15,908 research outputs found
A Comprehensive Analysis of 5G Heterogeneous Cellular Systems operating over - Shadowed Fading Channels
Emerging cellular technologies such as those proposed for use in 5G
communications will accommodate a wide range of usage scenarios with diverse
link requirements. This will include the necessity to operate over a versatile
set of wireless channels ranging from indoor to outdoor, from line-of-sight
(LOS) to non-LOS, and from circularly symmetric scattering to environments
which promote the clustering of scattered multipath waves. Unfortunately, many
of the conventional fading models adopted in the literature to develop network
models lack the flexibility to account for such disparate signal propagation
mechanisms. To bridge the gap between theory and practical channels, we
consider - shadowed fading, which contains as special cases, the
majority of the linear fading models proposed in the open literature, including
Rayleigh, Rician, Nakagami-m, Nakagami-q, One-sided Gaussian, -,
-, and Rician shadowed to name but a few. In particular, we apply an
orthogonal expansion to represent the - shadowed fading
distribution as a simplified series expression. Then using the series
expressions with stochastic geometry, we propose an analytic framework to
evaluate the average of an arbitrary function of the SINR over -
shadowed fading channels. Using the proposed method, we evaluate the spectral
efficiency, moments of the SINR, bit error probability and outage probability
of a -tier HetNet with classes of BSs, differing in terms of the
transmit power, BS density, shadowing characteristics and small-scale fading.
Building upon these results, we provide important new insights into the network
performance of these emerging wireless applications while considering a diverse
range of fading conditions and link qualities
Wearable Communications in 5G: Challenges and Enabling Technologies
As wearable devices become more ingrained in our daily lives, traditional
communication networks primarily designed for human being-oriented applications
are facing tremendous challenges. The upcoming 5G wireless system aims to
support unprecedented high capacity, low latency, and massive connectivity. In
this article, we evaluate key challenges in wearable communications. A
cloud/edge communication architecture that integrates the cloud radio access
network, software defined network, device to device communications, and
cloud/edge technologies is presented. Computation offloading enabled by this
multi-layer communications architecture can offload computation-excessive and
latency-stringent applications to nearby devices through device to device
communications or to nearby edge nodes through cellular or other wireless
technologies. Critical issues faced by wearable communications such as short
battery life, limited computing capability, and stringent latency can be
greatly alleviated by this cloud/edge architecture. Together with the presented
architecture, current transmission and networking technologies, including
non-orthogonal multiple access, mobile edge computing, and energy harvesting,
can greatly enhance the performance of wearable communication in terms of
spectral efficiency, energy efficiency, latency, and connectivity.Comment: This work has been accepted by IEEE Vehicular Technology Magazin
Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks
Without any doubt, Machine Learning (ML) will be an important driver of
future communications due to its foreseen performance when applied to complex
problems. However, the application of ML to networking systems raises concerns
among network operators and other stakeholders, especially regarding
trustworthiness and reliability. In this paper, we devise the role of network
simulators for bridging the gap between ML and communications systems. In
particular, we present an architectural integration of simulators in ML-aware
networks for training, testing, and validating ML models before being applied
to the operative network. Moreover, we provide insights on the main challenges
resulting from this integration, and then give hints discussing how they can be
overcome. Finally, we illustrate the integration of network simulators into
ML-assisted communications through a proof-of-concept testbed implementation of
a residential Wi-Fi network
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