192 research outputs found
Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions
The ever-increasing number of resource-constrained Machine-Type Communication
(MTC) devices is leading to the critical challenge of fulfilling diverse
communication requirements in dynamic and ultra-dense wireless environments.
Among different application scenarios that the upcoming 5G and beyond cellular
networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the
unique technical challenge of supporting a huge number of MTC devices, which is
the main focus of this paper. The related challenges include QoS provisioning,
handling highly dynamic and sporadic MTC traffic, huge signalling overhead and
Radio Access Network (RAN) congestion. In this regard, this paper aims to
identify and analyze the involved technical issues, to review recent advances,
to highlight potential solutions and to propose new research directions. First,
starting with an overview of mMTC features and QoS provisioning issues, we
present the key enablers for mMTC in cellular networks. Along with the
highlights on the inefficiency of the legacy Random Access (RA) procedure in
the mMTC scenario, we then present the key features and channel access
mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT.
Subsequently, we present a framework for the performance analysis of
transmission scheduling with the QoS support along with the issues involved in
short data packet transmission. Next, we provide a detailed overview of the
existing and emerging solutions towards addressing RAN congestion problem, and
then identify potential advantages, challenges and use cases for the
applications of emerging Machine Learning (ML) techniques in ultra-dense
cellular networks. Out of several ML techniques, we focus on the application of
low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss
some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future
publication in IEEE Communications Surveys and Tutorial
Ultra-Dense Mobile Networks: Optimal Design and Communications Strategies
This thesis conducts an extensive analysis within the mobile telecommunications sub-field of the ultra-dense mobile networks, in which a massive deployment of network’s pieces of equipment is assumed. Future cache-enabled mobile networks are expected to meet most of the generated content demands directly at the edge, where each node has the availability to proactively store a set of contents in a local memory. This thesis makes several important contributions. The research being presented in this thesis proposes new analytical expressions to modeling the performance associated to the network’s edge. Base-stations’ idling technologies are also investigated to temporarily turn off some network nodes, saving energy and, in some circumstances, improving the overall performance by contributing less interference at the network’s edge. On the other hand, making use of fewer base-stations however reduces the amount of available resources at the network’s edge. A trade-off is investigated, which balances among interference saturation and available resources to increase the average user’s quality of experience. In this work, we treat the edge node density as a variable of the problem. This greatly increases the difficulty of obtaining analytical expressions, but also offers a direct access for optimizing the users’ average performance and network’s energy consumptions. An energy-focused performance metric is subsequently proposed, with the intention to highlight an interesting duality within the same network’s tier, which can transition from a better efficient to a more performing state, according to the energy expenses from the operators. Nonetheless, under an ultra-dense scenario, line-of-sight wireless links between the user and the nodes become more likely. The introduction of a main component of the multi-path propagated copies of a signal involves analytical complications. A feasible approximation is proposed and validated through a set of computer simulations. The scalability of the proposed technique allows to generalise existing results in the literature
Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks
Soaring capacity and coverage demands dictate that future cellular networks
need to soon migrate towards ultra-dense networks. However, network
densification comes with a host of challenges that include compromised energy
efficiency, complex interference management, cumbersome mobility management,
burdensome signaling overheads and higher backhaul costs. Interestingly, most
of the problems, that beleaguer network densification, stem from legacy
networks' one common feature i.e., tight coupling between the control and data
planes regardless of their degree of heterogeneity and cell density.
Consequently, in wake of 5G, control and data planes separation architecture
(SARC) has recently been conceived as a promising paradigm that has potential
to address most of aforementioned challenges. In this article, we review
various proposals that have been presented in literature so far to enable SARC.
More specifically, we analyze how and to what degree various SARC proposals
address the four main challenges in network densification namely: energy
efficiency, system level capacity maximization, interference management and
mobility management. We then focus on two salient features of future cellular
networks that have not yet been adapted in legacy networks at wide scale and
thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and
device-to-device (D2D) communications. After providing necessary background on
CoMP and D2D, we analyze how SARC can particularly act as a major enabler for
CoMP and D2D in context of 5G. This article thus serves as both a tutorial as
well as an up to date survey on SARC, CoMP and D2D. Most importantly, the
article provides an extensive outlook of challenges and opportunities that lie
at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201
Ternary Stochastic Geometry Theory for Performance Analysis of RIS-Assisted UDN
With the fast development of reconfigurable intelligent surface (RIS), the
network topology becomes more complex and varied, which makes the network
design and analysis extremely challenging. Most of the current works adopt the
binary system stochastic geometric, missing the coupling relationships between
the direct and reflected paths caused by RISs. In this paper, we first define
the typical triangle which consists of a base station (BS), a RIS and a user
equipment (UE) as the basic ternary network unit in a RIS-assisted ultra-dense
network (UDN). In addition, we extend the Campbell's theorem to the ternary
system and present the ternary probability generating functional (PGFL) of the
stochastic geometry. Based on the ternary stochastic geometry theory, we derive
and analyze the coverage probability, area spectral efficiency (ASE), area
energy efficiency (AEE) and energy coverage efficiency (ECE) of the
RIS-assisted UDN system. Simulation results show that the RISs can improve the
system performances, especially for the UE who has a high signal to
interference plus noise ratio (SINR), as if the introduced RIS brings in
Matthew effect. This phenomenon of RIS is appealing for guiding the design of
complex networks.Comment: 29 pages, 11 figure
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