1,051 research outputs found
Quantifying Potential Energy Efficiency Gain in Green Cellular Wireless Networks
Conventional cellular wireless networks were designed with the purpose of
providing high throughput for the user and high capacity for the service
provider, without any provisions of energy efficiency. As a result, these
networks have an enormous Carbon footprint. In this paper, we describe the
sources of the inefficiencies in such networks. First we present results of the
studies on how much Carbon footprint such networks generate. We also discuss
how much more mobile traffic is expected to increase so that this Carbon
footprint will even increase tremendously more. We then discuss specific
sources of inefficiency and potential sources of improvement at the physical
layer as well as at higher layers of the communication protocol hierarchy. In
particular, considering that most of the energy inefficiency in cellular
wireless networks is at the base stations, we discuss multi-tier networks and
point to the potential of exploiting mobility patterns in order to use base
station energy judiciously. We then investigate potential methods to reduce
this inefficiency and quantify their individual contributions. By a
consideration of the combination of all potential gains, we conclude that an
improvement in energy consumption in cellular wireless networks by two orders
of magnitude, or even more, is possible.Comment: arXiv admin note: text overlap with arXiv:1210.843
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
Energy-Efficient Power Control: A Look at 5G Wireless Technologies
This work develops power control algorithms for energy efficiency (EE)
maximization (measured in bit/Joule) in wireless networks. Unlike previous
related works, minimum-rate constraints are imposed and the
signal-to-interference-plus-noise ratio takes a more general expression, which
allows one to encompass some of the most promising 5G candidate technologies.
Both network-centric and user-centric EE maximizations are considered. In the
network-centric scenario, the maximization of the global EE and the minimum EE
of the network are performed. Unlike previous contributions, we develop
centralized algorithms that are guaranteed to converge, with affordable
computational complexity, to a Karush-Kuhn-Tucker point of the considered
non-convex optimization problems. Moreover, closed-form feasibility conditions
are derived. In the user-centric scenario, game theory is used to study the
equilibria of the network and to derive convergent power control algorithms,
which can be implemented in a fully decentralized fashion. Both scenarios above
are studied under the assumption that single or multiple resource blocks are
employed for data transmission. Numerical results assess the performance of the
proposed solutions, analyzing the impact of minimum-rate constraints, and
comparing the network-centric and user-centric approaches.Comment: Accepted for Publication in the IEEE Transactions on Signal
Processin
Twin Delayed DDPG based Dynamic Power Allocation for Mobility in IoRT
The internet of robotic things (IoRT) is a modern as well as fast-evolving technology employed in abundant socio-economical aspects which connect user equipment (UE) for communication and data transfer among each other. For ensuring the quality of service (QoS) in IoRT applications, radio resources, for example, transmitting power allocation (PA), interference management, throughput maximization etc., should be efficiently employed and allocated among UE. Traditionally, resource allocation has been formulated using optimization problems, which are then solved using mathematical computer techniques. However, those optimization problems are generally nonconvex as well as nondeterministic polynomial-time hardness (NP-hard). In this paper, one of the most crucial challenges in radio resource management is the emitting power of an antenna called PA, considering that the interfering multiple access channel (IMAC) has been considered. In addition, UE has a natural movement behavior that directly impacts the channel condition between remote radio head (RRH) and UE. Additionally, we have considered two well-known UE mobility models i) random walk and ii) modified Gauss-Markov (GM). As a result, the simulation environment is more realistic and complex. A data-driven as well as model-free continuous action based deep reinforcement learning algorithm called twin delayed deep deterministic policy gradient (TD3) has been proposed that is the combination of policy gradient, actor-critics, as well as double deep Q-learning (DDQL). It optimizes the PA for i) stationary UE, ii) the UE movements according to random walk model, and ii) the UE movement based on the modified GM model. Simulation results show that the proposed TD3 method outperforms model-based techniques like weighted MMSE (WMMSE) and fractional programming (FP) as well as model-free algorithms, for example, deep Q network (DQN) and DDPG in terms of average sum-rate performance
Ondas milimétricas e MIMO massivo para otimização da capacidade e cobertura de redes heterogeneas de 5G
Today's Long Term Evolution Advanced (LTE-A) networks cannot support
the exponential growth in mobile traffic forecast for the next decade. By
2020, according to Ericsson, 6 billion mobile subscribers worldwide are projected
to generate 46 exabytes of mobile data traffic monthly from 24 billion
connected devices, smartphones and short-range Internet of Things (IoT)
devices being the key prosumers. In response, 5G networks are foreseen
to markedly outperform legacy 4G systems. Triggered by the International
Telecommunication Union (ITU) under the IMT-2020 network initiative, 5G
will support three broad categories of use cases: enhanced mobile broadband
(eMBB) for multi-Gbps data rate applications; ultra-reliable and low latency
communications (URLLC) for critical scenarios; and massive machine
type communications (mMTC) for massive connectivity. Among the several
technology enablers being explored for 5G, millimeter-wave (mmWave)
communication, massive MIMO antenna arrays and ultra-dense small cell
networks (UDNs) feature as the dominant technologies. These technologies
in synergy are anticipated to provide the 1000_ capacity increase for 5G
networks (relative to 4G) through the combined impact of large additional
bandwidth, spectral efficiency (SE) enhancement and high frequency reuse,
respectively. However, although these technologies can pave the way towards
gigabit wireless, there are still several challenges to solve in terms of
how we can fully harness the available bandwidth efficiently through appropriate
beamforming and channel modeling approaches. In this thesis, we
investigate the system performance enhancements realizable with mmWave
massive MIMO in 5G UDN and cellular infrastructure-to-everything (C-I2X)
application scenarios involving pedestrian and vehicular users. As a critical
component of the system-level simulation approach adopted in this thesis,
we implemented 3D channel models for the accurate characterization of the
wireless channels in these scenarios and for realistic performance evaluation.
To address the hardware cost, complexity and power consumption of the
massive MIMO architectures, we propose a novel generalized framework for
hybrid beamforming (HBF) array structures. The generalized model reveals
the opportunities that can be harnessed with the overlapped subarray structures
for a balanced trade-o_ between SE and energy efficiently (EE) of 5G
networks. The key results in this investigation show that mmWave massive
MIMO can deliver multi-Gbps rates for 5G whilst maintaining energy-efficient operation of the network.As redes LTE-A atuais não são capazes de suportar o crescimento exponencial
de tráfego que está previsto para a próxima década. De acordo
com a previsão da Ericsson, espera-se que em 2020, a nível global, 6 mil
milhões de subscritores venham a gerar mensalmente 46 exa bytes de tráfego
de dados a partir de 24 mil milhões de dispositivos ligados à rede móvel,
sendo os telefones inteligentes e dispositivos IoT de curto alcance os principais
responsáveis por tal nível de tráfego. Em resposta a esta exigência,
espera-se que as redes de 5a geração (5G) tenham um desempenho substancialmente
superior às redes de 4a geração (4G) atuais. Desencadeado pelo
UIT (União Internacional das Telecomunicações) no âmbito da iniciativa
IMT-2020, o 5G irá suportar três grandes tipos de utilizações: banda larga
móvel capaz de suportar aplicações com débitos na ordem de vários Gbps;
comunicações de baixa latência e alta fiabilidade indispensáveis em cenários
de emergência; comunicações massivas máquina-a-máquina para conectividade
generalizada. Entre as várias tecnologias capacitadoras que estão a ser
exploradas pelo 5G, as comunicações através de ondas milimétricas, os agregados
MIMO massivo e as redes celulares ultradensas (RUD) apresentam-se
como sendo as tecnologias fundamentais. Antecipa-se que o conjunto
destas tecnologias venha a fornecer às redes 5G um aumento de capacidade
de 1000x através da utilização de maiores larguras de banda, melhoria da
eficiência espectral, e elevada reutilização de frequências respetivamente.
Embora estas tecnologias possam abrir caminho para as redes sem fios
com débitos na ordem dos gigabits, existem ainda vários desafios que têm
que ser resolvidos para que seja possível aproveitar totalmente a largura de
banda disponível de maneira eficiente utilizando abordagens de formatação
de feixe e de modelação de canal adequadas. Nesta tese investigamos a
melhoria de desempenho do sistema conseguida através da utilização de
ondas milimétricas e agregados MIMO massivo em cenários de redes celulares
ultradensas de 5a geração e em cenários 'infraestrutura celular-para-qualquer
coisa' (do inglês: cellular infrastructure-to-everything) envolvendo
utilizadores pedestres e veiculares. Como um componente fundamental das
simulações de sistema utilizadas nesta tese é o canal de propagação, implementamos modelos de canal tridimensional (3D) para caracterizar de
forma precisa o canal de propagação nestes cenários e assim conseguir uma
avaliação de desempenho mais condizente com a realidade. Para resolver os
problemas associados ao custo do equipamento, complexidade e consumo
de energia das arquiteturas MIMO massivo, propomos um modelo inovador
de agregados com formatação de feixe híbrida. Este modelo genérico revela
as oportunidades que podem ser aproveitadas através da sobreposição
de sub-agregados no sentido de obter um compromisso equilibrado entre
eficiência espectral (ES) e eficiência energética (EE) nas redes 5G. Os principais
resultados desta investigação mostram que a utilização conjunta de
ondas milimétricas e de agregados MIMO massivo possibilita a obtenção, em
simultâneo, de taxas de transmissão na ordem de vários Gbps e a operação
de rede de forma energeticamente eficiente.Programa Doutoral em Telecomunicaçõe
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