751 research outputs found
Joint Source-Channel Coding for Semantics-Aware Grant-Free Radio Access in IoT Fog Networks
A fog-radio access network (F-RAN) architecture is studied for an
Internet-of-Things (IoT) system in which wireless sensors monitor a number of
multi-valued events and transmit in the uplink using grant-free random access
to multiple edge nodes (ENs). Each EN is connected to a central processor (CP)
via a finite-capacity fronthaul link. In contrast to conventional
information-agnostic protocols based on separate source-channel (SSC) coding,
where each device uses a separate codebook, this paper considers an
information-centric approach based on joint source-channel (JSC) coding via a
non-orthogonal generalization of type-based multiple access (TBMA). By
leveraging the semantics of the observed signals, all sensors measuring the
same event share the same codebook (with non-orthogonal codewords), and all
such sensors making the same local estimate of the event transmit the same
codeword. The F-RAN architecture directly detects the events values without
first performing individual decoding for each device. Cloud and edge detection
schemes based on Bayesian message passing are designed and trade-offs between
cloud and edge processing are assessed.Comment: submitted for publicatio
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
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
Information Bottleneck-Inspired Type Based Multiple Access for Remote Estimation in IoT Systems
Type-based multiple access (TBMA) is a semantics-aware multiple access
protocol for remote inference. In TBMA, codewords are reused across
transmitting sensors, with each codeword being assigned to a different
observation value. Existing TBMA protocols are based on fixed shared codebooks
and on conventional maximum-likelihood or Bayesian decoders, which require
knowledge of the distributions of observations and channels. In this letter, we
propose a novel design principle for TBMA based on the information bottleneck
(IB). In the proposed IB-TBMA protocol, the shared codebook is jointly
optimized with a decoder based on artificial neural networks (ANNs), so as to
adapt to source, observations, and channel statistics based on data only. We
also introduce the Compressed IB-TBMA (CIB-TBMA) protocol, which improves
IB-TBMA by enabling a reduction in the number of codewords via an IB-inspired
clustering phase. Numerical results demonstrate the importance of a joint
design of codebook and neural decoder, and validate the benefits of codebook
compression.Comment: 5 pages, 3 figures, accepted by IEEE Signal Processing Letters (SPL
Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G
The next wave of wireless technologies is proliferating in connecting things
among themselves as well as to humans. In the era of the Internet of things
(IoT), billions of sensors, machines, vehicles, drones, and robots will be
connected, making the world around us smarter. The IoT will encompass devices
that must wirelessly communicate a diverse set of data gathered from the
environment for myriad new applications. The ultimate goal is to extract
insights from this data and develop solutions that improve quality of life and
generate new revenue. Providing large-scale, long-lasting, reliable, and near
real-time connectivity is the major challenge in enabling a smart connected
world. This paper provides a comprehensive survey on existing and emerging
communication solutions for serving IoT applications in the context of
cellular, wide-area, as well as non-terrestrial networks. Specifically,
wireless technology enhancements for providing IoT access in fifth-generation
(5G) and beyond cellular networks, and communication networks over the
unlicensed spectrum are presented. Aligned with the main key performance
indicators of 5G and beyond 5G networks, we investigate solutions and standards
that enable energy efficiency, reliability, low latency, and scalability
(connection density) of current and future IoT networks. The solutions include
grant-free access and channel coding for short-packet communications,
non-orthogonal multiple access, and on-device intelligence. Further, a vision
of new paradigm shifts in communication networks in the 2030s is provided, and
the integration of the associated new technologies like artificial
intelligence, non-terrestrial networks, and new spectra is elaborated. Finally,
future research directions toward beyond 5G IoT networks are pointed out.Comment: Submitted for review to IEEE CS&
Non-Coherent Active Device Identification for Massive Random Access
Massive Machine-Type Communications (mMTC) is a key service category in the
current generation of wireless networks featuring an extremely high density of
energy and resource-limited devices with sparse and sporadic activity patterns.
In order to enable random access in such mMTC networks, base station needs to
identify the active devices while operating within stringent access delay
constraints. In this paper, an energy efficient active device identification
protocol is proposed in which active devices transmit On-Off Keying (OOK)
modulated preambles jointly and base station employs non-coherent energy
detection avoiding channel estimation overheads. The minimum number of
channel-uses required by the active user identification protocol is
characterized in the asymptotic regime of total number of devices when
the number of active devices scales as along with an
achievability scheme relying on the equivalence of activity detection to a
group testing problem. Several practical schemes based on Belief Propagation
(BP) and Combinatorial Orthogonal Matching Pursuit (COMP) are also proposed.
Simulation results show that BP strategies outperform COMP significantly and
can operate close to the theoretical achievability bounds. In a
partial-recovery setting where few misdetections are allowed, BP continues to
perform well
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