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Deep automation provided by self-organizing network (SON) features and their emerging variants such as zero touch automation solutions is a key enabler for increasingly dense wireless networks and pervasive Internet of Things (IoT). To realize their objectives, most automation functionalities rely on the Minimization of Drive Test (MDT) reports. The MDT reports are used to generate inferences about network state and performance, thus dynamically change network parameters accordingly. However, the collection of MDT reports from commodity user devices, particularly low cost IoT devices, make them a vulnerable entry point to launch an adversarial attack on emerging deeply automated wireless networks. This adds a new dimension to the security threats in the IoT and cellular networks. Existing literature on IoT, SON or zero touch automation does not address this important problem. In this article, we investigate an impactful, first of its kind adversarial attack that can be launched by exploiting the malicious MDT reports from the compromised user equipment (UE). We highlight the detrimental repercussions of this attack on the performance of common network automation functions. We also propose a novel Malicious MDT Reports Identification framework (MRIF) as a countermeasure to detect and eliminate the malicious MDT reports using Machine Learning and verify it through a use case. Thus, the defense mechanism can provide the resilience and robustness for zero touch automation SON engines against the adversarial MDT attack.

In modern wireless communication systems, radio propagation modeling to estimate pathloss has always been a fundamental task in system design and optimization. The state-of-the-art empirical propagation models are based on measurements in specific environments and limited in their ability to capture idiosyncrasies of various propagation environments. To cope with this problem, ray-tracing based solutions are used in commercial planning tools, but they tend to be extremely time-consuming and expensive. We propose a Machine Learning (ML)-based model that leverages novel key predictors for estimating pathloss. By quantitatively evaluating the ability of various ML algorithms in terms of predictive, generalization and computational performance, our results show that Light Gradient Boosting Machine (LightGBM) algorithm overall outperforms others, even with sparse training data, by providing a 65% increase in prediction accuracy as compared to empirical models and 13x decrease in prediction time as compared to ray-tracing. To address the interpretability challenge that thwarts the adoption of most Machine Learning (ML)-based models, we perform extensive secondary analysis using SHapley Additive exPlanations (SHAP) method, yielding many practically useful insights that can be leveraged for intelligently tuning the network configuration, selective enrichment of training data in real networks and for building lighter ML-based propagation model to enable low-latency use-cases.

The key enablers for emerging cellular networks (such as densification, concurrent operation at multiple bands, and harnessing mmWave spectrum), give birth to a peculiar set of new network management challenges. One such key challenge is user mobility management. In this article, we identify key issues that render current mobility management paradigm inadequate for delivering the expected Quality of Experience (QoE) and resource efficiency in emerging and future cellular networks. Together, these challenges call for a paradigm shift in the way mobility is managed in cellular networks. We present an Advanced Mobility Management and Utilization Framework (A-MMUF) that can enable this paradigm shift by transforming mobility management from being a reactive to a proactive process. The core idea of A-MMUF is to build upon Mobility Prediction Models (MPMs) to predict various user mobility attributes and traffic patterns, such as next candidate cell for handover (HO), HO time, and future cell loads. These predictions are then leveraged to not only improve HO process for better QoE and less signaling overhead, but also to enable proactive automation to further maximize network performance (in terms of energy and spectrum efficiency). Understandable, however, the A-MMUF gains offered hinge on the MPMs accuracy. Those gains may vary, not only with underlying machine learning technique choice, but also with training data volume, variety and fidelity. We analyze the potential A-MMUF gains through three case studies: namely proactive HO, proactive Mobility Load Balancing (P-MLB), and proactive Energy Savings (P-ES). In addition to demonstrating significant gains in all three case studies, the results provide useful insights into the agility versus accuracy tradeoff – that can be leveraged for choosing optimal machine learning models for practical A-MMUF deployment.