133 research outputs found
Exploiting Device-to-Device Communications to Enhance Spatial Reuse for Popular Content Downloading in Directional mmWave Small Cells
With the explosive growth of mobile demand, small cells in millimeter wave
(mmWave) bands underlying the macrocell networks have attracted intense
interest from both academia and industry. MmWave communications in the 60 GHz
band are able to utilize the huge unlicensed bandwidth to provide multiple Gbps
transmission rates. In this case, device-to-device (D2D) communications in
mmWave bands should be fully exploited due to no interference with the
macrocell networks and higher achievable transmission rates. In addition, due
to less interference by directional transmission, multiple links including D2D
links can be scheduled for concurrent transmissions (spatial reuse). With the
popularity of content-based mobile applications, popular content downloading in
the small cells needs to be optimized to improve network performance and
enhance user experience. In this paper, we develop an efficient scheduling
scheme for popular content downloading in mmWave small cells, termed PCDS
(popular content downloading scheduling), where both D2D communications in
close proximity and concurrent transmissions are exploited to improve
transmission efficiency. In PCDS, a transmission path selection algorithm is
designed to establish multi-hop transmission paths for users, aiming at better
utilization of D2D communications and concurrent transmissions. After
transmission path selection, a concurrent transmission scheduling algorithm is
designed to maximize the spatial reuse gain. Through extensive simulations
under various traffic patterns, we demonstrate PCDS achieves near-optimal
performance in terms of delay and throughput, and also superior performance
compared with other existing protocols, especially under heavy load.Comment: 12 pages, to appear in IEEE Transactions on Vehicular Technolog
Percutaneous Transvenous Embolization of Intracranial Dural Arteriovenous Fistulas with Detachable Coils and/or in Combination with Onyx
Traumatic Middle Meningeal Artery and Fistula Formation with the Cavernous Sinus and a Review of the Literature on Endovascular Management of Traumatic Carotid Cavernous Fistulas
Short-Term Traffic Forecasting Using High-Resolution Traffic Data
This paper develops a data-driven toolkit for traffic forecasting using
high-resolution (a.k.a. event-based) traffic data. This is the raw data
obtained from fixed sensors in urban roads. Time series of such raw data
exhibit heavy fluctuations from one time step to the next (typically on the
order of 0.1-1 second). Short-term forecasts (10-30 seconds into the future) of
traffic conditions are critical for traffic operations applications (e.g.,
adaptive signal control). But traffic forecasting tools in the literature deal
predominantly with 3-5 minute aggregated data, where the typical signal cycle
is on the order of 2 minutes. This renders such forecasts useless at the
operations level. To this end, we model the traffic forecasting problem as a
matrix completion problem, where the forecasting inputs are mapped to a higher
dimensional space using kernels. The formulation allows us to capture both
nonlinear dependencies between forecasting inputs and outputs but also allows
us to capture dependencies among the inputs. These dependencies correspond to
correlations between different locations in the network. We further employ
adaptive boosting to enhance the training accuracy and capture historical
patterns in the data. The performance of the proposed methods is verified using
high-resolution data obtained from a real-world traffic network in Abu Dhabi,
UAE. Our experimental results show that the proposed method outperforms other
state-of-the-art algorithms
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