4,044 research outputs found
How Much Frequency Can Be Reused in 5G Cellular Networks---A Matrix Graph Model
The 5th Generation cellular network may have the key feature of smaller cell
size and denser resource employment, resulted from diminishing resource and
increasing communication demands. However, small cell may result in high
interference between cells. Moreover, the random geographic patterns of small
cell networks make them hard to analyze, at least excluding schemes in the
well-accepted hexagonal grid model. In this paper, a new model---the matrix
graph is proposed which takes advantage of the small cell size and high
inter-cell interference to reduce computation complexity. This model can
simulate real world networks accurately and offers convenience in frequency
allocation problems which are usually NP-complete. An algorithm dealing with
this model is also given, which asymptotically achieves the theoretical limit
of frequency allocation, and has a complexity which decreases with cell size
and grows linearly with the network size. This new model is specifically
proposed to characterize the next-generation cellular networks.Comment: 12 page
Age-Optimal Trajectory Planning for UAV-Assisted Data Collection
Unmanned aerial vehicle (UAV)-aided data collection is a new and promising
application in many practical scenarios. In this work, we study the age-optimal
trajectory planning problem in UAV-enabled wireless sensor networks, where a
UAV is dispatched to collect data from the ground sensor nodes (SNs). The age
of information (AoI) collected from each SN is characterized by the data
uploading time and the time elapsed since the UAV leaves this SN. We attempt to
design two age-optimal trajectories, referred to as the Max-AoI-optimal and
Ave-AoI-optimal trajectories, respectively. The Max-AoI-optimal trajectory
planning is to minimize the age of the `oldest' sensed information among the
SNs. The Ave-AoI-optimal trajectory planning is to minimize the average AoI of
all the SNs. Then, we show that each age-optimal flight trajectory corresponds
to a shortest Hamiltonian path in the wireless sensor network where the
distance between any two SNs represents the amount of inter-visit time. The
dynamic programming (DP) method and genetic algorithm (GA) are adopted to find
the two different age-optimal trajectories. Simulation results validate the
effectiveness of the proposed methods, and show how the UAV's trajectory is
affected by the two AoI metrics.Comment: IEEE Infocom 2018 - 1st Workshop on Age of Informatio
Coded Caching in Fog-RAN: b-Matching Approach
Fog radio access network (Fog-RAN), which pushes the caching and computing
capabilities to the network edge, is capable of efficiently delivering contents
to users by using carefully designed caching placement and content replacement
algorithms. In this paper, the transmission scheme design and coding parameter
optimization will be considered for coded caching in Fog-RAN, where the
reliability of content delivery, i.e., content outage probability, is used as
the performance metric. The problem will be formulated as a complicated
multi-objective probabilistic combinatorial optimization. A novel maximum
b-matching approach will then be proposed to obtain the Pareto optimal solution
with fairness constraint. Based on the fast message passing approach, a
distributed algorithm with a low memory usage of O(M + N) is also proposed,
where M is the number of users and N is the number of Fog-APs. Although it is
usually very difficult to derive the closed-form formulas for the optimal
solution, the approximation formulas of the content outage probability will
also be obtained as a function of coding parameters. The asymptotic optimal
coding parameters can then be obtained by defining and deriving the outage
exponent region (OER) and diversity-multiplexing region (DMR). Simulation
results will illustrate the accuracy of the theoretical derivations, and verify
the outage performance of the proposed approach. Therefore, this paper not only
proposes a practical distributed Fog-AP selection algorithm for coded caching,
but also provides a systematic way to evaluate and optimize the performance of
Fog-RANs.Comment: to appear in IEEE TRANSACTIONS ON COMMUNICATION
An Outage Exponent Region based Coded f-Matching Framework for Channel Allocation in Multi-carrier Multi-access Channels
The multi-carrier multi-access technique is widely adopt in future wireless
communication systems, such as IEEE 802.16m and 3GPP LTE-A. The channel
resources allocation in multi-carrier multi-access channel, which can greatly
improve the system throughput with QoS assurance, thus attracted much attention
from both academia and industry. There lacks, however, an analytic framework
with a comprehensive performance metric, such that it is difficult to fully
exploit the potentials of channel allocation. This paper will propose an
analytic coded fmatching framework, where the outage exponent region (OER) will
be defined as the performance metric. The OER determines the relationship of
the outage performance among all of the users in the full SNR range, and
converges to the diversity-multiplexing region (DMR) when SNR tends to
infinity. To achieve the optimal OER and DMR, the random bipartite graph (RBG)
approach, only depending on 1 bit CSI, will be proposed to formulate this
problem. Based on the RBG formulation, the optimal frequency-domain coding
based maximum f-matching method is then proposed. By analyzing the
combinatorial structure of the RBG based coded f-matching with the help of
saddlepoint approximation, the outage probability of each user, OER, and DMR
will be derived in closed-form formulas. It will be shown that all of the users
share the total multiplexing gain according to their rate requirements, while
achieving the full frequency diversity, i.e., the optimal OER and DMR. Based on
the principle of parallel computations, the parallel vertices expansion &
random rotation based Hopcroft-Karp (PVER2HK) algorithm, which enjoys a
logarithmic polynomial complexity, will be proposed. The simulation results
will not only verify the theoretical derivations, but also show the significant
performance gains.Comment: 19pages, 13 figure
Cache Placement in Fog-RANs: From Centralized to Distributed Algorithms
To deal with the rapid growth of high-speed and/or ultra-low latency data
traffic for massive mobile users, fog radio access networks (Fog-RANs) have
emerged as a promising architecture for next-generation wireless networks. In
Fog-RANs, the edge nodes and user terminals possess storage, computation and
communication functionalities to various degrees, which provides high
flexibility for network operation, i.e., from fully centralized to fully
distributed operation. In this paper, we study the cache placement problem in
Fog-RANs, by taking into account flexible physical-layer transmission schemes
and diverse content preferences of different users. We develop both centralized
and distributed transmission aware cache placement strategies to minimize
users' average download delay subject to the storage capacity constraints. In
the centralized mode, the cache placement problem is transformed into a matroid
constrained submodular maximization problem, and an approximation algorithm is
proposed to find a solution within a constant factor to the optimum. In the
distributed mode, a belief propagation based distributed algorithm is proposed
to provide a suboptimal solution, with iterative updates at each BS based on
locally collected information. Simulation results show that by exploiting
caching and cooperation gains, the proposed transmission aware caching
algorithms can greatly reduce the users' average download delay.Comment: 13 pages, 10 figure
Outage Exponent: A Unified Performance Metric for Parallel Fading Channels
The parallel fading channel, which consists of finite number of subchannels,
is very important, because it can be used to formulate many practical
communication systems. The outage probability, on the other hand, is widely
used to analyze the relationship among the communication efficiency,
reliability, SNR, and channel fading. To the best of our knowledge, the
previous works only studied the asymptotic outage performance of the parallel
fading channel which are only valid for a large number of subchannels or high
SNRs. In this paper, a unified performance metric, which we shall refer to as
the outage exponent, will be proposed. Our approach is mainly based on the
large deviations theory and the Meijer's G-function. It is shown that the
proposed outage exponent is not only an accurate estimation of the outage
probability for any number of subchannels, any SNR, and any target transmission
rate, but also provides an easy way to compute the outage capacity, finite-SNR
diversity-multiplexing tradeoff, and SNR gain. The asymptotic performance
metrics, such as the delay-limited capacity, ergodic capacity, and
diversity-multiplexing tradeoff can be directly obtained by letting the number
of subchannels or SNR tends to infinity. Similar to Gallager's error exponent,
a reliable function for parallel fading channels, which illustrates a
fundamental relationship between the transmission reliability and efficiency,
can also be defined from the outage exponent. Therefore, the proposed outage
exponent provides a complete and comprehensive performance measure for parallel
fading channels.Comment: 19 pages, 10 figure
CNN Feature boosted SeqSLAM for Real-Time Loop Closure Detection
Loop closure detection (LCD) is an indispensable part of simultaneous
localization and mapping systems (SLAM); it enables robots to produce a
consistent map by recognizing previously visited places. When robots operate
over extended periods, robustness to viewpoint and condition changes as well as
satisfactory real-time performance become essential requirements for a
practical LCD system.
This paper presents an approach to directly utilize the outputs at the
intermediate layer of a pre-trained convolutional neural network (CNN) as image
descriptors. The matching location is determined by matching the image
sequences through a method called SeqCNNSLAM. The utility of SeqCNNSLAM is
comprehensively evaluated in terms of viewpoint and condition invariance.
Experiments show that SeqCNNSLAM outperforms state-of-the-art LCD systems, such
as SeqSLAM and Change Removal, in most cases. To allow for the real-time
performance of SeqCNNSLAM, an acceleration method, A-SeqCNNSLAM, is
established. This method exploits the location relationship between the
matching images of adjacent images to reduce the matching range of the current
image. Results demonstrate that acceleration of 4-6 is achieved with minimal
accuracy degradation, and the method's runtime satisfies the real-time demand.
To extend the applicability of A-SeqCNNSLAM to new environments, a method
called O-SeqCNNSLAM is established for the online adjustment of the parameters
of A-SeqCNNSLAM
Learning to Generate Structured Queries from Natural Language with Indirect Supervision
Generating structured query language (SQL) from natural language is an
emerging research topic. This paper presents a new learning paradigm from
indirect supervision of the answers to natural language questions, instead of
SQL queries. This paradigm facilitates the acquisition of training data due to
the abundant resources of question-answer pairs for various domains in the
Internet, and expels the difficult SQL annotation job. An end-to-end neural
model integrating with reinforcement learning is proposed to learn SQL
generation policy within the answer-driven learning paradigm. The model is
evaluated on datasets of different domains, including movie and academic
publication. Experimental results show that our model outperforms the baseline
models.Comment: 11 pages, 4 figure
Improving the staggered grid Lagrangian hydrodynamics for modeling multi-material flows
In this work, we make two improvements on the staggered grid hydrodynamics
(SGH) Lagrangian scheme for modeling 2-dimensional compressible multi-material
flows on triangular mesh. The first improvement is the construction of a
dynamic local remeshing scheme for preventing mesh distortion. The remeshing
scheme is similar to many published algorithms except that it introduces some
special operations for treating grids around multi-material interfaces. This
makes the simulation of extremely deforming and topology-variable
multi-material processes possible, such as the complete process of a heavy
fluid dipping into a light fluid. The second improvement is the construction of
an Euler-like flow on each edge of the mesh to count for the "edge-bending"
effect, so as to mitigate the "checkerboard" oscillation that commonly exists
in Lagrangian simulations, especially the triangular mesh based simulations.
Several typical hydrodynamic problems are simulated by the improved staggered
grid Lagrangian hydrodynamic method to test its performance.Comment: 33 pages, 25 figure
Large-Scale Convex Optimization for Ultra-Dense Cloud-RAN
The heterogeneous cloud radio access network (Cloud-RAN) provides a
revolutionary way to densify radio access networks. It enables centralized
coordination and signal processing for efficient interference management and
flexible network adaptation. Thus, it can resolve the main challenges for
next-generation wireless networks, including higher energy efficiency and
spectral efficiency, higher cost efficiency, scalable connectivity, and low
latency. In this article, we shall provide an algorithmic thinking on the new
design challenges for the dense heterogeneous Cloud-RAN based on convex
optimization. As problem sizes scale up with the network size, we will
demonstrate that it is critical to take unique structures of design problems
and inherent characteristics of wireless channels into consideration, while
convex optimization will serve as a powerful tool for such purposes. Network
power minimization and channel state information acquisition will be used as
two typical examples to demonstrate the effectiveness of convex optimization
methods. We will then present a two-stage framework to solve general
large-scale convex optimization problems, which is amenable to parallel
implementation in the cloud data center.Comment: to appear in IEEE Wireless Commun. Mag., June 201
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