1,967 research outputs found
Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks
Last year, IEEE 802.11 Extremely High Throughput Study Group (EHT Study
Group) was established to initiate discussions on new IEEE 802.11 features.
Coordinated control methods of the access points (APs) in the wireless local
area networks (WLANs) are discussed in EHT Study Group. The present study
proposes a deep reinforcement learning-based channel allocation scheme using
graph convolutional networks (GCNs). As a deep reinforcement learning method,
we use a well-known method double deep Q-network. In densely deployed WLANs,
the number of the available topologies of APs is extremely high, and thus we
extract the features of the topological structures based on GCNs. We apply GCNs
to a contention graph where APs within their carrier sensing ranges are
connected to extract the features of carrier sensing relationships.
Additionally, to improve the learning speed especially in an early stage of
learning, we employ a game theory-based method to collect the training data
independently of the neural network model. The simulation results indicate that
the proposed method can appropriately control the channels when compared to
extant methods
Resilient Autonomous Control of Distributed Multi-agent Systems in Contested Environments
An autonomous and resilient controller is proposed for leader-follower
multi-agent systems under uncertainties and cyber-physical attacks. The leader
is assumed non-autonomous with a nonzero control input, which allows changing
the team behavior or mission in response to environmental changes. A resilient
learning-based control protocol is presented to find optimal solutions to the
synchronization problem in the presence of attacks and system dynamic
uncertainties. An observer-based distributed H_infinity controller is first
designed to prevent propagating the effects of attacks on sensors and actuators
throughout the network, as well as to attenuate the effect of these attacks on
the compromised agent itself. Non-homogeneous game algebraic Riccati equations
are derived to solve the H_infinity optimal synchronization problem and
off-policy reinforcement learning is utilized to learn their solution without
requiring any knowledge of the agent's dynamics. A trust-confidence based
distributed control protocol is then proposed to mitigate attacks that hijack
the entire node and attacks on communication links. A confidence value is
defined for each agent based solely on its local evidence. The proposed
resilient reinforcement learning algorithm employs the confidence value of each
agent to indicate the trustworthiness of its own information and broadcast it
to its neighbors to put weights on the data they receive from it during and
after learning. If the confidence value of an agent is low, it employs a trust
mechanism to identify compromised agents and remove the data it receives from
them from the learning process. Simulation results are provided to show the
effectiveness of the proposed approach
Multiscale Markov Decision Problems: Compression, Solution, and Transfer Learning
Many problems in sequential decision making and stochastic control often have
natural multiscale structure: sub-tasks are assembled together to accomplish
complex goals. Systematically inferring and leveraging hierarchical structure,
particularly beyond a single level of abstraction, has remained a longstanding
challenge. We describe a fast multiscale procedure for repeatedly compressing,
or homogenizing, Markov decision processes (MDPs), wherein a hierarchy of
sub-problems at different scales is automatically determined. Coarsened MDPs
are themselves independent, deterministic MDPs, and may be solved using
existing algorithms. The multiscale representation delivered by this procedure
decouples sub-tasks from each other and can lead to substantial improvements in
convergence rates both locally within sub-problems and globally across
sub-problems, yielding significant computational savings. A second fundamental
aspect of this work is that these multiscale decompositions yield new transfer
opportunities across different problems, where solutions of sub-tasks at
different levels of the hierarchy may be amenable to transfer to new problems.
Localized transfer of policies and potential operators at arbitrary scales is
emphasized. Finally, we demonstrate compression and transfer in a collection of
illustrative domains, including examples involving discrete and continuous
statespaces.Comment: 86 pages, 15 figure
The edge cloud: A holistic view of communication, computation and caching
The evolution of communication networks shows a clear shift of focus from
just improving the communications aspects to enabling new important services,
from Industry 4.0 to automated driving, virtual/augmented reality, Internet of
Things (IoT), and so on. This trend is evident in the roadmap planned for the
deployment of the fifth generation (5G) communication networks. This ambitious
goal requires a paradigm shift towards a vision that looks at communication,
computation and caching (3C) resources as three components of a single holistic
system. The further step is to bring these 3C resources closer to the mobile
user, at the edge of the network, to enable very low latency and high
reliability services. The scope of this chapter is to show that signal
processing techniques can play a key role in this new vision. In particular, we
motivate the joint optimization of 3C resources. Then we show how graph-based
representations can play a key role in building effective learning methods and
devising innovative resource allocation techniques.Comment: to appear in the book "Cooperative and Graph Signal Pocessing:
Principles and Applications", P. Djuric and C. Richard Eds., Academic Press,
Elsevier, 201
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