Page 18 - ITU Journal, Future and evolving technologies - Volume 1 (2020), Issue 1, Inaugural issue

ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1

A blueprint for effective pandemic mitigation

Pages 89-101
Rahul Singh, Wenbo Ren, Fang Liu, Dong Xuan, Zhiqiang Lin, Ness B. Shroff

Traditional methods for mitigating pandemics employ a dual strategy of contact tracing plus testing
combined with quarantining and isolation. The contact tracing aspect is usually done via manual (human)
contact tracers, which are labor-intensive and expensive. In many large-scale pandemics (e.g., COVID-
19), testing capacity is resource limited, and current myopic testing strategies are resource wasteful. To
address these challenges, in this work, we provide a blueprint on how to contain the spread of a
pandemic by leveraging wireless technologies and advances in sequential learning for efficiently using
testing resources in order to mitigate the spread of a large-scale pandemic. We study how different
wireless technologies could be leveraged to improve contact tracing and reduce the probabilities of
detection and false alarms. The idea is to integrate different streams of data in order to create a
susceptibility graph whose nodes correspond to an individual and whose links correspond to spreading
probabilities. We then show how to develop efficient sequential learning based algorithms in order to
minimize the spread of the virus infection. In particular, we show that current contact tracing plus testing
strategies that are aimed at identifying (and testing) individuals with the highest probability of infection
are inefficient. Rather, we argue that in a resource constrained testing environment, it is instead better
to test those individuals whose expected impact on virus spread is the highest. We rigorously formulate
the resource constrained testing problem as a sequential learning problem and provide efficient
algorithms to solve it. We also provide numerical results that show the efficacy of our testing strategy.

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Machine learning-assisted cross-slice radio resource optimization:
Implementation framework and algorithmic solution

Pages 103-120
Ramon Ferrús, Jordi Pérez-Romero, Oriol Sallent, Irene Vilà, Ramon Agustí

Network slicing is a central feature in 5G and beyond systems to allow operators to customize their
networks for different applications and customers. With network slicing, different logical networks, i.e.
network slices, with specific functional and performance requirements can be created over the same
physical network. A key challenge associated with the exploitation of the network slicing feature is how
to efficiently allocate underlying network resources, especially radio resources, to cope with the spatio-
temporal traffic variability while ensuring that network slices can be provisioned and assured within
the boundaries of Service Level Agreements / Service Level Specifications (SLAs/SLSs) with
customers. In this field, the use of artificial intelligence, and, specifically, Machine Learning (ML)
techniques, has arisen as a promising approach to cater for the complexity of resource allocation
optimization among network slices. This paper tackles the description of a feasible implementation
framework for deploying ML-assisted solutions for cross-slice radio resource optimization that builds
upon the work conducted by 3GPP and O-RAN Alliance. On this basis, the paper also describes and
evaluates an ML-assisted solution that uses a Multi-Agent Reinforcement Learning (MARL) approach
based on the Deep Q-Network (DQN) technique and fits within the presented implementation
framework.
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