Addressing training data sparsity and interpretability challenges in AI based cellular networks

To meet the diverse and stringent communication requirements for emerging networks use cases, zero-touch arti cial intelligence (AI) based deep automation in cellular networks is envisioned. However, the full potential of AI in cellular networks remains hindered by two key challenges: (i) training data is not as freely available in cellular networks as in other fields where AI has made a profound impact and (ii) current AI models tend to have black box behavior making operators reluctant to entrust the operation of multibillion mission critical networks to a black box AI engine, which allow little insights and discovery of relationships between the configuration and optimization parameters and key performance indicators. This dissertation systematically addresses and proposes solutions to these two key problems faced by emerging networks. A framework towards addressing the training data sparsity challenge in cellular networks is developed, that can assist network operators and researchers in choosing the optimal data enrichment technique for different network scenarios, based on the available information. The framework encompasses classical interpolation techniques, like inverse distance weighted and kriging to more advanced ML-based methods, like transfer learning and generative adversarial networks, several new techniques, such as matrix completion theory and leveraging different types of network geometries, and simulators and testbeds, among others. The proposed framework will lead to more accurate ML models, that rely on sufficient amount of representative training data. Moreover, solutions are proposed to address the data sparsity challenge specifically in Minimization of drive test (MDT) based automation approaches. MDT allows coverage to be estimated at the base station by exploiting measurement reports gathered by the user equipment without the need for drive tests. Thus, MDT is a key enabling feature for data and artificial intelligence driven autonomous operation and optimization in current and emerging cellular networks. However, to date, the utility of MDT feature remains thwarted by issues such as sparsity of user reports and user positioning inaccuracy. For the first time, this dissertation reveals the existence of an optimal bin width for coverage estimation in the presence of inaccurate user positioning, scarcity of user reports and quantization error. The presented framework can enable network operators to configure the bin size for given positioning accuracy and user density that results in the most accurate MDT based coverage estimation. The lack of interpretability in AI-enabled networks is addressed by proposing a first of its kind novel neural network architecture leveraging analytical modeling, domain knowledge, big data and machine learning to turn black box machine learning models into more interpretable models. The proposed approach combines analytical modeling and domain knowledge to custom design machine learning models with the aim of moving towards interpretable machine learning models, that not only require a lesser training time, but can also deal with issues such as sparsity of training data and determination of model hyperparameters. The approach is tested using both simulated data and real data and results show that the proposed approach outperforms existing mathematical models, while also remaining interpretable when compared with black-box ML models. Thus, the proposed approach can be used to derive better mathematical models of complex systems. The findings from this dissertation can help solve the challenges in emerging AI-based cellular networks and thus aid in their design, operation and optimization

COVERAGE ANALYSIS OF UNMANNED AERIAL VEHICLES BASED NETWORK

Unmanned Aerial Vehicle (UAV) based cellular deployment research has recently gained significant attention due to its ability to overcome certain limitations of both terrestrial and satellite networks. However, most studies on the topic analyze key UAV deployment parameters, such as coverage radius and altitude while omitting or over-simplifying an important piece of UAV deployment puzzle, i.e., effect of a realistic antenna pattern. This thesis addresses the UAV deployment problem while using a realistic 3D directional antenna model and analyses the new tradeoffs between key UAV deployment parameters that emerge only when a realistic antenna model is used. The focus of this study is to identify and analyze the various UAV deployment design options after adding a new dimension of beamwidth to the UAV deployment design space. The extensive numerical results, cross validated with simulations reveal many new insights that can be crucial in practical deployments but cannot be examined with existing simplified models. Through quantitative comparison of coverage to deployment parameter sensitivity, it is shown how beamwidth may be a more feasible parameter to design optimal coverage as compared to height optimization proposed in prior literature. Moreover, the significance of optimizing both antenna beamwidth and UAV altitude in tandem with each other, rather than independently is established. Extending analysis to multiple UAVs, a hexagonal packing scheme to optimize number of UAVs needed to cover a given area is proposed. The results are compared with what was obtained by circle packing theory in prior literature. The comparison uncovers several advantages the proposed approach offers as compared to circle packing based UAV deployment

Can Machine Learning Be Used to Recognize and Diagnose Coughs?

Emerging wireless technologies, such as 5G and beyond, are bringing new use cases to the forefront, one of the most prominent being machine learning empowered health care. One of the notable modern medical concerns that impose an immense worldwide health burden are respiratory infections. Since cough is an essential symptom of many respiratory infections, an automated system to screen for respiratory diseases based on raw cough data would have a multitude of beneficial research and medical applications. In literature, machine learning has already been successfully used to detect cough events in controlled environments. In this paper, we present a low complexity, automated recognition and diagnostic tool for screening respiratory infections that utilizes Convolutional Neural Networks (CNNs) to detect cough within environment audio and diagnose three potential illnesses (i.e., bronchitis, bronchiolitis and pertussis) based on their unique cough audio features. Both proposed detection and diagnosis models achieve an accuracy of over 89%, while also remaining computationally efficient. Results show that the proposed system is successfully able to detect and separate cough events from background noise. Moreover, the proposed single diagnosis model is capable of distinguishing between different illnesses without the need of separate models.Comment: Accepted in IEEE International Conference on E-Health and Bioengineering - EHB 202

Towards addressing training data scarcity challenge in emerging radio access networks: a survey and framework

The future of cellular networks is contingent on artificial intelligence (AI) based automation, particularly for radio access network (RAN) operation, optimization, and troubleshooting. To achieve such zero-touch automation, a myriad of AI-based solutions are being proposed in literature to leverage AI for modeling and optimizing network behavior to achieve the zero-touch automation goal. However, to work reliably, AI based automation, requires a deluge of training data. Consequently, the success of the proposed AI solutions is limited by a fundamental challenge faced by cellular network research community: scarcity of the training data. In this paper, we present an extensive review of classic and emerging techniques to address this challenge. We first identify the common data types in RAN and their known use-cases. We then present a taxonomized survey of techniques used in literature to address training data scarcity for various data types. This is followed by a framework to address the training data scarcity. The proposed framework builds on available information and combination of techniques including interpolation, domain-knowledge based, generative adversarial neural networks, transfer learning, autoencoders, fewshot learning, simulators and testbeds. Potential new techniques to enrich scarce data in cellular networks are also proposed, such as by matrix completion theory, and domain knowledge-based techniques leveraging different types of network geometries and network parameters. In addition, an overview of state-of-the art simulators and testbeds is also presented to make readers aware of current and emerging platforms to access real data in order to overcome the data scarcity challenge. The extensive survey of training data scarcity addressing techniques combined with proposed framework to select a suitable technique for given type of data, can assist researchers and network operators in choosing the appropriate methods to overcome the data scarcity challenge in leveraging AI to radio access network automation