1,899 research outputs found
Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions
Traditional power grids are being transformed into Smart Grids (SGs) to
address the issues in existing power system due to uni-directional information
flow, energy wastage, growing energy demand, reliability and security. SGs
offer bi-directional energy flow between service providers and consumers,
involving power generation, transmission, distribution and utilization systems.
SGs employ various devices for the monitoring, analysis and control of the
grid, deployed at power plants, distribution centers and in consumers' premises
in a very large number. Hence, an SG requires connectivity, automation and the
tracking of such devices. This is achieved with the help of Internet of Things
(IoT). IoT helps SG systems to support various network functions throughout the
generation, transmission, distribution and consumption of energy by
incorporating IoT devices (such as sensors, actuators and smart meters), as
well as by providing the connectivity, automation and tracking for such
devices. In this paper, we provide a comprehensive survey on IoT-aided SG
systems, which includes the existing architectures, applications and prototypes
of IoT-aided SG systems. This survey also highlights the open issues,
challenges and future research directions for IoT-aided SG systems
Opportunistic Relaying in Wireless Networks
Relay networks having source-to-destination pairs and half-duplex
relays, all operating in the same frequency band in the presence of block
fading, are analyzed. This setup has attracted significant attention and
several relaying protocols have been reported in the literature. However, most
of the proposed solutions require either centrally coordinated scheduling or
detailed channel state information (CSI) at the transmitter side. Here, an
opportunistic relaying scheme is proposed, which alleviates these limitations.
The scheme entails a two-hop communication protocol, in which sources
communicate with destinations only through half-duplex relays. The key idea is
to schedule at each hop only a subset of nodes that can benefit from
\emph{multiuser diversity}. To select the source and destination nodes for each
hop, it requires only CSI at receivers (relays for the first hop, and
destination nodes for the second hop) and an integer-value CSI feedback to the
transmitters. For the case when is large and is fixed, it is shown that
the proposed scheme achieves a system throughput of bits/s/Hz. In
contrast, the information-theoretic upper bound of bits/s/Hz
is achievable only with more demanding CSI assumptions and cooperation between
the relays. Furthermore, it is shown that, under the condition that the product
of block duration and system bandwidth scales faster than , the
achievable throughput of the proposed scheme scales as .
Notably, this is proven to be the optimal throughput scaling even if
centralized scheduling is allowed, thus proving the optimality of the proposed
scheme in the scaling law sense.Comment: 17 pages, 8 figures, To appear in IEEE Transactions on Information
Theor
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Interference alignment testbeds
Interference alignment has triggered high impact research in wireless communications since it was proposed nearly 10 years ago. However, the vast majority of research is centered on the theory of interference alignment and is hardly feasible in view of the existing state-of-the-art wireless technologies. Although several research groups have assessed the feasibility of interference alignment via testbed measurements in realistic environments, the experimental evaluation of interference alignment is still in its infancy since most of the experiments were limited to simpler scenarios and configurations. This article summarizes the practical limitations of experimentally evaluating interference alignment, provides an overview of the available interference alignment testbed implementations, including the costs, and highlights the imperatives for succeeding interference alignment testbed implementations. Finally, the article explores future research directions on the applications of interference alignment in the next generation wireless systems.Jacobo Fanjul's research has been supported by the Ministerio de EconomĂa y Competitividad (MINECO) of Spain, under grants TEC2013-47141-C4-R (RACHEL project) and FPI grant BES-2014-069786. JosĂ© A. GarcĂa-Naya's research has been funded by the Xunta de Galicia (ED431C 2016â045, ED341D R2016/012, E0431 G/01), the Agencia Estatal de InvestigaciĂłn of Spain (TEC2013-47141-C4-1-R, TEC2015-69648-REOC, TEC2016-75067-C4-1-R), and ERDF funds of the EU (AEI/FEDER, UE). Hamed Farhadi's research has been funded by the Swedish Research Council (VR) under grant 2015â00500
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