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Register a new userAn Overview of Low latency for Wireless Communications: an Evolutionary Perspective
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Added by
Xin Fan
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Ultra-low latency supported by the fifth generation (5G) give impetus to the prosperity of many wireless network applications, such as autonomous driving, robotics, telepresence, virtual reality and so on. Ultra-low latency is not achieved in a moment, but requires long-term evolution of network structure and key enabling communication technologies. In this paper, we provide an evolutionary overview of low latency in mobile communication systems, including two different evolutionary perspectives: 1) network architecture; 2) physical layer air interface technologies. We firstly describe in detail the evolution of communication network architecture from the second generation (2G) to 5G, highlighting the key points reducing latency. Moreover, we review the evolution of key enabling technologies in the physical layer from 2G to 5G, which is also aimed at reducing latency. We also discussed the challenges and future research directions for low latency in network architecture and physical layer.
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Wireless Mesh Networks (WMNs) have been extensively studied for nearly two decades as one of the most promising candidates expected to power the high bandwidth, high coverage wireless networks of the future. However, consumer demand for such networks has only recently caught up, rendering efforts at optimizing WMNs to support high capacities and offer high QoS, while being secure and fault tolerant, more important than ever. To this end, a recent trend has been the application of Machine Learning (ML) to solve various design and management tasks related to WMNs. In this work, we discuss key ML techniques and analyze how past efforts have applied them in WMNs, while noting some existing issues and suggesting potential solutions. We also provide directions on how ML could advance future research and examine recent developments in the field.
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In January 2019, DeepMind revealed AlphaStar to the world-the first artificial intelligence (AI) system to beat a professional player at the game of StarCraft II-representing a milestone in the progress of AI. AlphaStar draws on many areas of AI research, including deep learning, reinforcement learning, game theory, and evolutionary computation (EC). In this paper we analyze AlphaStar primarily through the lens of EC, presenting a new look at the system and relating it to many concepts in the field. We highlight some of its most interesting aspects-the use of Lamarckian evolution, competitive co-evolution, and quality diversity. In doing so, we hope to provide a bridge between the wider EC community and one of the most significant AI systems developed in recent times.
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