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The increasing demand for efficient resource allocation in mobile networks has catalyzed the exploration of innovative solutions that could enhance the task of real-time cellular traffic prediction. Under these circumstances, federated learning (FL) stands out as a distributed and privacy-preserving solution to foster collaboration among different sites, thus enabling responsive near-the-edge solutions. In this paper, we comprehensively study the potential benefits of FL in telecommunications through a case study on federated traffic forecasting using real-world data from base stations (BSs) in Barcelona (Spain). Our study encompasses relevant aspects within the federated experience, including model aggregation techniques, outlier management, the impact of individual clients, personalized learning, and the integration of exogenous sources of data. The performed evaluation is based on both prediction accuracy and sustainability, thus showcasing the environmental impact of employed FL algorithms in various settings. The findings from our study highlight FL as a promising and robust solution for mobile traffic prediction, emphasizing its twin merits as a privacy-conscious and environmentally sustainable approach, while also demonstrating its capability to overcome data heterogeneity and ensure high-quality predictions, marking a significant stride towards its integration in mobile traffic management systems.

We consider collaborative inference at the wireless edge, where each client's model is trained independently on its local dataset. Clients are queried in parallel to make an accurate decision collaboratively. In addition to maximizing the inference accuracy, we also want to ensure the privacy of local models. To this end, we leverage the superposition property of the multiple access channel to implement bandwidth-efficient multi-user inference methods. We propose different methods for ensemble and multi-view classification that exploit over-the-air computation (OAC). We show that these schemes perform better than their orthogonal counterparts with statistically significant differences while using fewer resources and providing privacy guarantees. We also provide experimental results verifying the benefits of the proposed OAC approach to multi-user inference, and perform an ablation study to demonstrate the effectiveness of our design choices. We share the source code of the framework publicly on Github to facilitate further research and reproducibility., Comment: 17 pages, 8 figures. This work extends from our preliminary study presented at the 2022 IEEE International Symposium on Information Theory [1]. arXiv admin note: text overlap with arXiv:2202.03129

We consider low-latency image transmission over a noisy wireless channel when correlated side information is present only at the receiver side (the Wyner-Ziv scenario). In particular, we are interested in developing practical schemes using a data-driven joint source-channel coding (JSCC) approach, which has been previously shown to outperform conventional separation-based approaches in the practical finite blocklength regimes, and to provide graceful degradation with channel quality. We propose a novel neural network architecture that incorporates the decoder-only side information at multiple stages at the receiver side. Our results demonstrate that the proposed method succeeds in integrating the side information, yielding improved performance at all channel conditions in terms of the various quality measures considered here, especially at low channel signal-to-noise ratios (SNRs) and small bandwidth ratios (BRs). We have made the source code of the proposed method public to enable further research, and the reproducibility of the results., Comment: To appear in IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN) 2024

We consider the image transmission problem over a noisy wireless channel via deep learning-based joint source-channel coding (DeepJSCC) along with a denoising diffusion probabilistic model (DDPM) at the receiver. Specifically, we are interested in the perception-distortion trade-off in the practical finite block length regime, in which separate source and channel coding can be highly suboptimal. We introduce a novel scheme, where the conventional DeepJSCC encoder targets transmitting a lower resolution version of the image, which later can be refined thanks to the generative model available at the receiver. In particular, we utilize the range-null space decomposition of the target image; DeepJSCC transmits the range-space of the image, while DDPM progressively refines its null space contents. Through extensive experiments, we demonstrate significant improvements in distortion and perceptual quality of reconstructed images compared to standard DeepJSCC and the state-of-the-art generative learning-based method., Comment: 6 pages, 5 figures. Published at INFOCOM 2024 Workshops

Cellular traffic prediction is a crucial activity for optimizing networks in fifth-generation (5G) networks and beyond, as accurate forecasting is essential for intelligent network design, resource allocation and anomaly mitigation. Although machine learning (ML) is a promising approach to effectively predict network traffic, the centralization of massive data in a single data center raises issues regarding confidentiality, privacy and data transfer demands. To address these challenges, federated learning (FL) emerges as an appealing ML training framework which offers high accurate predictions through parallel distributed computations. However, the environmental impact of these methods is often overlooked, which calls into question their sustainability. In this paper, we address the trade-off between accuracy and energy consumption in FL by proposing a novel sustainability indicator that allows assessing the feasibility of ML models. Then, we comprehensively evaluate state-of-the-art deep learning (DL) architectures in a federated scenario using real-world measurements from base station (BS) sites in the area of Barcelona, Spain. Our findings indicate that larger ML models achieve marginally improved performance but have a significant environmental impact in terms of carbon footprint, which make them impractical for real-world applications., Comment: International Symposium on Federated Learning Technologies and Applications (FLTA), 2023

We consider distributed image transmission over a noisy multiple access channel (MAC) using deep joint source-channel coding (DeepJSCC). It is known that Shannon's separation theorem holds when transmitting independent sources over a MAC in the asymptotic infinite block length regime. However, we are interested in the practical finite block length regime, in which case separate source and channel coding is known to be suboptimal. We introduce a novel joint image compression and transmission scheme, where the devices send their compressed image representations in a non-orthogonal manner. While non-orthogonal multiple access (NOMA) is known to achieve the capacity region, to the best of our knowledge, non-orthogonal joint source channel coding (JSCC) scheme for practical systems has not been studied before. Through extensive experiments, we show significant improvements in terms of the quality of the reconstructed images compared to orthogonal transmission employing current DeepJSCC approaches particularly for low bandwidth ratios. We publicly share source code to facilitate further research and reproducibility., Comment: To appear in IEEE International Conference on Communications (ICC) 2023

As wireless standards evolve, more complex functionalities are introduced to address the increasing requirements in terms of throughput, latency, security, and efficiency. To unleash the potential of such new features, artificial intelligence (AI) and machine learning (ML) are currently being exploited for deriving models and protocols from data, rather than by hand-programming. In this paper, we explore the feasibility of applying ML in next-generation wireless local area networks (WLANs). More specifically, we focus on the IEEE 802.11ax spatial reuse (SR) problem and predict its performance through federated learning (FL) models. The set of FL solutions overviewed in this work is part of the 2021 International Telecommunication Union (ITU) AI for 5G Challenge.

We consider distributed inference at the wireless edge, where multiple clients with an ensemble of models, each trained independently on a local dataset, are queried in parallel to make an accurate decision on a new sample. In addition to maximizing inference accuracy, we also want to maximize the privacy of local models. We exploit the superposition property of the air to implement bandwidth-efficient ensemble inference methods. We introduce different over-the-air ensemble methods and show that these schemes perform significantly better than their orthogonal counterparts, while using less resources and providing privacy guarantees. We also provide experimental results verifying the benefits of the proposed over-the-air inference approach, whose source code is shared publicly on Github., Comment: To appear in IEEE International Symposium on Information Theory (ISIT) 2022

PySAD is an open-source python framework for anomaly detection on streaming data. PySAD serves various state-of-the-art methods for streaming anomaly detection. The framework provides a complete set of tools to design anomaly detection experiments ranging from projectors to probability calibrators. PySAD builds upon popular open-source frameworks such as PyOD and scikit-learn. We enforce software quality by enforcing compliance with PEP8 guidelines, functional testing and using continuous integration. The source code is publicly available on https://github.com/selimfirat/pysad., Comment: 6 pages, 1 figure

We investigate cross-lingual sentiment analysis, which has attracted significant attention due to its applications in various areas including market research, politics and social sciences. In particular, we introduce a sentiment analysis framework in multi-label setting as it obeys Plutchik wheel of emotions. We introduce a novel dynamic weighting method that balances the contribution from each class during training, unlike previous static weighting methods that assign non-changing weights based on their class frequency. Moreover, we adapt the focal loss that favors harder instances from single-label object recognition literature to our multi-label setting. Furthermore, we derive a method to choose optimal class-specific thresholds that maximize the macro-f1 score in linear time complexity. Through an extensive set of experiments, we show that our method obtains the state-of-the-art performance in 7 of 9 metrics in 3 different languages using a single model compared to the common baselines and the best-performing methods in the SemEval competition. We publicly share our code for our model, which can perform sentiment analysis in 100 languages, to facilitate further research., Comment: 11 pages, 6 figures

Recurrent Neural Networks (RNNs) are widely used for online regression due to their ability to generalize nonlinear temporal dependencies. As an RNN model, Long-Short-Term-Memory Networks (LSTMs) are commonly preferred in practice, as these networks are capable of learning long-term dependencies while avoiding the vanishing gradient problem. However, due to their large number of parameters, training LSTMs requires considerably longer training time compared to simple RNNs (SRNNs). In this paper, we achieve the online regression performance of LSTMs with SRNNs efficiently. To this end, we introduce a first-order training algorithm with a linear time complexity in the number of parameters. We show that when SRNNs are trained with our algorithm, they provide very similar regression performance with the LSTMs in two to three times shorter training time. We provide strong theoretical analysis to support our experimental results by providing regret bounds on the convergence rate of our algorithm. Through an extensive set of experiments, we verify our theoretical work and demonstrate significant performance improvements of our algorithm with respect to LSTMs and the other state-of-the-art learning models., Comment: arXiv admin note: substantial text overlap with arXiv:2003.03601

We investigate unsupervised anomaly detection for high-dimensional data and introduce a deep metric learning (DML) based framework. In particular, we learn a distance metric through a deep neural network. Through this metric, we project the data into the metric space that better separates the anomalies from the normal data and reduces the effect of the curse of dimensionality for high-dimensional data. We present a novel data distillation method through self-supervision to remedy the conventional practice of assuming all data as normal. We also employ the hard mining technique from the DML literature. We show these components improve the performance of our model and significantly reduce the running time. Through an extensive set of experiments on the 14 real-world datasets, our method demonstrates significant performance gains compared to the state-of-the-art unsupervised anomaly detection methods, e.g., an absolute improvement between 4.44% and 11.74% on the average over the 14 datasets. Furthermore, we share the source code of our method on Github to facilitate further research., Comment: 11 pages, 3 figures

We investigate online nonlinear regression with continually running recurrent neural network networks (RNNs), i.e., RNN-based online learning. For RNN-based online learning, we introduce an efficient first-order training algorithm that theoretically guarantees to converge to the optimum network parameters. Our algorithm is truly online such that it does not make any assumption on the learning environment to guarantee convergence. Through numerical simulations, we verify our theoretical results and illustrate significant performance improvements achieved by our algorithm with respect to the state-of-the-art RNN training methods., Comment: This paper was an early draft of the presented results. We have written and published another paper (arXiv:2005.08948) where we have improved the material in this paper. The published paper covers most of the material presented in this paper as well. Therefore, we remove this paper from Arxiv and kindly refer the interested readers to arXiv:2005.08948

Pattern recognition and machine learning techniques are known to play an emerging role for studying underlying physics of light-matter interaction. While on the subject, photon condensation and Anti-Stokes cooling remain compelling phenomena of laser-underdense plasma interaction. In this work, the interaction of a 1064 nm continuum-wave laser with inductively-coupled plasma (ICP) of Mercury(Hg) has been studied by probabilistic pattern recognition over observed time resolved spectral data.3D vector fields obtained by the probabilistic pattern recognition over spectral database proficiently illuminates the analogue black hole (ABH) and its sonic horizon in which homoclinic orbit Whistler waves are exited. Vector spectra show the collective behavior of condensation and anti-Stokes cooling is due to the electronic transitions of Hg1 trapped by the phonon sink of ABH of laser. Modeling of subsonic phonon vector spectrum obtained by the probabilistic linear discriminant analysis(PLDA) estimates the 0.70 nanoKelvin of temperature of the sink region of ABH.

In this paper, the effects of electron beam on the X pinch produced spectra of L-shell Mo plasma have been investigated by principal component analysis (PCA), and this analysis is compared with that of line ratio diagnostics. Spectral database for PCA extraction was arranged using the non-LTE collisional radiative L-shell Mo model. PC vector spectra of L shell Mo including F, Ne, Na and Mg- like transitions were studied to investigate the polarization types of these transitions. PC1 vector spectra of F, Ne, Na and Mg- like transitions resulted in linear polarization of Stokes Q profiles. Besides, PC2 vector spectra showed linear polarization of Stokes U profiles of 2p53s of Ne like transitions which were recognized as responsive to the magnetic field (Trabert et al., 2017). 3D representation of PCA coefficients demonstrated that addition of electron beam to the non-LTE model generates the quantized collective clusters which are translations of each other and follow V-shaped cascade trajectories except for the case f = 0.0. The extracted principal coefficients were used as a database for the Artifical Neural Network (ANN) to estimate the plasma electron temperature, density and beam fractions of time integrated spatially resolved L-shell Mo X-pinch plasma spectrum. PCA based ANN provides advantage in reducing the network topology with a more efficient Backpropagation supervised learning algorithm. The modeled plasma electron temperature is about Te~660 eV and density ne=1x1020 cm-3 in the presence of the fraction of the beams with f~0.1 and centered energy of 5 keV.

Here, we have employed Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to analyze Mie calculated UV-Vis spectra of gold nanospheres (GNS). Eigen spectra of PCA perform the Fano type resonances.3D vector field spectra reveal the Homoclinic orbit Lorenz attractor. Quantum confinement effects are observed by 3D representation of LDA. Standing wave patterns resulting from oscillations of ion acoustic phonon and electron waves are illustrated through the eigen spectra of LDA. Such capabilities of GNPs have brought high attention for the high energy density physics applications. Furthermore, accurate prediction of gold nanoparticle (GNP) sizes using machine learning could provide rapid analysis without the need for expensive analysis. Two hybrid algorithms consist of unsupervised PCA and two different supervised ANN have been used to estimate the diameters of GNPs. PCA based artificial neural network (ANN) were found to estimate the diameters with a high accuracy.

In this work, the influence of crossed magnetic fields (B=100 Gauss) on the UV-vis and NIR spectra and breakdown voltages of nitrogen and oxygen at a pair of parallel plane copper electrodes with a spacing d=2 cm and diameter R= 2.2 cm are studied. Working pressures of the gases are kept between 0.1 and 1 Torr. The breakdown voltage measurements across the electrodes are conducted by Tektronix P6015 high voltage probe connected to a Tektronix 2430A oscilloscope. The Spectra of discharge plasmas in the absence and presence of magnetic fields are recorded between 200-1100 nm. by AvaSpec-ULS3648. In order to analyse observed spectra of nitrogen and oxygen plasmas, one of the pattern recognition techniques of principal component analysis has been employed. Results of principal component analysis shows that the presence of magnetic field cause the plasma particles to move in condensed way for the oxygen but not for the nitrogen. Principal component spectra shows that small amount of the cross magnetic field results in the Stokes linear polarization of the most of the oxygen and NIII (337 nm) of the Nitrogen species in the plasmas. The non-local thermal equilibrium based spectroscopic modeling of nitrogen and oxygen plasma gives that the electron temperature of nitrogen and oxygen are Te=25000 and 5000 Kelvin respectively.