An Online Context-Aware Machine Learning Algorithm for 5G mmWave Vehicular Communications

Sim, Gek Hong; Klos, Sabrina; Asadi, Arash; Klein, Anja; Hollick, Matthias

doi:10.1109/tnet.2018.2869244

Cited by 133 publications

(116 citation statements)

References 25 publications

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“…Context awareness is also a technological driver for M2M (machine to machine) and IoT, ubiquitous computing and event-driven computing environments. An online context-aware ML algorithm for 5G millimeter-wave vehicular communications was proposed in [192]. The algorithm sourced for sparse user location information, aggregated the received data and was thus able to learn and adapt to the environment.…”

Section: B Context Awareness For Resiliency In MLmentioning

confidence: 99%

Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS

Olowononi

Rawat

Liu

2021

IEEE Commun. Surv. Tutorials

143

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Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and information or cyber worlds. Their deployment in critical infrastructure have demonstrated a potential to transform the world. However, harnessing this potential is limited by their critical nature and the far reaching effects of cyber attacks on human, infrastructure and the environment. An attraction for cyber concerns in CPS rises from the process of sending information from sensors to actuators over the wireless communication medium, thereby widening the attack surface. Traditionally, CPS security has been investigated from the perspective of preventing intruders from gaining access to the system using cryptography and other access control techniques. Most research work have therefore focused on the detection of attacks in CPS. However, in a world of increasing adversaries, it is becoming more difficult to totally prevent CPS from adversarial attacks, hence the need to focus on making CPS resilient. Resilient CPS are designed to withstand disruptions and remain functional despite the operation of adversaries. One of the dominant methodologies explored for building resilient CPS is dependent on machine learning (ML) algorithms. However, rising from recent research in adversarial ML, we posit that ML algorithms for securing CPS must themselves be resilient. This paper is therefore aimed at comprehensively surveying the interactions between resilient CPS using ML and resilient ML when applied in CPS. The paper concludes with a number of research trends and promising future research directions. Furthermore, with this paper, readers can have a thorough understanding of recent advances on ML-based security and securing ML for CPS and countermeasures, as well as research trends in this active research area.

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Section: B Context Awareness For Resiliency In MLmentioning

confidence: 99%

Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS

Olowononi

Rawat

Liu

2021

IEEE Commun. Surv. Tutorials

143

View full text Add to dashboard Cite

show abstract

“…This makes it possible to reliably predict future beam steering angles from past information. Using machine learning, even complex scenarios can be predicted [34], and blockage when the users moves behind obstacles, such as a building, vegetation, vehicles, or other users, can be anticipated to perform a handover prior to link disruption. In summary, with these techniques, mmWave links can be maintained even in highly dynamic scenarios.…”

Section: B the Impact Of Dynamicsmentioning

confidence: 99%

Scaling Millimeter-Wave Networks to Dense Deployments and Dynamic Environments

et al. 2019

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Millimeter wave (mmWave) communications have emerged as one of the most promising options to vastly increase wireless data rates due to the high bandwidth they offer. Given the high path loss at mmWave frequencies, such systems require directional antennas to achieve a good communication range. The communicating devices thus need to align the beam directions of their mmWave antennas. Due to the high penetration loss, the paths between the antennas also need to be free of blocking obstacles. This makes efficient and reliable operation of mmWave networks in dynamic environments very challenging. At the same time, the directionality reduces interference and allows to scale these networks to much higher access point and device densities. In this article, we discuss the above challenges and present techniques that allow mmWave networks to scale to high-density deployments, to adapt to dynamic and mobile environments, and to consistently achieve high data rates. This includes learning the environment to find different propagation paths, reacting timely to channel impairments such as blockage, and integrating mmWave networks with networks operating at lower-frequency for robustness. A key ingredient to enable these forms of adaptivity is the use of location information. Such mechanisms then turn a collection of very-high-speed but brittle mmWave links into an efficient, low-latency, and reliable network.

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“…The authors in [9] proposed a fast, online machine learning algorithm in fifth-generation (5G) vehicle-to-everything communication environments that verified the accuracy of the proposed algorithm based on data from Google Maps. The authors in [10] proposed a Q-learning algorithm based on a distributed reinforcement learning framework to solve the cooperative retransmission in a wireless network.…”

mentioning

confidence: 93%

An Improved Sarsa($\lambda$ ) Reinforcement Learning Algorithm for Wireless Communication Systems

et al. 2019

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In this article, we provide a novel improved model-free temporal-difference control algorithm, namely, Expected Sarsa(λ), using the average value as an update target and introducing eligibility traces in wireless communication networks. In particular, we construct the update target using the average action value of all possible successive actions, and apply eligibility traces to record the historical access of every state action pair, which greatly improve the model's convergence property and learning efficiency. Numerical results demonstrate that the proposed algorithm has the advantage of high learning efficiency and a higher learning-rate tolerance range than Q Learning, Sarsa, Expected Sarsa, and Sarsa(λ) in the tabular case of a finite Markov decision process, thereby providing an efficient solution for the study and design wireless communication networks. This provides an efficient and effective solution to design further artificial intelligent communication networks. INDEX TERMS Model-free reinforcement learning, Sarsa, Q learning, eligibility traces. I. INTRODUCTION

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An Online Context-Aware Machine Learning Algorithm for 5G mmWave Vehicular Communications

Cited by 133 publications

References 25 publications

Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS

Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS

Scaling Millimeter-Wave Networks to Dense Deployments and Dynamic Environments

An Improved Sarsa($\lambda$ ) Reinforcement Learning Algorithm for Wireless Communication Systems

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