10,063 research outputs found
Applications of Soft Computing in Mobile and Wireless Communications
Soft computing is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or too difficult to model mathematically. Furthermore, the use of conventional modeling techniques demands rigor, precision and certainty, which carry computational cost. On the other hand, soft computing utilizes computation, reasoning and inference to reduce computational cost by exploiting tolerance for imprecision, uncertainty, partial truth and approximation. In addition to computational cost savings, soft computing is an excellent platform for autonomic computing, owing to its roots in artificial intelligence. Wireless communication networks are associated with much uncertainty and imprecision due to a number of stochastic processes such as escalating number of access points, constantly changing propagation channels, sudden variations in network load and random mobility of users. This reality has fuelled numerous applications of soft computing techniques in mobile and wireless communications. This paper reviews various applications of the core soft computing methodologies in mobile and wireless communications
SPS pilot signal design and power transponder analysis, volume 2, phase 3
The problem of pilot signal parameter optimization and the related problem of power transponder performance analysis for the Solar Power Satellite reference phase control system are addressed. Signal and interference models were established to enable specifications of the front end filters including both the notch filter and the antenna frequency response. A simulation program package was developed to be included in SOLARSIM to perform tradeoffs of system parameters based on minimizing the phase error for the pilot phase extraction. An analytical model that characterizes the overall power transponder operation was developed. From this model, the effects of different phase noise disturbance sources that contribute to phase variations at the output of the power transponders were studied and quantified. Results indicate that it is feasible to hold the antenna array phase error to less than one degree per power module for the type of disturbances modeled
Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems
Development of robust dynamical systems and networks such as autonomous
aircraft systems capable of accomplishing complex missions faces challenges due
to the dynamically evolving uncertainties coming from model uncertainties,
necessity to operate in a hostile cluttered urban environment, and the
distributed and dynamic nature of the communication and computation resources.
Model-based robust design is difficult because of the complexity of the hybrid
dynamic models including continuous vehicle dynamics, the discrete models of
computations and communications, and the size of the problem. We will overview
recent advances in methodology and tools to model, analyze, and design robust
autonomous aerospace systems operating in uncertain environment, with stress on
efficient uncertainty quantification and robust design using the case studies
of the mission including model-based target tracking and search, and trajectory
planning in uncertain urban environment. To show that the methodology is
generally applicable to uncertain dynamical systems, we will also show examples
of application of the new methods to efficient uncertainty quantification of
energy usage in buildings, and stability assessment of interconnected power
networks
Efficient State-Space Inference of Periodic Latent Force Models
Latent force models (LFM) are principled approaches to incorporating
solutions to differential equations within non-parametric inference methods.
Unfortunately, the development and application of LFMs can be inhibited by
their computational cost, especially when closed-form solutions for the LFM are
unavailable, as is the case in many real world problems where these latent
forces exhibit periodic behaviour. Given this, we develop a new sparse
representation of LFMs which considerably improves their computational
efficiency, as well as broadening their applicability, in a principled way, to
domains with periodic or near periodic latent forces. Our approach uses a
linear basis model to approximate one generative model for each periodic force.
We assume that the latent forces are generated from Gaussian process priors and
develop a linear basis model which fully expresses these priors. We apply our
approach to model the thermal dynamics of domestic buildings and show that it
is effective at predicting day-ahead temperatures within the homes. We also
apply our approach within queueing theory in which quasi-periodic arrival rates
are modelled as latent forces. In both cases, we demonstrate that our approach
can be implemented efficiently using state-space methods which encode the
linear dynamic systems via LFMs. Further, we show that state estimates obtained
using periodic latent force models can reduce the root mean squared error to
17% of that from non-periodic models and 27% of the nearest rival approach
which is the resonator model.Comment: 61 pages, 13 figures, accepted for publication in JMLR. Updates from
earlier version occur throughout article in response to JMLR review
Modeling and Performance of Uplink Cache-Enabled Massive MIMO Heterogeneous Networks
A significant burden on wireless networks is brought by the uploading of user-generated contents to the Internet by means of applications such as social media. To cope with this mobile data tsunami, we develop a novel multiple-input multiple-output (MIMO) network architecture with randomly located base stations (BSs) a large number of antennas employing cache-enabled uplink transmission. In particular, we formulate a scenario, where the users upload their content to their strongest BSs, which are Poisson point process distributed. In addition, the BSs, exploiting the benefits of massive MIMO, upload their contents to the core network by means of a finite-rate backhaul. After proposing the caching policies, where we propose the modified von Mises distribution as the popularity distribution function, we derive the outage probability and the average delivery rate by taking advantage of tools from the deterministic equivalent and stochastic geometry analyses. Numerical results investigate the realistic performance gains of the proposed heterogeneous cache-enabled uplink on the network in terms of cardinal operating parameters. For example, insights regarding the BSs storage size are exposed. Moreover, the impacts of the key parameters such as the file popularity distribution and the target bitrate are investigated. Specifically, the outage probability decreases if the storage size is increased, while the average delivery rate increases. In addition, the concentration parameter, defining the number of files stored at the intermediate nodes (popularity), affects the proposed metrics directly. Furthermore, a higher target rate results in higher outage because fewer users obey this constraint. Also, we demonstrate that a denser network decreases the outage and increases the delivery rate. Hence, the introduction of caching at the uplink of the system design ameliorates the network performance.Peer reviewe
Teaching old sensors New tricks: archetypes of intelligence
In this paper a generic intelligent sensor software architecture is described which builds upon the basic requirements of related industry standards (IEEE 1451 and SEVA BS- 7986). It incorporates specific functionalities such as real-time fault detection, drift compensation, adaptation to environmental changes and autonomous reconfiguration. The modular based structure of the intelligent sensor architecture provides enhanced flexibility in regard to the choice of specific algorithmic realizations. In this context, the particular aspects of fault detection and drift estimation are discussed. A mixed indicative/corrective fault detection approach is proposed while it is demonstrated that reversible/irreversible state dependent drift can be estimated using generic algorithms such as the EKF or on-line density estimators. Finally, a parsimonious density estimator is presented and validated through simulated and real data for use in an operating regime dependent fault detection framework
Efficient state-space inference of periodic latent force models
Latent force models (LFM) are principled approaches to incorporating solutions to differen-tial equations within non-parametric inference methods. Unfortunately, the developmentand application of LFMs can be inhibited by their computational cost, especially whenclosed-form solutions for the LFM are unavailable, as is the case in many real world prob-lems where these latent forces exhibit periodic behaviour. Given this, we develop a newsparse representation of LFMs which considerably improves their computational efficiency,as well as broadening their applicability, in a principled way, to domains with periodic ornear periodic latent forces. Our approach uses a linear basis model to approximate onegenerative model for each periodic force. We assume that the latent forces are generatedfrom Gaussian process priors and develop a linear basis model which fully expresses thesepriors. We apply our approach to model the thermal dynamics of domestic buildings andshow that it is effective at predicting day-ahead temperatures within the homes. We alsoapply our approach within queueing theory in which quasi-periodic arrival rates are mod-elled as latent forces. In both cases, we demonstrate that our approach can be implemented efficiently using state-space methods which encode the linear dynamic systems via LFMs.Further, we show that state estimates obtained using periodic latent force models can re-duce the root mean squared error to 17% of that from non-periodic models and 27% of thenearest rival approach which is the resonator model (S ̈arkk ̈a et al., 2012; Hartikainen et al.,2012.
Two-stage space construction for real-time modeling of distributed parameter systems under sparse sensing
Numerous industrial processes can be defined using distributed parameter
systems (DPSs). This study introduces a two-stage spatial construction approach
for real-time modeling of DPSs in cases of limited sensors. Initially, a
discrete space-completion approach is created to recuperate the spatiotemporal
patterns of non-monitored locations under sparse sensing. The high-dimensional
space construction method is employed to derive continuous spatial basis
functions (SBFs). The identification and adjustment of the nonlinear temporal
model are carried out via the long short-term memory (LSTM) neural network.
Eventually, the amalgamation of the derived SBFs and temporal model results in
a spatially continuous model. The use of a cubic B-spline surface is validated
as an effective solution for optimizing space construction in the sense of
least squares approximation. Experimental tests conducted on a pouch-type
Li-ion battery demonstrate the efficacy of the proposed modeling technique
under sparse sensing. This work highlights the promise of sparse sensors in
real-time full-space modeling for large-scale battery energy storage systems
Solcore: A multi-scale, python-based library for modelling solar cells and semiconductor materials
Computational models can provide significant insight into the operation
mechanisms and deficiencies of photovoltaic solar cells. Solcore is a modular
set of computational tools, written in Python 3, for the design and simulation
of photovoltaic solar cells. Calculations can be performed on ideal,
thermodynamic limiting behaviour, through to fitting experimentally accessible
parameters such as dark and light IV curves and luminescence. Uniquely, it
combines a complete semiconductor solver capable of modelling the optical and
electrical properties of a wide range of solar cells, from quantum well devices
to multi-junction solar cells. The model is a multi-scale simulation accounting
for nanoscale phenomena such as the quantum confinement effects of
semiconductor nanostructures, to micron level propagation of light through to
the overall performance of solar arrays, including the modelling of the
spectral irradiance based on atmospheric conditions. In this article we
summarize the capabilities in addition to providing the physical insight and
mathematical formulation behind the software with the purpose of serving as
both a research and teaching tool.Comment: 25 pages, 18 figures, Journal of Computational Electronics (2018
Smart Fridge / Dumb Grid? Demand Dispatch for the Power Grid of 2020
In discussions at the 2015 HICSS meeting, it was argued that loads can
provide most of the ancillary services required today and in the future.
Through load-level and grid-level control design, high-quality ancillary
service for the grid is obtained without impacting quality of service delivered
to the consumer. This approach to grid regulation is called demand dispatch:
loads are providing service continuously and automatically, without consumer
interference.
In this paper we ask, what intelligence is required at the grid-level? In
particular, does the grid-operator require more than one-way communication to
the loads? Our main conclusion: risk is not great in lower frequency ranges,
e.g., PJM's RegA or BPA's balancing reserves. In particular, ancillary services
from refrigerators and pool-pumps can be obtained successfully with only
one-way communication. This requires intelligence at the loads, and much less
intelligence at the grid level
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