1,125 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
Compressive Sensing-Based Grant-Free Massive Access for 6G Massive Communication
The advent of the sixth-generation (6G) of wireless communications has given
rise to the necessity to connect vast quantities of heterogeneous wireless
devices, which requires advanced system capabilities far beyond existing
network architectures. In particular, such massive communication has been
recognized as a prime driver that can empower the 6G vision of future
ubiquitous connectivity, supporting Internet of Human-Machine-Things for which
massive access is critical. This paper surveys the most recent advances toward
massive access in both academic and industry communities, focusing primarily on
the promising compressive sensing-based grant-free massive access paradigm. We
first specify the limitations of existing random access schemes and reveal that
the practical implementation of massive communication relies on a dramatically
different random access paradigm from the current ones mainly designed for
human-centric communications. Then, a compressive sensing-based grant-free
massive access roadmap is presented, where the evolutions from single-antenna
to large-scale antenna array-based base stations, from single-station to
cooperative massive multiple-input multiple-output systems, and from unsourced
to sourced random access scenarios are detailed. Finally, we discuss the key
challenges and open issues to shed light on the potential future research
directions of grant-free massive access.Comment: Accepted by IEEE IoT Journa
RIS-Aided Cell-Free Massive MIMO Systems for 6G: Fundamentals, System Design, and Applications
An introduction of intelligent interconnectivity for people and things has
posed higher demands and more challenges for sixth-generation (6G) networks,
such as high spectral efficiency and energy efficiency, ultra-low latency, and
ultra-high reliability. Cell-free (CF) massive multiple-input multiple-output
(mMIMO) and reconfigurable intelligent surface (RIS), also called intelligent
reflecting surface (IRS), are two promising technologies for coping with these
unprecedented demands. Given their distinct capabilities, integrating the two
technologies to further enhance wireless network performances has received
great research and development attention. In this paper, we provide a
comprehensive survey of research on RIS-aided CF mMIMO wireless communication
systems. We first introduce system models focusing on system architecture and
application scenarios, channel models, and communication protocols.
Subsequently, we summarize the relevant studies on system operation and
resource allocation, providing in-depth analyses and discussions. Following
this, we present practical challenges faced by RIS-aided CF mMIMO systems,
particularly those introduced by RIS, such as hardware impairments and
electromagnetic interference. We summarize corresponding analyses and solutions
to further facilitate the implementation of RIS-aided CF mMIMO systems.
Furthermore, we explore an interplay between RIS-aided CF mMIMO and other
emerging 6G technologies, such as next-generation multiple-access (NGMA),
simultaneous wireless information and power transfer (SWIPT), and millimeter
wave (mmWave). Finally, we outline several research directions for future
RIS-aided CF mMIMO systems.Comment: 30 pages, 15 figure
A Survey of Beam Management for mmWave and THz Communications Towards 6G
Communication in millimeter wave (mmWave) and even terahertz (THz) frequency
bands is ushering in a new era of wireless communications. Beam management,
namely initial access and beam tracking, has been recognized as an essential
technique to ensure robust mmWave/THz communications, especially for mobile
scenarios. However, narrow beams at higher carrier frequency lead to huge beam
measurement overhead, which has a negative impact on beam acquisition and
tracking. In addition, the beam management process is further complicated by
the fluctuation of mmWave/THz channels, the random movement patterns of users,
and the dynamic changes in the environment. For mmWave and THz communications
toward 6G, we have witnessed a substantial increase in research and industrial
attention on artificial intelligence (AI), reconfigurable intelligent surface
(RIS), and integrated sensing and communications (ISAC). The introduction of
these enabling technologies presents both open opportunities and unique
challenges for beam management. In this paper, we present a comprehensive
survey on mmWave and THz beam management. Further, we give some insights on
technical challenges and future research directions in this promising area.Comment: accepted by IEEE Communications Surveys & Tutorial
Signal Processing and Learning for Next Generation Multiple Access in 6G
Wireless communication systems to date primarily rely on the orthogonality of
resources to facilitate the design and implementation, from user access to data
transmission. Emerging applications and scenarios in the sixth generation (6G)
wireless systems will require massive connectivity and transmission of a deluge
of data, which calls for more flexibility in the design concept that goes
beyond orthogonality. Furthermore, recent advances in signal processing and
learning have attracted considerable attention, as they provide promising
approaches to various complex and previously intractable problems of signal
processing in many fields. This article provides an overview of research
efforts to date in the field of signal processing and learning for
next-generation multiple access, with an emphasis on massive random access and
non-orthogonal multiple access. The promising interplay with new technologies
and the challenges in learning-based NGMA are discussed
- …