30 research outputs found
Massive MIMO for Internet of Things (IoT) Connectivity
Massive MIMO is considered to be one of the key technologies in the emerging
5G systems, but also a concept applicable to other wireless systems. Exploiting
the large number of degrees of freedom (DoFs) of massive MIMO essential for
achieving high spectral efficiency, high data rates and extreme spatial
multiplexing of densely distributed users. On the one hand, the benefits of
applying massive MIMO for broadband communication are well known and there has
been a large body of research on designing communication schemes to support
high rates. On the other hand, using massive MIMO for Internet-of-Things (IoT)
is still a developing topic, as IoT connectivity has requirements and
constraints that are significantly different from the broadband connections. In
this paper we investigate the applicability of massive MIMO to IoT
connectivity. Specifically, we treat the two generic types of IoT connections
envisioned in 5G: massive machine-type communication (mMTC) and ultra-reliable
low-latency communication (URLLC). This paper fills this important gap by
identifying the opportunities and challenges in exploiting massive MIMO for IoT
connectivity. We provide insights into the trade-offs that emerge when massive
MIMO is applied to mMTC or URLLC and present a number of suitable communication
schemes. The discussion continues to the questions of network slicing of the
wireless resources and the use of massive MIMO to simultaneously support IoT
connections with very heterogeneous requirements. The main conclusion is that
massive MIMO can bring benefits to the scenarios with IoT connectivity, but it
requires tight integration of the physical-layer techniques with the protocol
design.Comment: Submitted for publicatio
Unsourced Random Access Using Multiple Stages of Orthogonal Pilots: MIMO and Single-Antenna Structures
We study the problem of unsourced random access (URA) over Rayleigh
block-fading channels with a receiver equipped with multiple antennas. We
propose a slotted structure with multiple stages of orthogonal pilots, each of
which is randomly picked from a codebook. In the proposed signaling structure,
each user encodes its message using a polar code and appends it to the selected
pilot sequences to construct its transmitted signal. Accordingly, the
transmitted signal is composed of multiple orthogonal pilot parts and a
polar-coded part, which is sent through a randomly selected slot. The
performance of the proposed scheme is further improved by randomly dividing
users into different groups each having a unique interleaver-power pair. We
also apply the idea of multiple stages of orthogonal pilots to the case of a
single receive antenna. In all the set-ups, we use an iterative approach for
decoding the transmitted messages along with a suitable successive interference
cancellation technique. The use of orthogonal pilots and the slotted structure
lead to improved accuracy and reduced computational complexity in the proposed
set-ups, and make the implementation with short blocklengths more viable.
Performance of the proposed set-ups is illustrated via extensive simulation
results which show that the proposed set-ups with multiple antennas perform
better than the existing MIMO URA solutions for both short and large
blocklengths, and that the proposed single-antenna set-ups are superior to the
existing single-antenna URA schemes
Massive Unsourced Random Access: Exploiting Angular Domain Sparsity
This paper investigates the unsourced random access (URA) scheme to accommodate numerous machine-type users communicating to a base station equipped with multiple antennas. Existing works adopt a slotted transmission strategy to reduce system complexity; they operate under the framework of coupled compressed sensing (CCS) which concatenates an outer tree code to an inner compressed sensing code for slot-wise message stitching. We suggest that by exploiting the MIMO channel information in the angular domain, redundancies required by the tree encoder/decoder in CCS can be removed to improve spectral efficiency, thereby an uncoupled transmission protocol is devised. To perform activity detection and channel estimation, we propose an expectation-maximization-aided generalized approximate message passing algorithm with a Markov random field support structure, which captures the inherent clustered sparsity structure of the angular domain channel. Then, message reconstruction in the form of a clustering decoder is performed by recognizing slot-distributed channels of each active user based on similarity. We put forward the slot-balanced K-means algorithm as the kernel of the clustering decoder, resolving constraints and collisions specific to the application scene. Extensive simulations reveal that the proposed scheme achieves a better error performance at high spectral efficiency compared to the CCS-based URA schemes
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
A Fully Bayesian Approach for Massive MIMO Unsourced Random Access
In this paper, we propose a novel fully Bayesian approach for the massive
multiple-input multiple-output (MIMO) massive unsourced random access (URA).
The payload of each user device is coded by the sparse regression codes
(SPARCs) without redundant parity bits. A Bayesian model is established to
capture the probabilistic characteristics of the overall system. Particularly,
we adopt the core idea of the model-based learning approach to establish a
flexible Bayesian channel model to adapt the complex environments. Different
from the traditional divide-and-conquer or pilot-based massive MIMO URA
strategies, we propose a three-layer message passing (TLMP) algorithm to
jointly decode all the information blocks, as well as acquire the massive MIMO
channel, which adopts the core idea of the variational message passing and
approximate message passing. We verify that our proposed TLMP significantly
enhances the spectral efficiency compared with the state-of-the-arts baselines,
and is more robust to the possible codeword collisions