253 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
FiFo: Fishbone Forwarding in Massive IoT Networks
Massive Internet of Things (IoT) networks have a wide range of applications,
including but not limited to the rapid delivery of emergency and disaster
messages. Although various benchmark algorithms have been developed to date for
message delivery in such applications, they pose several practical challenges
such as insufficient network coverage and/or highly redundant transmissions to
expand the coverage area, resulting in considerable energy consumption for each
IoT device. To overcome this problem, we first characterize a new performance
metric, forwarding efficiency, which is defined as the ratio of the coverage
probability to the average number of transmissions per device, to evaluate the
data dissemination performance more appropriately. Then, we propose a novel and
effective forwarding method, fishbone forwarding (FiFo), which aims to improve
the forwarding efficiency with acceptable computational complexity. Our FiFo
method completes two tasks: 1) it clusters devices based on the unweighted pair
group method with the arithmetic average; and 2) it creates the main axis and
sub axes of each cluster using both the expectation-maximization algorithm for
the Gaussian mixture model and principal component analysis. We demonstrate the
superiority of FiFo by using a real-world dataset. Through intensive and
comprehensive simulations, we show that the proposed FiFo method outperforms
benchmark algorithms in terms of the forwarding efficiency.Comment: 13 pages, 16 figures, 5 tables; to appear in the IEEE Internet of
Things Journal (Please cite our journal version that will appear in an
upcoming issue.
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
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