Collaborative Research: CNS: Medium: Scalable Learning from Distributed Data for Wireless Network Management

Synopsis

As we transition to 5G and beyond, we observe both the emergence of new applications (e.g., Internet of Things, virtual and augmented reality) as well as an increased attack surface exploitable by malicious behaviors (e.g., as evidenced by the Mirai botnet). Future wireless networks will need better control and management; e.g., allocating spectrum, and in identifying and isolating distributed attacks.

In this context, data-driven and machine-learning (ML) based approaches offer tremendous promise to simplify and enhance these management tasks. However, this promise is especially hard to realize in wireless networks, as the data is decentralized (e.g., available at geographically apart base stations). In spite of advances, the backhaul network is fundamentally constrained. Canonical approaches (e.g., sampling or sending raw data) induce fundamental undesirable tradeoffs between scalability and fidelity.

Our focus in this proposal is on developing decentralized telemetry and analytics capabilities that provide scalable and high-fidelity inputs for wireless network management, rather than the control capabilities. The driving principle in our work is to look for algorithmic opportunities minimize the amount of backhaul traffic to send the appropriate telemetry information. The fundamental challenge lies in supporting rich, diverse tasks that often need network-wide visibility, while still keeping the overhead low. To this end, we will develop the algorithmic, machine learning, and network systems foundations to systematically balance these tradeoffs.

The proposed work will be accomplished through the following three convergent tasks and can facilitate a variety of applications such as detecting new patterns, and sophisticated attacks along three tasks:

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