Learning Decentralized Wireless Resource Allocations With Graph Neural Networks

Decentralized Learning in Wireless Sensor Networks

In this paper we use a reinforcement learning algorithm with the aim to increase the autonomous lifetime of a Wireless Sensor Network (WSN) and decrease latency in a decentralized manner. WSNs are collections of sensor nodes that gather environmental data, where the main challenges are the limited power supply of nodes and the need for decentralized control. To overcome these challenges, we mak...

Few-Shot Learning with Graph Neural Networks

We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not. By assimilating generic message-passing inference algorithms with their neural-network counterparts, we define a graph neural network architecture that generalizes several of the recentl...

A model for randomized resource allocation in decentralized wireless networks

In this paper, we consider a decentralized wireless communication network with a fixed number u of frequency sub-bands to be shared among N transmitter-receiver pairs. It is assumed that the number of active users is a random variable with a given probability mass function. Moreover, users are unaware of each other’s codebooks and hence, no multiuser detection is possible. We propose a randomiz...

Decentralized Interaction Estimators in Large Scale Power Systems with Neural Networks.

Deep Neural Networks for Learning Graph Representations

In this paper, we propose a novel model for learning graph representations, which generates a low-dimensional vector representation for each vertex by capturing the graph structural information. Different from other previous research efforts, we adopt a random surfing model to capture graph structural information directly, instead of using the samplingbased method for generating linear sequence...