446 research outputs found
Sparse Message Passing Based Preamble Estimation for Crowded M2M Communications
Due to the massive number of devices in the M2M communication era, new
challenges have been brought to the existing random-access (RA) mechanism, such
as severe preamble collisions and resource block (RB) wastes. To address these
problems, a novel sparse message passing (SMP) algorithm is proposed, based on
a factor graph on which Bernoulli messages are updated. The SMP enables an
accurate estimation on the activity of the devices and the identity of the
preamble chosen by each active device. Aided by the estimation, the RB
efficiency for the uplink data transmission can be improved, especially among
the collided devices. In addition, an analytical tool is derived to analyze the
iterative evolution and convergence of the SMP algorithm. Finally, numerical
simulations are provided to verify the validity of our analytical results and
the significant improvement of the proposed SMP on estimation error rate even
when preamble collision occurs.Comment: submitted to ICC 2018 with 6 pages and 4 figure
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
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
Message Passing in C-RAN: Joint User Activity and Signal Detection
In cloud radio access network (C-RAN), remote radio heads (RRHs) and users
are uniformly distributed in a large area such that the channel matrix can be
considered as sparse. Based on this phenomenon, RRHs only need to detect the
relatively strong signals from nearby users and ignore the weak signals from
far users, which is helpful to develop low-complexity detection algorithms
without causing much performance loss. However, before detection, RRHs require
to obtain the realtime user activity information by the dynamic grant
procedure, which causes the enormous latency. To address this issue, in this
paper, we consider a grant-free C-RAN system and propose a low-complexity
Bernoulli-Gaussian message passing (BGMP) algorithm based on the sparsified
channel, which jointly detects the user activity and signal. Since active users
are assumed to transmit Gaussian signals at any time, the user activity can be
regarded as a Bernoulli variable and the signals from all users obey a
Bernoulli-Gaussian distribution. In the BGMP, the detection functions for
signals are designed with respect to the Bernoulli-Gaussian variable. Numerical
results demonstrate the robustness and effectivity of the BGMP. That is, for
different sparsified channels, the BGMP can approach the mean-square error
(MSE) of the genie-aided sparse minimum mean-square error (GA-SMMSE) which
exactly knows the user activity information. Meanwhile, the fast convergence
and strong recovery capability for user activity of the BGMP are also verified.Comment: Conference, 6 pages, 7 figures, accepted by IEEE Globecom 201
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