243 research outputs found
Convergence Analysis of Mixed Timescale Cross-Layer Stochastic Optimization
This paper considers a cross-layer optimization problem driven by
multi-timescale stochastic exogenous processes in wireless communication
networks. Due to the hierarchical information structure in a wireless network,
a mixed timescale stochastic iterative algorithm is proposed to track the
time-varying optimal solution of the cross-layer optimization problem, where
the variables are partitioned into short-term controls updated in a faster
timescale, and long-term controls updated in a slower timescale. We focus on
establishing a convergence analysis framework for such multi-timescale
algorithms, which is difficult due to the timescale separation of the algorithm
and the time-varying nature of the exogenous processes. To cope with this
challenge, we model the algorithm dynamics using stochastic differential
equations (SDEs) and show that the study of the algorithm convergence is
equivalent to the study of the stochastic stability of a virtual stochastic
dynamic system (VSDS). Leveraging the techniques of Lyapunov stability, we
derive a sufficient condition for the algorithm stability and a tracking error
bound in terms of the parameters of the multi-timescale exogenous processes.
Based on these results, an adaptive compensation algorithm is proposed to
enhance the tracking performance. Finally, we illustrate the framework by an
application example in wireless heterogeneous network
Effect of Aspect Ratio on the Flow Structures Behind a Square Cylinder
In this thesis, the effect of aspect ratio on the flow past square cross-section wall-mounted cylinders is evaluated using computational fluid dynamics. The simulations are carried out using the Improved Delayed Detached Eddy (IDDES) turbulence model. Three cases with different heights of the cylinder (aspect ratio = cylinder height/width = 1, 2, and 4) were studied. The IDDES prediction of the flow statistics is validated against a set of wind tunnel experimental results from a recent report on the flow at a Reynolds number of 12,000 for a cylinder aspect ratio of four. It is common practise to analyse results in different horizontal and vertical planes in the wake of the bluff body. To this end, the traditional methods use a geometrical scaling factor such as the height/diameter of the cylinder or depth of flow. However, this can lead to an improper analysis as one may not capture the flow properties based on the physics of the flow. The flow characteristics can be influenced by both the proximity to the bed and to the cylinder’s free-end. In this thesis, a new method, based on the flow physics, is proposed to evaluate the role of aspect ratio using the forebody pressure distribution. Using the turbulence features and vortex identification methods, it is observed that the flow structure is influenced by the aspect ratio. The downwash flow noticed in the wake tends to become less dominant with increasing aspect ratio, accompanied by a near-bed upwash flow at the rear of the cylinder. The mean and instantaneous flow field characteristics at each aspect ratio has been examined and compared in different planes to elucidate their three-dimensional features. The far-wake of each flow field is visualized and examined using the three-dimensional iso-surface of the λ2 criterion
A Fully Convolutional Tri-branch Network (FCTN) for Domain Adaptation
A domain adaptation method for urban scene segmentation is proposed in this
work. We develop a fully convolutional tri-branch network, where two branches
assign pseudo labels to images in the unlabeled target domain while the third
branch is trained with supervision based on images in the pseudo-labeled target
domain. The re-labeling and re-training processes alternate. With this design,
the tri-branch network learns target-specific discriminative representations
progressively and, as a result, the cross-domain capability of the segmenter
improves. We evaluate the proposed network on large-scale domain adaptation
experiments using both synthetic (GTA) and real (Cityscapes) images. It is
shown that our solution achieves the state-of-the-art performance and it
outperforms previous methods by a significant margin.Comment: Accepted by ICASSP 201
Distributive Network Utility Maximization (NUM) over Time-Varying Fading Channels
Distributed network utility maximization (NUM) has received an increasing
intensity of interest over the past few years. Distributed solutions (e.g., the
primal-dual gradient method) have been intensively investigated under fading
channels. As such distributed solutions involve iterative updating and explicit
message passing, it is unrealistic to assume that the wireless channel remains
unchanged during the iterations. Unfortunately, the behavior of those
distributed solutions under time-varying channels is in general unknown. In
this paper, we shall investigate the convergence behavior and tracking errors
of the iterative primal-dual scaled gradient algorithm (PDSGA) with dynamic
scaling matrices (DSC) for solving distributive NUM problems under time-varying
fading channels. We shall also study a specific application example, namely the
multi-commodity flow control and multi-carrier power allocation problem in
multi-hop ad hoc networks. Our analysis shows that the PDSGA converges to a
limit region rather than a single point under the finite state Markov chain
(FSMC) fading channels. We also show that the order of growth of the tracking
errors is given by O(T/N), where T and N are the update interval and the
average sojourn time of the FSMC, respectively. Based on this analysis, we
derive a low complexity distributive adaptation algorithm for determining the
adaptive scaling matrices, which can be implemented distributively at each
transmitter. The numerical results show the superior performance of the
proposed dynamic scaling matrix algorithm over several baseline schemes, such
as the regular primal-dual gradient algorithm
Geography-aware Optimal UAV 3D Placement for LOS Relaying: A Geometry Approach
Many emerging technologies for the next generation wireless network prefer
line-of-sight (LOS) propagation conditions to fully release their performance
advantages. This paper studies 3D unmanned aerial vehicle (UAV) placement to
establish LOS links for two ground terminals in deep shadow in a dense urban
environment. The challenge is that the LOS region for the feasible UAV
positions can be arbitrary due to the complicated structure of the environment.
While most existing works rely on simplified stochastic LOS models and problem
relaxations, this paper focuses on establishing theoretical guarantees for the
optimal UAV placement to ensure LOS conditions for two ground users in an
actual propagation environment. It is found that it suffices to search a
bounded 2D area for the globally optimal 3D UAV position. Thus, this paper
develops an exploration-exploitation algorithm with a linear trajectory length
and achieves above 99% global optimality over several real city environments
being tested in our experiments. To further enhance the search capability in an
ultra-dense environment, a dynamic multi-stage algorithm is developed and
theoretically shown to find an -optimal UAV position with a search
length . Significant performance advantages are demonstrated in
several numerical experiments for wireless communication relaying and wireless
power transfer
Constructing Indoor Region-based Radio Map without Location Labels
Radio map construction requires a large amount of radio measurement data with
location labels, which imposes a high deployment cost. This paper develops a
region-based radio map from received signal strength (RSS) measurements without
location labels. The construction is based on a set of blindly collected RSS
measurement data from a device that visits each region in an indoor area
exactly once, where the footprints and timestamps are not recorded. The main
challenge is to cluster the RSS data and match clusters with the physical
regions. Classical clustering algorithms fail to work as the RSS data naturally
appears as non-clustered due to multipaths and noise. In this paper, a signal
subspace model with a sequential prior is constructed for the RSS data, and an
integrated segmentation and clustering algorithm is developed, which is shown
to find the globally optimal solution in a special case. Furthermore, the
clustered data is matched with the physical regions using a graph-based
approach. Based on real measurements from an office space, the proposed scheme
reduces the region localization error by roughly 50% compared to a weighted
centroid localization (WCL) baseline, and it even outperforms some supervised
localization schemes, including k-nearest neighbor (KNN), support vector
machine (SVM), and deep neural network (DNN), which require labeled data for
training
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