191 research outputs found
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
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
Joint Domain Based Massive Access for Small Packets Traffic of Uplink Wireless Channel
The fifth generation (5G) communication scenarios such as the cellular
network and the emerging machine type communications will produce massive small
packets. To support massive connectivity and avoid signaling overhead caused by
the transmission of those small packets, this paper proposes a novel method to
improve the transmission efficiency for massive connections of wireless uplink
channel. The proposed method combines compressive sensing (CS) with power
domain NOMA jointly, especially neither the scheduling nor the centralized
power allocation is necessary in the method. Both the analysis and simulation
show that the method can support up to two or three times overloading.Comment: 6 pages, 5 figures.submitted to globecom 201
Joint User and Data Detection in Grant-Free NOMA with Attention-based BiLSTM Network
We consider the multi-user detection (MUD) problem in uplink grant-free
non-orthogonal multiple access (NOMA), where the access point has to identify
the total number and correct identity of the active Internet of Things (IoT)
devices and decode their transmitted data. We assume that IoT devices use
complex spreading sequences and transmit information in a random-access manner
following the burst-sparsity model, where some IoT devices transmit their data
in multiple adjacent time slots with a high probability, while others transmit
only once during a frame. Exploiting the temporal correlation, we propose an
attention-based bidirectional long short-term memory (BiLSTM) network to solve
the MUD problem. The BiLSTM network creates a pattern of the device activation
history using forward and reverse pass LSTMs, whereas the attention mechanism
provides essential context to the device activation points. By doing so, a
hierarchical pathway is followed for detecting active devices in a grant-free
scenario. Then, by utilising the complex spreading sequences, blind data
detection for the estimated active devices is performed. The proposed framework
does not require prior knowledge of device sparsity levels and channels for
performing MUD. The results show that the proposed network achieves better
performance compared to existing benchmark schemes
Modeling, Analysis, and Optimization of Grant-Free NOMA in Massive MTC via Stochastic Geometry
Massive machine-type communications (mMTC) is a crucial scenario to support
booming Internet of Things (IoTs) applications. In mMTC, although a large
number of devices are registered to an access point (AP), very few of them are
active with uplink short packet transmission at the same time, which requires
novel design of protocols and receivers to enable efficient data transmission
and accurate multi-user detection (MUD). Aiming at this problem, grant-free
non-orthogonal multiple access (GF-NOMA) protocol is proposed. In GF-NOMA,
active devices can directly transmit their preambles and data symbols
altogether within one time frame, without grant from the AP. Compressive
sensing (CS)-based receivers are adopted for non-orthogonal preambles
(NOP)-based MUD, and successive interference cancellation is exploited to
decode the superimposed data signals. In this paper, we model, analyze, and
optimize the CS-based GF-MONA mMTC system via stochastic geometry (SG), from an
aspect of network deployment. Based on the SG network model, we first analyze
the success probability as well as the channel estimation error of the CS-based
MUD in the preamble phase and then analyze the average aggregate data rate in
the data phase. As IoT applications highly demands low energy consumption, low
infrastructure cost, and flexible deployment, we optimize the energy efficiency
and AP coverage efficiency of GF-NOMA via numerical methods. The validity of
our analysis is verified via Monte Carlo simulations. Simulation results also
show that CS-based GF-NOMA with NOP yields better MUD and data rate
performances than contention-based GF-NOMA with orthogonal preambles and
CS-based grant-free orthogonal multiple access.Comment: This paper is submitted to IEEE Internet Of Things Journa
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