2,766 research outputs found
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Data-driven Integrated Sensing and Communication: Recent Advances, Challenges, and Future Prospects
Integrated Sensing and Communication (ISAC), combined with data-driven
approaches, has emerged as a highly significant field, garnering considerable
attention from academia and industry. Its potential to enable wide-scale
applications in the future sixth-generation (6G) networks has led to extensive
recent research efforts. Machine learning (ML) techniques, including
-nearest neighbors (KNN), support vector machines (SVM), deep learning (DL)
architectures, and reinforcement learning (RL) algorithms, have been deployed
to address various design aspects of ISAC and its diverse applications.
Therefore, this paper aims to explore integrating various ML techniques into
ISAC systems, covering various applications. These applications span
intelligent vehicular networks, encompassing unmanned aerial vehicles (UAVs)
and autonomous cars, as well as radar applications, localization and tracking,
millimeter wave (mmWave) and Terahertz (THz) communication, and beamforming.
The contributions of this paper lie in its comprehensive survey of ML-based
works in the ISAC domain and its identification of challenges and future
research directions. By synthesizing the existing knowledge and proposing new
research avenues, this survey serves as a valuable resource for researchers,
practitioners, and stakeholders involved in advancing the capabilities of ISAC
systems in the context of 6G networks.Comment: ISAC-ML surve
Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions
Technology solutions must effectively balance economic growth, social equity,
and environmental integrity to achieve a sustainable society. Notably, although
the Internet of Things (IoT) paradigm constitutes a key sustainability enabler,
critical issues such as the increasing maintenance operations, energy
consumption, and manufacturing/disposal of IoT devices have long-term negative
economic, societal, and environmental impacts and must be efficiently
addressed. This calls for self-sustainable IoT ecosystems requiring minimal
external resources and intervention, effectively utilizing renewable energy
sources, and recycling materials whenever possible, thus encompassing energy
sustainability. In this work, we focus on energy-sustainable IoT during the
operation phase, although our discussions sometimes extend to other
sustainability aspects and IoT lifecycle phases. Specifically, we provide a
fresh look at energy-sustainable IoT and identify energy provision, transfer,
and energy efficiency as the three main energy-related processes whose
harmonious coexistence pushes toward realizing self-sustainable IoT systems.
Their main related technologies, recent advances, challenges, and research
directions are also discussed. Moreover, we overview relevant performance
metrics to assess the energy-sustainability potential of a certain technique,
technology, device, or network and list some target values for the next
generation of wireless systems. Overall, this paper offers insights that are
valuable for advancing sustainability goals for present and future generations.Comment: 25 figures, 12 tables, submitted to IEEE Open Journal of the
Communications Societ
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