An Investigation on Online Versus Batch Learning in Predicting User Behaviour

Burlutskiy, Nikolay; Petridis, Miltos; Fish, Andrew; Chernov, Alexey; Alí, Nour

doi:10.1007/978-3-319-47175-4_9

Cited by 9 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, ANNs may enable multiple BSs to learn how to form multi-hop, mmWave links over backhaul infrastructure, while properly allocating resources across those links in an autonomous manner [154], [155]. To cope with the changes in the traffic model and/or the users' mobility pattern, ANNs can be combined with online ML [156] by properly re-training the weights of the developed learning mechanisms. Multi-mode BSs can, thus, learn the traffic patterns over time and, thus, predict the future channel availability status.…”

Section: E Co-existence Of Multiple Radio Access Technologies 1) Co-e...mentioning

confidence: 99%

Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial

Chen

Challita

Saad

et al. 2019

IEEE Commun. Surv. Tutorials

800

418

View full text Add to dashboard Cite

In order to effectively provide ultra reliable low latency communications and pervasive connectivity for Internet of Things (IoT) devices, next-generation wireless networks can leverage intelligent, data-driven functions enabled by the integration of machine learning notions across the wireless core and edge infrastructure. In this context, this paper provides a comprehensive tutorial that overviews how artificial neural networks (ANNs)-based machine learning algorithms can be employed for solving various wireless networking problems. For this purpose, we first present a detailed overview of a number of key types of ANNs that include recurrent, spiking, and deep neural networks, that are pertinent to wireless networking applications. For each type of ANN, we present the basic architecture as well as specific examples that are particularly important for wireless network design. Such examples include echo state networks, liquid state machine, and long short term memory. And then, we provide an in-depth overview on the variety of wireless communication problems that can be addressed using ANNs, ranging from communication using unmanned aerial vehicles to virtual reality applications over wireless networks and edge computing and caching. For each individual application, we present the main motivation for using ANNs along with the associated challenges while we also provide a detailed example for a use case scenario and outline future works that can be addressed using ANNs. In a nutshell, this article constitutes the first holistic tutorial on the development of ANN-based machine learning techniques tailored to the needs of future wireless networks. arXiv:1710.02913v2 [cs.IT] 30 Jun 2019• Input gate (i t ): controls whether the input is passed on to the memory cell or ignored. • Output gate (o t ): controls whether the current activation

show abstract

Section: E Co-existence Of Multiple Radio Access Technologies 1) Co-e...mentioning

confidence: 99%

Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial

Chen

Challita

Saad

et al. 2019

IEEE Commun. Surv. Tutorials

800

418

View full text Add to dashboard Cite

show abstract

“…Then, the total computational complexity of the GAN-1SVM model in training is O(2(dN trIL) + (N te 3 )). As reported in [62], the time complexity of DBN is similar to that of SVM, O(N te 3 ), which is relatively high. The time complexity for training the RBM model is O(c(m + n)), where n and m denote the number of visible and hidden units, and c denotes the number of iterations [57], [63].…”

Section: B Modulation Identificationmentioning

confidence: 74%

A Semi-Supervised Modulation Identification in MIMO Systems: A Deep Learning Strategy

et al. 2022

View full text Add to dashboard Cite

Accurate modulation identification of the received signals is undoubtedly a central component in multiple-input multiple-output (MIMO) communication systems, facilitating the demodulation task. This study presents a flexible and semi-supervised deep learning-driven strategy for automatic modulation identification. To this end, the multiclass classification problem is treated as multiple binary discrimination problems to address modulation identification challenges. Here, we merge the features extraction ability of the Generative Adversarial Network (GAN) model and the semi-supervised anomaly detection scheme, the one-class Support Vector Machine (1SVM). Essentially, a single GAN-based 1SVM detector is trained using training data of each class, with the samples of that class as inlier and all other samples as anomalies (i.e., one-vs.-rest). The 1SVM is trained using the features learned by the GAN model. A dataset consisting of three digital modulations (i.e., BFSK, CPFSK, and PAM4) and three analog modulations (i.e., AM-DSB, AM-SSB, and WB-FM), widely used in wireless communications systems, is employed to demonstrate the performance of the considered deep learning-based methods. Compared to Restricted Boltzmann Machine (RBM) and Deep Belief Network (DBN)-based 1SVM, the conventional GAN, DBN, and RBM with softmax layer as discriminator layer, the proposed GAN-based 1SVM detector offers superior discrimination performance of modulation types by achieving an averaged accuracy of 0.951 and F1-Score of 0.954. Results also showed that the GAN-1SVM detector dominates the state-of-the-art modulation classification techniques.

show abstract

“…end for can be considered to be negligible. The proposed approach can also be combined with online machine learning [37] to accommodate changes in the traffic model, by properly re-training the developed learning mechanism. Consequently, the proposed algorithm offers a practical solution that is amenable to implementation.…”

Section: End Whilementioning

confidence: 99%

Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective

Challita

Dong

Saad

2018

IEEE Trans. Wireless Commun.

172

134

View full text Add to dashboard Cite

LTE in unlicensed spectrum using licensed assisted access LTE (LTE-LAA) is a promising approach to overcome the wireless spectrum scarcity. However, to reap the benefits of LTE-LAA, a fair coexistence mechanism with other incumbent WiFi deployments is required. In this paper, a novel deep learning approach is proposed for modeling the resource allocation problem of LTE-LAA small base stations (SBSs). The proposed approach enables multiple SBSs to proactively perform dynamic channel selection, carrier aggregation, and fractional spectrum access while guaranteeing fairness with existing WiFi networks and other LTE-LAA operators. Adopting a proactive coexistence mechanism enables future delay-tolerant LTE-LAA data demands to be served within a given prediction window ahead of their actual arrival time thus avoiding the underutilization of the unlicensed spectrum during off-peak hours while maximizing the total served LTE-LAA traffic load. To this end, a noncooperative game model is formulated in which SBSs are modeled as Homo Egualis agents that aim at predicting a sequence of future actions and thus achieving long-term equal weighted fairness with WLAN and other LTE-LAA operators over a given time horizon. The proposed deep learning algorithm is then shown to reach a mixed-strategy Nash equilibrium (NE), when it converges. Simulation results using real data traces show that the proposed scheme can yield up to 28% and 11% gains over a conventional reactive approach and a proportional fair coexistence mechanism, respectively. The results also show that the proposed framework prevents WiFi performance degradation for a densely deployed LTE-LAA network. Index TermsLicensed assisted access LTE (LTE-LAA); LTE-U; small cell; unlicensed band; long short term memory (LSTM); deep reinforcement learning; game theory; proactive resource allocation A preliminary version of this work was published at European Wireless 2017 [1].

show abstract

An Investigation on Online Versus Batch Learning in Predicting User Behaviour

Cited by 9 publications

References 16 publications

Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial

Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial

A Semi-Supervised Modulation Identification in MIMO Systems: A Deep Learning Strategy

Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective

Contact Info

Product

Resources

About