publications on machine learning

We have developed a new framework using time-series analysis for dynamically assigning mobile network traffic prediction models in previously unseen wireless environments. Our framework selectively employs learned behaviors, outperforming any single model with over a 50% improvement relative to current studies. More importantly, it surpasses traditional approaches without needing prior knowledge of a cell. While this paper focuses on network traffic prediction using our adaptive forecasting framework, this framework can also be applied to other machine learning applications in uncertain environments. The framework begins with unsupervised clustering of time-series data to identify unique trends and seasonal patterns. Subsequently, we apply supervised learning for traffic volume prediction within each cluster. This specialization towards specific traffic behaviors occurs without penalties from spatial and temporal variations. Finally, the framework adaptively assigns trained models to new, previously unseen cells. By analyzing real-time measurements of a cell, our framework intelligently selects the most suitable cluster for that cell at any given time, with cluster assignment dynamically adjusting to spatio-temporal fluctuations.

We introduce a Channel Distribution Information (CDI)-aware Generative Adversarial Network (GAN), designed to address the unique challenges of adversarial attacks in wireless communication systems. The generator in this CDI-aware GAN maps random input noise to the feature space, generating perturbations intended to deceive a target modulation classifier. Its discriminators play a dual role: one enforces that the perturbations follow a Gaussian distribution, making them indistinguishable from Gaussian noise, while the other ensures these perturbations account for realistic channel effects and resemble no-channel perturbations. Our proposed CDI-aware GAN can be used as an attacker and a defender. In attack scenarios, the CDI-aware GAN demonstrates its prowess by generating robust adversarial perturbations that effectively deceive the target classifier, outperforming known methods. Furthermore, CDI-aware GAN as a defender significantly improves the target classifier's resilience against adversarial attacks.

Adversarial Machine Learning (AML) has shown significant success when applied to deep learning models across various domains. This paper delves into channel-aware adversarial attacks on DNN-based modulation classification models within wireless environments. Our investigation centers on the robustness of these attacks concerning channel distribution and path-loss parameters. We examine two scenarios: one where the attacker has instantaneous channel knowledge and the other where the attacker is informed by statistical channel data. In both cases, we study channels under conditions of Rayleigh fading alone, Rayleigh fading combined with shadowing, and Rayleigh fading with both shadowing and path loss. Our findings reveal that the success of an AML attack is largely dictated by the distance between the attacker and the legitimate receiver. Without precise distance estimation, adversarial attacks are likely to fail.

This study presents a spatiotemporal traffic prediction approach for NextG mobile networks, ensuring the service-level agreements (SLAs) of each network slice. Our approach is multivariate, multi-step, and spatiotemporal. Leveraging 20 radio access network (RAN) features, peak traffic hour data, and mobility-based clustering, we propose a parametric SLA-based loss function to guarantee an SLA violation rate. We focus on single-cell, multi-cell, and slice-based prediction approaches and present a detailed comparative analysis of their performances, strengths, and limitations. First, we address the application of single-cell and multi-cell training architectures. While single-cell training offers individual cell-level prediction, multi-cell training involves training a model using traffic from multiple cells from the same or different base stations. We show that the single-cell approach outperforms the multi-cell approach and results in test loss improvements of 11.4\% and 38.1\% compared to baseline SLA-based and MAE-based models, respectively. Next, we explore slice-based traffic prediction. We present single-slice and multi-slice methods for slice-based downlink traffic volume prediction, arguing that multi-slice prediction offers a more accurate forecast. The slice-based model we introduce offers substantial test loss improvements of 28.2\%, 36.4\%, and 55.6\% compared to our cell-based model, the baseline SLA-based model, and the baseline MAE-based model, respectively.

This paper focuses on predicting downlink (DL) traffic volume in mobile networks while minimizing overprovisioning and meeting a given service-level agreement (SLA) violation rate. We present a multivariate, multi-step, and SLA-driven approach that incorporates 20 different radio access network (RAN) features, a custom feature set based on peak traffic hours, and handover-based clustering to leverage the spatiotemporal effects. In addition, we propose a custom loss function that ensures the SLA violation rate constraint is satisfied while minimizing overprovisioning. We also perform multi-step prediction up to 24 steps ahead and evaluate performance under both single-step and multi-step prediction conditions. Our study makes several contributions, including the analysis of RAN features, the custom feature set design, a custom loss function, and a parametric method to satisfy SLA constraints.

We consider an RNN-based traffic volume prediction, which is a critical problem for network slice management and resource allocation in slicing-enabled next generation cellular networks. We propose to use a novel cost function that takes SLA violations into account. Our approach is multivariate and spatio-temporal in three aspects. First, we consider the effects of several other RAN features in a cell besides the traffic volume. Second, we introduce feature vectors based on peak hours of the day and days of the week. Third, we introduce feature vectors based on incoming handover statistics from the neighboring cells. Our results show about 60\% improvement over MAE-based univariate LSTM models and about 20\% improvement over SLA-based univariate models.