Context-Awareness

et al. 2021

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“…This paper provides an overview of some of the algorithms and corrections that enable LR from a machine learning point of view to be both fast and accurate. In [20], KNN learning-based classification technique is implemented for cooperative spectrum sensing (CSS), KNN required a very small amount of time for training the classifiers. In [21], this paper tests cognitive radio network (CRN) spectrum occupancy based on the naive Bayesian classifier (NBC), The motive for this work is the problem of classification in spectrum sensing, where secondary users (SUs) must sense and use the free channel for their purpose of transmission/reception.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Based Analysis of Cellular Spectrum

Yasir¹,

Shah²,

Memon³

et al. 2021

IJWMT

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One of the key issues of wireless communication networks is the spectrum crisis, and studies noted that static licensed bands are in the under-utilization stage. Recently Cognitive Radio Network facilitates a solution to minimize the spectrum crisis in which unlicensed users can utilize the licensed spectrum without transmission interference. To achieve this task we used Machine Learning techniques for analyzing spectrum occupancy which is an efficient method to analyze spectrum occupancy and provides high accuracy. Supervised machine learning algorithms namely Logistic regression, K nearest neighbor, and Naive Bayes are used to classify a given frequency band. In this paper we collect spectrum samples of GSM 900, 1800, and 2100 bands using Rohde & Schwarz FSH6 Handheld Spectrum Analyzer for developing a dataset, using that dataset we trained the classifiers and analyze their classification performance accuracy. Results have shown the best performance on the validation and testing partition for various Unweighted Average Recall (UAR) of each classifier. Here the Logistic Regression classifier learns the best representation from their feature vector. This research is helpful to measure the spectrum occupancy of different static allocated licensed bands for 24/7. This will give better ideas about spectrum utilization, future spectrum allocation and comfort to serve more users in the limited spectrum. The occupancy measurements of current allocated spectrums can not only provide a convincing basis for making future spectrum allocation policies, but also provide technical support for the development of new communication technologies.

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“…The probability of detection and false alarm are analyzed for the different SNR values and for the different number of samples [10]. The machine learning algorithms is used for improving the spectrum sensing [11][12][13][14][15][16][17]. Among the machine learning algorithms prescribed by the author, KNN and random forest for the spectrum usage opportunistically.…”

Section: Introductionmentioning

confidence: 99%

Fast and accurate primary user detection with machine learning techniques for cognitive radio networks

Pethunachiyar

Sankaragomathi

2021

IJEECS

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<p class="IJASEITAbtract"><span>Spectrum decision is an important and crucial task for the secondary user to avail the unlicensed spectrum for transmission. Managing the spectrum is an efficient one for spectrum sensing. Determining the primary user presence in the spectrum is an essential work for using the licensed spectrum of primary user. The information which lacks in managing the spectrum are the information about the primary user presence, accuracy in determining the existence of user in the spectrum, the cost for computation and difficult in finding the user in low signal-to noise ratio (SNR) values. The proposed system overcomes the above limitations. In the proposed system, the various techniques of machine learning like decision tree, support vector machines, naive bayes, ensemble based trees, nearest neighbour’s and logistic regression are used for testing the algorithm. As a first step, the spectrum sensing is done in two stages with Orthogonal Frequency Division Multiplexing and Energy Detection algorithm at the various values of SNR. The results generated from the above algorithm is used for database generation. Next, the different machine learning techniques are trained and compared for the results produced by different algorithms with the characteristics like speed, time taken for training and accuracy in prediction. The accuracy and finding the presence of the user in the spectrum at low SNR values are achieved by all the algorithms. The computation cost of the algorithm differs from each other. Among the tested techniques, k-nearest neighbour (KNN) algorithm produces the better performance in a minimized time.</span></p>

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Machine Learning for LTE Energy Detection Performance Improvement

Cited by 19 publications

References 30 publications

Artificial Intelligence for Radio Communication Context-Awareness

Artificial Intelligence for Radio Communication Context-Awareness

Machine Learning Based Analysis of Cellular Spectrum

Fast and accurate primary user detection with machine learning techniques for cognitive radio networks

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