3,624 research outputs found
Fast Cell Discovery in mm-wave 5G Networks with Context Information
The exploitation of mm-wave bands is one of the key-enabler for 5G mobile
radio networks. However, the introduction of mm-wave technologies in cellular
networks is not straightforward due to harsh propagation conditions that limit
the mm-wave access availability. Mm-wave technologies require high-gain antenna
systems to compensate for high path loss and limited power. As a consequence,
directional transmissions must be used for cell discovery and synchronization
processes: this can lead to a non-negligible access delay caused by the
exploration of the cell area with multiple transmissions along different
directions.
The integration of mm-wave technologies and conventional wireless access
networks with the objective of speeding up the cell search process requires new
5G network architectural solutions. Such architectures introduce a functional
split between C-plane and U-plane, thereby guaranteeing the availability of a
reliable signaling channel through conventional wireless technologies that
provides the opportunity to collect useful context information from the network
edge.
In this article, we leverage the context information related to user
positions to improve the directional cell discovery process. We investigate
fundamental trade-offs of this process and the effects of the context
information accuracy on the overall system performance. We also cope with
obstacle obstructions in the cell area and propose an approach based on a
geo-located context database where information gathered over time is stored to
guide future searches. Analytic models and numerical results are provided to
validate proposed strategies.Comment: 14 pages, submitted to IEEE Transaction on Mobile Computin
Acoustical Ranging Techniques in Embedded Wireless Sensor Networked Devices
Location sensing provides endless opportunities for a wide range of applications in GPS-obstructed environments;
where, typically, there is a need for higher degree of accuracy. In this article, we focus on robust range
estimation, an important prerequisite for fine-grained localization. Motivated by the promise of acoustic in
delivering high ranging accuracy, we present the design, implementation and evaluation of acoustic (both
ultrasound and audible) ranging systems.We distill the limitations of acoustic ranging; and present efficient
signal designs and detection algorithms to overcome the challenges of coverage, range, accuracy/resolution,
tolerance to Doppler’s effect, and audible intensity. We evaluate our proposed techniques experimentally on
TWEET, a low-power platform purpose-built for acoustic ranging applications. Our experiments demonstrate
an operational range of 20 m (outdoor) and an average accuracy 2 cm in the ultrasound domain. Finally,
we present the design of an audible-range acoustic tracking service that encompasses the benefits of a near-inaudible
acoustic broadband chirp and approximately two times increase in Doppler tolerance to achieve better performance
Sparse Localization with a Mobile Beacon Based on LU Decomposition in Wireless Sensor Networks
Node localization is the core in wireless sensor network. It can be solved by powerful beacons, which are equipped with global positioning system devices to know their location information. In this article, we present a novel sparse localization approach with a mobile beacon based on LU decomposition. Our scheme firstly translates node localization problem into a 1-sparse vector recovery problem by establishing sparse localization model. Then, LU decomposition pre-processing is adopted to solve the problem that measurement matrix does not meet the re¬stricted isometry property. Later, the 1-sparse vector can be exactly recovered by compressive sensing. Finally, as the 1-sparse vector is approximate sparse, weighted Cen¬troid scheme is introduced to accurately locate the node. Simulation and analysis show that our scheme has better localization performance and lower requirement for the mobile beacon than MAP+GC, MAP-M, and MAP-M&N schemes. In addition, the obstacles and DOI have little effect on the novel scheme, and it has great localization performance under low SNR, thus, the scheme proposed is robust
Direction finding in sensors model based automatic modulation classification
In this paper, the RSSI testing as well the Angle of Arrival (AoA) have been examined for position prediction also produce the front specified composition of the possibility distribution of the location of a sensor node. "Multiple Signal Classification" (MUSIC) defined as a popular "Eigen" construction approach with large declaration, which broadly utilized for predicting the total of waveforms, as well their corners of arrival. In this research an examination of the ability to development of part of key specifications of the "MUSIC" technique has been presented, which might improve the response of the prediction operation. The outcomes of the simulation of this approach point out that the position of the sensor node may be evaluated in a little time period values as well that the condition of the explanation is competitive beside last techniques
Multimodal Content Delivery for Geo-services
This thesis describes a body of work carried out over several research projects in the area of multimodal interaction for location-based services. Research in this area has progressed from using simulated mobile environments to demonstrate the visual modality, to the ubiquitous delivery of rich media using multimodal interfaces (geo- services). To effectively deliver these services, research focused on innovative solutions to real-world problems in a number of disciplines including geo-location, mobile spatial interaction, location-based services, rich media interfaces and auditory user interfaces. My original contributions to knowledge are made in the areas of multimodal interaction underpinned by advances in geo-location technology and supported by the proliferation of mobile device technology into modern life. Accurate positioning is a known problem for location-based services, contributions in the area of mobile positioning demonstrate a hybrid positioning technology for mobile devices that uses terrestrial beacons to trilaterate position. Information overload is an active concern for location-based applications that struggle to manage large amounts of data, contributions in the area of egocentric visibility that filter data based on field-of-view demonstrate novel forms of multimodal input. One of the more pertinent characteristics of these applications is the delivery or output modality employed (auditory, visual or tactile). Further contributions in the area of multimodal content delivery are made, where multiple modalities are used to deliver information using graphical user interfaces, tactile interfaces and more notably auditory user interfaces. It is demonstrated how a combination of these interfaces can be used to synergistically deliver context sensitive rich media to users - in a responsive way - based on usage scenarios that consider the affordance of the device, the geographical position and bearing of the device and also the location of the device
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