2 research outputs found
Configuration Learning in Underwater Optical Links
A new research problem named configuration learning is described in this
work. A novel algorithm is proposed to address the configuration learning
problem. The configuration learning problem is defined to be the optimization
of the Machine Learning (ML) classifier to maximize the ML performance metric
optimizing the transmitter configuration in the signal processing/communication
systems. Specifically, this configuration learning problem is investigated in
an underwater optical communication system with signal processing performance
metric of the physical-layer communication throughput. A novel algorithm is
proposed to perform the configuration learning by alternating optimization of
key design parameters and switching between several Recurrent Neural Network
(RNN) classifiers dependant on the learning objective. The proposed ML
algorithm is validated with the datasets of an underwater optical communication
system and is compared with competing ML algorithms. Performance results
indicate that the proposal outperforms the competing algorithms for binary and
multi-class configuration learning in underwater optical communication
datasets. The proposed configuration learning framework can be further
investigated and applied to a broad range of topics in signal processing and
communications
Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges
Internet-of-Things (IoT) refers to a massively heterogeneous network formed
through smart devices connected to the Internet. In the wake of disruptive IoT
with a huge amount and variety of data, Machine Learning (ML) and Deep Learning
(DL) mechanisms will play a pivotal role to bring intelligence to the IoT
networks. Among other aspects, ML and DL can play an essential role in
addressing the challenges of resource management in large-scale IoT networks.
In this article, we conduct a systematic and in-depth survey of the ML- and
DL-based resource management mechanisms in cellular wireless and IoT networks.
We start with the challenges of resource management in cellular IoT and
low-power IoT networks, review the traditional resource management mechanisms
for IoT networks, and motivate the use of ML and DL techniques for resource
management in these networks. Then, we provide a comprehensive survey of the
existing ML- and DL-based resource allocation techniques in wireless IoT
networks and also techniques specifically designed for HetNets, MIMO and D2D
communications, and NOMA networks. To this end, we also identify the future
research directions in using ML and DL for resource allocation and management
in IoT networks.Comment: 21 pages, 3 figure