IoT-related Attack Platforms
Start Date
23-10-2020 2:30 PM
End Date
23-10-2020 3:00 PM
Location
Zoom Session 2 (SunTrust Track)
Abstract
We study the jamming resistant mobile device communication problem under the multiple resource constraint model in 5G networks. Given a set of communication links, assume that the complete channel state information of each link is unknown subject to jamming resistant constraints, but we can estimate it by exploiting the memory along with channel state feedback. Assume time is divided into time-slots. The objective is to select links under the multiple resource constraint model to transmit sequentially to maximize the jamming resistant throughput over an infinite time horizon. Existing work simply assumes a single resource constraint or an even simpler model. To this end, we apply the framework of restless multi-armed bandit and develop a fast and simple approximation algorithm. We prove that the proposed algorithm can achieve good approximation bounds. We evaluate and compare our work with a greedy method adapted from the well known Whittle’s index policy, and show that our algorithm outperfoms the greedy method in terms of average throughput.
IoT-related Attack Platforms
Zoom Session 2 (SunTrust Track)
We study the jamming resistant mobile device communication problem under the multiple resource constraint model in 5G networks. Given a set of communication links, assume that the complete channel state information of each link is unknown subject to jamming resistant constraints, but we can estimate it by exploiting the memory along with channel state feedback. Assume time is divided into time-slots. The objective is to select links under the multiple resource constraint model to transmit sequentially to maximize the jamming resistant throughput over an infinite time horizon. Existing work simply assumes a single resource constraint or an even simpler model. To this end, we apply the framework of restless multi-armed bandit and develop a fast and simple approximation algorithm. We prove that the proposed algorithm can achieve good approximation bounds. We evaluate and compare our work with a greedy method adapted from the well known Whittle’s index policy, and show that our algorithm outperfoms the greedy method in terms of average throughput.