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Found 8 papers, 0 papers with code
Top-k Multi-Armed Bandit Learning for Content Dissemination in Swarms of Micro-UAVs
The paper proposes a decentralized Top-k Multi-Armed Bandit (Top-k MAB) learning approach for UAV caching decisions, accommodating geotemporal disparities in content popularity and diverse content demands.
Unsupervised Speaker Diarization in Distributed IoT Networks Using Federated Learning
In this new approach, speaker change detection is biased around detected quasi-silences, which reduces the severity of the trade-off between the missed detection and false detection rates.
Multi-Armed Bandit Learning for Content Provisioning in Network of UAVs
Functional verification and performance evaluation of the proposed caching framework is done for a wide range of network size, UAV distribution, and content popularity.
Multi-armed Bandit Learning for TDMA Transmission Slot Scheduling and Defragmentation for Improved Bandwidth Usage
Moreover, the nodes learn to self-adjust their transmission schedules using a novel DDSB framework in order to reduce bandwidth usage.
Reinforcement Learning for Protocol Synthesis in Resource-Constrained Wireless Sensor and IoT Networks
It then introduces a novel learning based protocol synthesis framework that addresses specific difficulties and limitations in medium access for both random access and time slotted networks.
Medium Access using Distributed Reinforcement Learning for IoTs with Low-Complexity Wireless Transceivers
This paper proposes a distributed Reinforcement Learning (RL) based framework that can be used for synthesizing MAC layer wireless protocols in IoT networks with low-complexity wireless transceivers.
Towards Multi-agent Reinforcement Learning for Wireless Network Protocol Synthesis
Finally, it is also shown that the online learning feature of reinforcement learning is able to make the protocol adapt to time-varying loading conditions.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
A Natural Language-Inspired Multi-label Video Streaming Traffic Classification Method Based on Deep Neural Networks
This paper presents a deep-learning based traffic classification method for identifying multiple streaming video sources at the same time within an encrypted tunnel.
