1,041 research outputs found
Common-reflection-surface imaging of shallow and ultrashallow reflectors
We analyzed the feasibility of the common-reflection-surface
(CRS) stack for near-surface surveys as an alternative to the conventional
common midpoint (CMP) stacking procedure. The
data-driven, less user-interactive CRS method could be more
cost efficient for shallow surveys, where the high sensitivity
to velocity analysis makes data processing a critical step. We
compared the results for two field data sets collected to image
shallow and ultrashallow reflectors: an example of shallow Pwave
reflection for targets in the first few hundred meters,
and an example of SH-wave reflection for targets in the first
10 m. By processing the shallow P-wave records using the
CMP method, we imaged several nearly horizontal reflectors
with onsets from 60 to about 250 ms. The CRS stack produced
a stacked section more suited for a subsurface interpretation,
without any preliminary formal and time-consuming velocity analysis, because the imaged reflectors possessed greater coherency
and lateral continuity. With CMP processing of the SHwave
records, we imaged a dipping bedrock interface below
four horizontal reflectors in unconsolidated, very low velocity
sediments. The vertical and lateral resolution was very high, despite
the very shallow depth: the image showed the pinchout of
two layers at less than 10 m depth. The numerous traces used by
the CRS stack improved the continuity of the shallowest reflector,
but the deepest overburden reflectors appear unresolved,
with not well-imaged pinchouts. Using the kinematic wavefield
attributes determined for each stacking operation, we retrieved
velocity fields fitting the stacking velocities we had estimated in
the CMP processing. The use of CRS stack could be a significant
step ahead to increase the acceptance of the seismic reflection
method as a routine investigation method in shallow and
ultrashallow seismics
Energy Efficient Scheduling for Loss Tolerant IoT Applications with Uninformed Transmitter
In this work we investigate energy efficient packet scheduling problem for
the loss tolerant applications. We consider slow fading channel for a point to
point connection with no channel state information at the transmitter side
(CSIT). In the absence of CSIT, the slow fading channel has an outage
probability associated with every transmit power. As a function of data loss
tolerance parameters and peak power constraints, we formulate an optimization
problem to minimize the average transmit energy for the user equipment (UE).
The optimization problem is not convex and we use stochastic optimization
technique to solve the problem. The numerical results quantify the effect of
different system parameters on average transmit power and show significant
power savings for the loss tolerant applications.Comment: Published in ICC 201
Minimizing Energy Consumption in MU-MIMO via Antenna Muting by Neural Networks with Asymmetric Loss
Transmit antenna muting (TAM) in multiple-user multiple-input multiple-output
(MU-MIMO) networks allows reducing the power consumption of the base station
(BS) by properly utilizing only a subset of antennas in the BS. In this paper,
we consider the downlink transmission of an MU-MIMO network where TAM is
formulated to minimize the number of active antennas in the BS while
guaranteeing the per-user throughput requirements. To address the computational
complexity of the combinatorial optimization problem, we propose an algorithm
called neural antenna muting (NAM) with an asymmetric custom loss function. NAM
is a classification neural network trained in a supervised manner. The
classification error in this scheme leads to either sub-optimal energy
consumption or lower quality of service (QoS) for the communication link. We
control the classification error probability distribution by designing an
asymmetric loss function such that the erroneous classification outputs are
more likely to result in fulfilling the QoS requirements. Furthermore, we
present three heuristic algorithms and compare them with the NAM. Using a 3GPP
compliant system-level simulator, we show that NAM achieves energy
saving compared to the full antenna configuration in the BS with
reliability in achieving the user throughput requirements while being around
and less computationally intensive than the greedy
heuristic algorithm and the fixed column antenna muting algorithm,
respectively.Comment: Submitted to IEEE Transactions on Vehicular Technolog
Energy and bursty packet loss tradeoff over fading channels: a system-level model
Energy efficiency and quality of service (QoS) guarantees are the key design goals for the 5G wireless communication systems. In this context, we discuss a multiuser scheduling scheme over fading channels for loss tolerant applications. The loss tolerance of the application is characterized in terms of different parameters that contribute to quality of experience (QoE) for the application. The mobile users are scheduled opportunistically such that a minimum QoS is guaranteed. We propose an opportunistic scheduling scheme and address the cross-layer design framework when channel state information (CSI) is not perfectly available at the transmitter and the receiver. We characterize the system energy as a function of different QoS and channel state estimation error parameters. The optimization problem is formulated using Markov chain framework and solved using stochastic optimization techniques. The results demonstrate that the parameters characterizing the packet loss are tightly coupled and relaxation of one parameter does not benefit the system much if the other constraints are tight. We evaluate the energy-performance tradeoff numerically and show the effect of channel uncertainty on the packet scheduler design
Leveraging intelligence from network CDR data for interference aware energy consumption minimization
Cell densification is being perceived as the panacea for the imminent capacity crunch. However, high aggregated energy consumption and increased inter-cell interference (ICI) caused by densification, remain the two long-standing problems. We propose a novel network orchestration solution for simultaneously minimizing energy consumption and ICI in ultra-dense 5G networks. The proposed solution builds on a big data analysis of over 10 million CDRs from a real network that shows there exists strong spatio-temporal predictability in real network traffic patterns. Leveraging this we develop a novel scheme to pro-actively schedule radio resources and small cell sleep cycles yielding substantial energy savings and reduced ICI, without compromising the users QoS. This scheme is derived by formulating a joint Energy Consumption and ICI minimization problem and solving it through a combination of linear binary integer programming, and progressive analysis based heuristic algorithm. Evaluations using: 1) a HetNet deployment designed for Milan city where big data analytics are used on real CDRs data from the Telecom Italia network to model traffic patterns, 2) NS-3 based Monte-Carlo simulations with synthetic Poisson traffic show that, compared to full frequency reuse and always on approach, in best case, proposed scheme can reduce energy consumption in HetNets to 1/8th while providing same or better Qo
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