116 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
Compressive Sensing Based Massive Access for IoT Relying on Media Modulation Aided Machine Type Communications
A fundamental challenge of the large-scale Internet-of-Things lies in how to
support massive machine-type communications (mMTC). This letter proposes a
media modulation based mMTC solution for increasing the throughput, where a
massive multi-input multi-output based base station (BS) is used for enhancing
the detection performance. For such a mMTC scenario, the reliable active device
detection and data decoding pose a serious challenge. By leveraging the
sparsity of the uplink access signals of mMTC received at the BS, a compressive
sensing based massive access solution is proposed for tackling this challenge.
Specifically, we propose a block sparsity adaptive matching pursuit algorithm
for detecting the active devices, whereby the block-sparsity of the uplink
access signals exhibited across the successive time slots and the structured
sparsity of media modulated symbols are exploited for enhancing the detection
performance. Moreover, a successive interference cancellation based structured
subspace pursuit algorithm is conceived for data demodulation of the active
devices, whereby the structured sparsity of media modulation based symbols
found in each time slot is exploited for improving the detection performance.
Finally, our simulation results verify the superiority of the proposed scheme
over state-of-the-art solutions.Comment: submitted to IEEE Transactions on Vehicular Technology [Major
Revision
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
Massive Access in Media Modulation Based Massive Machine-Type Communications
The massive machine-type communications (mMTC) paradigm based on media
modulation in conjunction with massive MIMO base stations (BSs) is emerging as
a viable solution to support the massive connectivity for the future
Internet-of-Things, in which the inherent massive access at the BSs poses
significant challenges for device activity and data detection (DADD). This
paper considers the DADD problem for both uncoded and coded media modulation
based mMTC with a slotted access frame structure, where the device activity
remains unchanged within one frame. Specifically, due to the slotted access
frame structure and the adopted media modulated symbols, the access signals
exhibit a doubly structured sparsity in both the time domain and the modulation
domain. Inspired by this, a doubly structured approximate message passing
(DS-AMP) algorithm is proposed for reliable DADD in the uncoded case. Also, we
derive the state evolution of the DS-AMP algorithm to theoretically
characterize its performance. As for the coded case, we develop a
bit-interleaved coded media modulation scheme and propose an iterative DS-AMP
(IDS-AMP) algorithm based on successive inference cancellation (SIC), where the
signal components associated with the detected active devices are successively
subtracted to improve the data decoding performance. In addition, the channel
estimation problem for media modulation based mMTC is discussed and an
efficient data-aided channel state information (CSI) update strategy is
developed to reduce the training overhead in block fading channels. Finally,
simulation results and computational complexity analysis verify the superiority
of the proposed algorithms in both uncoded and coded cases. Also, our results
verify the validity of the proposed data-aided CSI update strategy.Comment: Accepted by IEEE Transactions on Wireless Communications. The codes
and some other materials about this work may be available at
https://gaozhen16.github.i
Active Terminal Identification, Channel Estimation, and Signal Detection for Grant-Free NOMA-OTFS in LEO Satellite Internet-of-Things
This paper investigates the massive connectivity of low Earth orbit (LEO)
satellite-based Internet-of-Things (IoT) for seamless global coverage. We
propose to integrate the grant-free non-orthogonal multiple access (GF-NOMA)
paradigm with the emerging orthogonal time frequency space (OTFS) modulation to
accommodate the massive IoT access, and mitigate the long round-trip latency
and severe Doppler effect of terrestrial-satellite links (TSLs). On this basis,
we put forward a two-stage successive active terminal identification (ATI) and
channel estimation (CE) scheme as well as a low-complexity multi-user signal
detection (SD) method. Specifically, at the first stage, the proposed training
sequence aided OTFS (TS-OTFS) data frame structure facilitates the joint ATI
and coarse CE, whereby both the traffic sparsity of terrestrial IoT terminals
and the sparse channel impulse response are leveraged for enhanced performance.
Moreover, based on the single Doppler shift property for each TSL and sparsity
of delay-Doppler domain channel, we develop a parametric approach to further
refine the CE performance. Finally, a least square based parallel time domain
SD method is developed to detect the OTFS signals with relatively low
complexity. Simulation results demonstrate the superiority of the proposed
methods over the state-of-the-art solutions in terms of ATI, CE, and SD
performance confronted with the long round-trip latency and severe Doppler
effect.Comment: 20 pages, 9 figures, accepted by IEEE Transactions on Wireless
Communication
Multidimensional Index Modulation for 5G and Beyond Wireless Networks
This study examines the flexible utilization of existing IM techniques in a
comprehensive manner to satisfy the challenging and diverse requirements of 5G
and beyond services. After spatial modulation (SM), which transmits information
bits through antenna indices, application of IM to orthogonal frequency
division multiplexing (OFDM) subcarriers has opened the door for the extension
of IM into different dimensions, such as radio frequency (RF) mirrors, time
slots, codes, and dispersion matrices. Recent studies have introduced the
concept of multidimensional IM by various combinations of one-dimensional IM
techniques to provide higher spectral efficiency (SE) and better bit error rate
(BER) performance at the expense of higher transmitter (Tx) and receiver (Rx)
complexity. Despite the ongoing research on the design of new IM techniques and
their implementation challenges, proper use of the available IM techniques to
address different requirements of 5G and beyond networks is an open research
area in the literature. For this reason, we first provide the dimensional-based
categorization of available IM domains and review the existing IM types
regarding this categorization. Then, we develop a framework that investigates
the efficient utilization of these techniques and establishes a link between
the IM schemes and 5G services, namely enhanced mobile broadband (eMBB),
massive machine-type communications (mMTC), and ultra-reliable low-latency
communication (URLLC). Additionally, this work defines key performance
indicators (KPIs) to quantify the advantages and disadvantages of IM techniques
in time, frequency, space, and code dimensions. Finally, future recommendations
are given regarding the design of flexible IM-based communication systems for
5G and beyond wireless networks.Comment: This work has been submitted to Proceedings of the IEEE for possible
publicatio
Matrix Factorization Based Blind Bayesian Receiver for Grant-Free Random Access in mmWave MIMO mMTC
Grant-free random access is promising for massive connectivity with sporadic
transmissions in massive machine type communications (mMTC), where the
hand-shaking between the access point (AP) and users is skipped, leading to
high access efficiency. In grant-free random access, the AP needs to identify
the active users and perform channel estimation and signal detection.
Conventionally, pilot signals are required for the AP to achieve user activity
detection and channel estimation before active user signal detection, which may
still result in substantial overhead and latency. In this paper, to further
reduce the overhead and latency, we explore the problem of grant-free random
access without the use of pilot signals in a millimeter wave (mmWave) multiple
input and multiple output (MIMO) system, where the AP performs blind joint user
activity detection, channel estimation and signal detection (UACESD). We show
that the blind joint UACESD can be formulated as a constrained composite matrix
factorization problem, which can be solved by exploiting the structures of the
channel matrix and signal matrix. Leveraging our recently developed unitary
approximate message passing based matrix factorization (UAMP-MF) algorithm, we
design a message passing based Bayesian algorithm to solve the blind joint
UACESD problem. Extensive simulation results demonstrate the effectiveness of
the blind grant-free random access scheme
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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