36,980 research outputs found
Learning Autonomy in Management of Wireless Random Networks
This paper presents a machine learning strategy that tackles a distributed
optimization task in a wireless network with an arbitrary number of randomly
interconnected nodes. Individual nodes decide their optimal states with
distributed coordination among other nodes through randomly varying backhaul
links. This poses a technical challenge in distributed universal optimization
policy robust to a random topology of the wireless network, which has not been
properly addressed by conventional deep neural networks (DNNs) with rigid
structural configurations. We develop a flexible DNN formalism termed
distributed message-passing neural network (DMPNN) with forward and backward
computations independent of the network topology. A key enabler of this
approach is an iterative message-sharing strategy through arbitrarily connected
backhaul links. The DMPNN provides a convergent solution for iterative
coordination by learning numerous random backhaul interactions. The DMPNN is
investigated for various configurations of the power control in wireless
networks, and intensive numerical results prove its universality and viability
over conventional optimization and DNN approaches.Comment: to appear in IEEE TW
Defeating Proactive Jammers Using Deep Reinforcement Learning for Resource-Constrained IoT Networks
Traditional anti-jamming techniques like spread spectrum, adaptive power/rate
control, and cognitive radio, have demonstrated effectiveness in mitigating
jamming attacks. However, their robustness against the growing complexity of
internet-of-thing (IoT) networks and diverse jamming attacks is still limited.
To address these challenges, machine learning (ML)-based techniques have
emerged as promising solutions. By offering adaptive and intelligent
anti-jamming capabilities, ML-based approaches can effectively adapt to dynamic
attack scenarios and overcome the limitations of traditional methods. In this
paper, we propose a deep reinforcement learning (DRL)-based approach that
utilizes state input from realistic wireless network interface cards. We train
five different variants of deep Q-network (DQN) agents to mitigate the effects
of jamming with the aim of identifying the most sample-efficient, lightweight,
robust, and least complex agent that is tailored for power-constrained devices.
The simulation results demonstrate the effectiveness of the proposed DRL-based
anti-jamming approach against proactive jammers, regardless of their jamming
strategy which eliminates the need for a pattern recognition or jamming
strategy detection step. Our findings present a promising solution for securing
IoT networks against jamming attacks and highlights substantial opportunities
for continued investigation and advancement within this field
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
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