2,008 research outputs found
Compression and Conditional Emulation of Climate Model Output
Numerical climate model simulations run at high spatial and temporal
resolutions generate massive quantities of data. As our computing capabilities
continue to increase, storing all of the data is not sustainable, and thus it
is important to develop methods for representing the full datasets by smaller
compressed versions. We propose a statistical compression and decompression
algorithm based on storing a set of summary statistics as well as a statistical
model describing the conditional distribution of the full dataset given the
summary statistics. The statistical model can be used to generate realizations
representing the full dataset, along with characterizations of the
uncertainties in the generated data. Thus, the methods are capable of both
compression and conditional emulation of the climate models. Considerable
attention is paid to accurately modeling the original dataset--one year of
daily mean temperature data--particularly with regard to the inherent spatial
nonstationarity in global fields, and to determining the statistics to be
stored, so that the variation in the original data can be closely captured,
while allowing for fast decompression and conditional emulation on modest
computers
Cross-layer design for single-cell OFDMA systems with heterogeneous QoS and partial CSIT
Abstract— This paper proposes a novel cross-layer scheduling scheme for a single-cell orthogonal frequency division multiple access (OFDMA) wireless system with partial channel state information (CSI) at transmitter (CSIT) and heterogeneous user delay requirements. Previous research efforts on OFDMA resource allocation are typically based on the availability of perfect CSI or imperfect CSI but with small error variance. Either case consists to typify a non tangible system as the potential facts of channel feedback delay or large channel estimation errors have not been considered. Thus, to attain a more realistic resolution our cross-layer design determines optimal subcarrier and power allocation policies based on partial CSIT and individual user’s quality of service (QoS) requirements. The simulation results show that the proposed cross-layer scheduler can maximize the system’s throughput and at the same time satisfy heterogeneous delay requirements of various users with significant low power consumption
Efficient Radio Resource Allocation Schemes and Code Optimizations for High Speed Downlink Packet Access Transmission
An important enhancement on the Wideband Code Division Multiple Access
(WCDMA) air interface of the 3G mobile communications, High Speed Downlink
Packet Access (HSDPA) standard has been launched to realize higher spectral
utilization efficiency. It introduces the features of multicode CDMA transmission
and Adaptive Modulation and Coding (AMC) technique, which makes radio resource
allocation feasible and essential. This thesis studies channel-aware resource
allocation schemes, coupled with fast power adjustment and spreading code optimization
techniques, for the HSDPA standard operating over frequency selective
channel.
A two-group resource allocation scheme is developed in order to achieve a
promising balance between performance enhancement and time efficiency. It only
requires calculating two parameters to specify the allocations of discrete bit rates
and transmitted symbol energies in all channels. The thesis develops the calculation
methods of the two parameters for interference-free and interference-present
channels, respectively. For the interference-present channels, the performance of
two-group allocation can be further enhanced by applying a clustering-based channel
removal scheme.
In order to make the two-group approach more time-efficient, reduction in
matrix inversions in optimum energy calculation is then discussed. When the
Minimum Mean Square Error (MMSE) equalizer is applied, optimum energy allocation
can be calculated by iterating a set of eigenvalues and eigenvectors. By
using the MMSE Successive Interference Cancellation (SIC) receiver, the optimum
energies are calculated recursively combined with an optimum channel ordering
scheme for enhancement in both system performance and time efficiency.
This thesis then studies the signature optimization methods with multipath
channel and examines their system performances when combined with different
resource allocation methods. Two multipath-aware signature optimization methods
are developed by applying iterative optimization techniques, for the system
using MMSE equalizer and MMSE precoder respectively. A PAM system using
complex signature sequences is also examined for improving resource utilization
efficiency, where two receiving schemes are proposed to fully take advantage of
PAM features. In addition by applying a short chip sampling window, a Singular
Value Decomposition (SVD) based interference-free signature design method is
presented
The Application of Nature-inspired Metaheuristic Methods for Optimising Renewable Energy Problems and the Design of Water Distribution Networks
This work explores the technical challenges that emerge when applying bio-inspired optimisation methods to real-world engineering problems. A number of new heuristic algorithms were proposed and tested to deal with these challenges. The work is divided into three main dimensions: i) One of the most significant industrial optimisation problems is optimising renewable energy systems. Ocean wave energy is a promising technology for helping to meet future growth in global energy demand. However, the current technologies of wave energy converters (WECs) are not fully developed because of technical engineering and design challenges. This work proposes new hybrid heuristics consisting of cooperative coevolutionary frameworks and neuro-surrogate optimisation methods for optimising WECs problem in three domains, including position, control parameters, and geometric parameters. Our problem-specific algorithms perform better than existing approaches in terms of higher quality results and the speed of convergence. ii) The second part applies search methods to the optimization of energy output in wind farms. Wind energy has key advantages in terms of technological maturity, cost, and life-cycle greenhouse gas emissions. However, designing an accurate local wind speed and power prediction is challenging. We propose two models for wind speed and power forecasting for two wind farms located in Sweden and the Baltic Sea by a combination of recurrent neural networks and evolutionary search algorithms. The proposed models are superior to other applied machine learning methods. iii) Finally, we investigate the design of water distribution systems (WDS) as another challenging real-world optimisation problem. WDS optimisation is demanding because it has a high-dimensional discrete search space and complex constraints. A hybrid evolutionary algorithm is suggested for minimising the cost of various water distribution networks and for speeding up the convergence rate of search.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202
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