349 research outputs found
User Attraction via Wireless Charging in Cellular Networks
A strong motivation of charging depleted battery can be an enabler for
network capacity increase. In this light we propose a spatial attraction
cellular network (SAN) consisting of macro cells overlaid with small cell base
stations that wirelessly charge user batteries. Such a network makes battery
depleting users move toward the vicinity of small cell base stations. With a
fine adjustment of charging power, this user spatial attraction (SA) improves
in spectral efficiency as well as load balancing. We jointly optimize both
enhancements thanks to SA, and derive the corresponding optimal charging power
in a closed form by using a stochastic geometric approach.Comment: to be presented in IEEE International Symposium on Modeling and
Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt) Workshop on
Green Networks (GREENNET) 2016, Arizona, USA (8 pages, 4 figures
Live Prefetching for Mobile Computation Offloading
The conventional designs of mobile computation offloading fetch user-specific
data to the cloud prior to computing, called offline prefetching. However, this
approach can potentially result in excessive fetching of large volumes of data
and cause heavy loads on radio-access networks. To solve this problem, the
novel technique of live prefetching is proposed in this paper that seamlessly
integrates the task-level computation prediction and prefetching within the
cloud-computing process of a large program with numerous tasks. The technique
avoids excessive fetching but retains the feature of leveraging prediction to
reduce the program runtime and mobile transmission energy. By modeling the
tasks in an offloaded program as a stochastic sequence, stochastic optimization
is applied to design fetching policies to minimize mobile energy consumption
under a deadline constraint. The policies enable real-time control of the
prefetched-data sizes of candidates for future tasks. For slow fading, the
optimal policy is derived and shown to have a threshold-based structure,
selecting candidate tasks for prefetching and controlling their prefetched data
based on their likelihoods. The result is extended to design close-to-optimal
prefetching policies to fast fading channels. Compared with fetching without
prediction, live prefetching is shown theoretically to always achieve reduction
on mobile energy consumption.Comment: To appear in IEEE Trans. on Wireless Communicatio
Mobility-Induced Graph Learning for WiFi Positioning
A smartphone-based user mobility tracking could be effective in finding
his/her location, while the unpredictable error therein due to low
specification of built-in inertial measurement units (IMUs) rejects its
standalone usage but demands the integration to another positioning technique
like WiFi positioning. This paper aims to propose a novel integration technique
using a graph neural network called Mobility-INduced Graph LEarning (MINGLE),
which is designed based on two types of graphs made by capturing different user
mobility features. Specifically, considering sequential measurement points
(MPs) as nodes, a user's regular mobility pattern allows us to connect neighbor
MPs as edges, called time-driven mobility graph (TMG). Second, a user's
relatively straight transition at a constant pace when moving from one position
to another can be captured by connecting the nodes on each path, called a
direction-driven mobility graph (DMG). Then, we can design graph convolution
network (GCN)-based cross-graph learning, where two different GCN models for
TMG and DMG are jointly trained by feeding different input features created by
WiFi RTTs yet sharing their weights. Besides, the loss function includes a
mobility regularization term such that the differences between adjacent
location estimates should be less variant due to the user's stable moving pace.
Noting that the regularization term does not require ground-truth location,
MINGLE can be designed under semi- and self-supervised learning frameworks. The
proposed MINGLE's effectiveness is extensively verified through field
experiments, showing a better positioning accuracy than benchmarks, say root
mean square errors (RMSEs) being 1.398 (m) and 1.073 (m) for self- and
semi-supervised learning cases, respectively.Comment: submitted to a possible IEEE journa
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