9,916 research outputs found
Scaling-up Distributed Processing of Data Streams for Machine Learning
Emerging applications of machine learning in numerous areas involve
continuous gathering of and learning from streams of data. Real-time
incorporation of streaming data into the learned models is essential for
improved inference in these applications. Further, these applications often
involve data that are either inherently gathered at geographically distributed
entities or that are intentionally distributed across multiple machines for
memory, computational, and/or privacy reasons. Training of models in this
distributed, streaming setting requires solving stochastic optimization
problems in a collaborative manner over communication links between the
physical entities. When the streaming data rate is high compared to the
processing capabilities of compute nodes and/or the rate of the communications
links, this poses a challenging question: how can one best leverage the
incoming data for distributed training under constraints on computing
capabilities and/or communications rate? A large body of research has emerged
in recent decades to tackle this and related problems. This paper reviews
recently developed methods that focus on large-scale distributed stochastic
optimization in the compute- and bandwidth-limited regime, with an emphasis on
convergence analysis that explicitly accounts for the mismatch between
computation, communication and streaming rates. In particular, it focuses on
methods that solve: (i) distributed stochastic convex problems, and (ii)
distributed principal component analysis, which is a nonconvex problem with
geometric structure that permits global convergence. For such methods, the
paper discusses recent advances in terms of distributed algorithmic designs
when faced with high-rate streaming data. Further, it reviews guarantees
underlying these methods, which show there exist regimes in which systems can
learn from distributed, streaming data at order-optimal rates.Comment: 45 pages, 9 figures; preprint of a journal paper published in
Proceedings of the IEEE (Special Issue on Optimization for Data-driven
Learning and Control
Video Streaming in Distributed Erasure-coded Storage Systems: Stall Duration Analysis
The demand for global video has been burgeoning across industries. With the
expansion and improvement of video-streaming services, cloud-based video is
evolving into a necessary feature of any successful business for reaching
internal and external audiences. This paper considers video streaming over
distributed systems where the video segments are encoded using an erasure code
for better reliability thus being the first work to our best knowledge that
considers video streaming over erasure-coded distributed cloud systems. The
download time of each coded chunk of each video segment is characterized and
ordered statistics over the choice of the erasure-coded chunks is used to
obtain the playback time of different video segments. Using the playback times,
bounds on the moment generating function on the stall duration is used to bound
the mean stall duration. Moment generating function based bounds on the ordered
statistics are also used to bound the stall duration tail probability which
determines the probability that the stall time is greater than a pre-defined
number. These two metrics, mean stall duration and the stall duration tail
probability, are important quality of experience (QoE) measures for the end
users. Based on these metrics, we formulate an optimization problem to jointly
minimize the convex combination of both the QoE metrics averaged over all
requests over the placement and access of the video content. The non-convex
problem is solved using an efficient iterative algorithm. Numerical results
show significant improvement in QoE metrics for cloud-based video as compared
to the considered baselines.Comment: 18 pages, accepted to IEEE/ACM Transactions on Networkin
Near-Optimal Hybrid Processing for Massive MIMO Systems via Matrix Decomposition
For the practical implementation of massive multiple-input multiple-output
(MIMO) systems, the hybrid processing (precoding/combining) structure is
promising to reduce the high cost rendered by large number of RF chains of the
traditional processing structure. The hybrid processing is performed through
low-dimensional digital baseband processing combined with analog RF processing
enabled by phase shifters. We propose to design hybrid RF and baseband
precoders/combiners for multi-stream transmission in point-to-point massive
MIMO systems, by directly decomposing the pre-designed unconstrained digital
precoder/combiner of a large dimension. The constant amplitude constraint of
analog RF processing results in the matrix decomposition problem non-convex.
Based on an alternate optimization technique, the non-convex matrix
decomposition problem can be decoupled into a series of convex sub-problems and
effectively solved by restricting the phase increment of each entry in the RF
precoder/combiner within a small vicinity of its preceding iterate. A singular
value decomposition based technique is proposed to secure an initial point
sufficiently close to the global solution of the original non-convex problem.
Through simulation, the convergence of the alternate optimization for such a
matrix decomposition based hybrid processing (MD-HP) scheme is examined, and
the performance of the MD-HP scheme is demonstrated to be near-optimal
Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues
As a promising paradigm to reduce both capital and operating expenditures,
the cloud radio access network (C-RAN) has been shown to provide high spectral
efficiency and energy efficiency. Motivated by its significant theoretical
performance gains and potential advantages, C-RANs have been advocated by both
the industry and research community. This paper comprehensively surveys the
recent advances of C-RANs, including system architectures, key techniques, and
open issues. The system architectures with different functional splits and the
corresponding characteristics are comprehensively summarized and discussed. The
state-of-the-art key techniques in C-RANs are classified as: the fronthaul
compression, large-scale collaborative processing, and channel estimation in
the physical layer; and the radio resource allocation and optimization in the
upper layer. Additionally, given the extensiveness of the research area, open
issues and challenges are presented to spur future investigations, in which the
involvement of edge cache, big data mining, social-aware device-to-device,
cognitive radio, software defined network, and physical layer security for
C-RANs are discussed, and the progress of testbed development and trial test
are introduced as well.Comment: 27 pages, 11 figure
Feature Selection: A Data Perspective
Feature selection, as a data preprocessing strategy, has been proven to be
effective and efficient in preparing data (especially high-dimensional data)
for various data mining and machine learning problems. The objectives of
feature selection include: building simpler and more comprehensible models,
improving data mining performance, and preparing clean, understandable data.
The recent proliferation of big data has presented some substantial challenges
and opportunities to feature selection. In this survey, we provide a
comprehensive and structured overview of recent advances in feature selection
research. Motivated by current challenges and opportunities in the era of big
data, we revisit feature selection research from a data perspective and review
representative feature selection algorithms for conventional data, structured
data, heterogeneous data and streaming data. Methodologically, to emphasize the
differences and similarities of most existing feature selection algorithms for
conventional data, we categorize them into four main groups: similarity based,
information theoretical based, sparse learning based and statistical based
methods. To facilitate and promote the research in this community, we also
present an open-source feature selection repository that consists of most of
the popular feature selection algorithms
(\url{http://featureselection.asu.edu/}). Also, we use it as an example to show
how to evaluate feature selection algorithms. At the end of the survey, we
present a discussion about some open problems and challenges that require more
attention in future research
Decomposition Principles and Online Learning in Cross-Layer Optimization for Delay-Sensitive Applications
In this paper, we propose a general cross-layer optimization framework in
which we explicitly consider both the heterogeneous and dynamically changing
characteristics of delay-sensitive applications and the underlying time-varying
network conditions. We consider both the independently decodable data units
(DUs, e.g. packets) and the interdependent DUs whose dependencies are captured
by a directed acyclic graph (DAG). We first formulate the cross-layer design as
a non-linear constrained optimization problem by assuming complete knowledge of
the application characteristics and the underlying network conditions. The
constrained cross-layer optimization is decomposed into several cross-layer
optimization subproblems for each DU and two master problems. The proposed
decomposition method determines the necessary message exchanges between layers
for achieving the optimal cross-layer solution. However, the attributes (e.g.
distortion impact, delay deadline etc) of future DUs as well as the network
conditions are often unknown in the considered real-time applications. The
impact of current cross-layer actions on the future DUs can be characterized by
a state-value function in the Markov decision process (MDP) framework. Based on
the dynamic programming solution to the MDP, we develop a low-complexity
cross-layer optimization algorithm using online learning for each DU
transmission. This online algorithm can be implemented in real-time in order to
cope with unknown source characteristics, network dynamics and resource
constraints. Our numerical results demonstrate the efficiency of the proposed
online algorithm.Comment: 30 pages, 10 figure
Spatially Sparse Precoding in Millimeter Wave MIMO Systems
Millimeter wave (mmWave) signals experience orders-of-magnitude more pathloss
than the microwave signals currently used in most wireless applications. MmWave
systems must therefore leverage large antenna arrays, made possible by the
decrease in wavelength, to combat pathloss with beamforming gain. Beamforming
with multiple data streams, known as precoding, can be used to further improve
mmWave spectral efficiency. Both beamforming and precoding are done digitally
at baseband in traditional multi-antenna systems. The high cost and power
consumption of mixed-signal devices in mmWave systems, however, make analog
processing in the RF domain more attractive. This hardware limitation restricts
the feasible set of precoders and combiners that can be applied by practical
mmWave transceivers. In this paper, we consider transmit precoding and receiver
combining in mmWave systems with large antenna arrays. We exploit the spatial
structure of mmWave channels to formulate the precoding/combining problem as a
sparse reconstruction problem. Using the principle of basis pursuit, we develop
algorithms that accurately approximate optimal unconstrained precoders and
combiners such that they can be implemented in low-cost RF hardware. We present
numerical results on the performance of the proposed algorithms and show that
they allow mmWave systems to approach their unconstrained performance limits,
even when transceiver hardware constraints are considered.Comment: 30 pages, 7 figures, submitted to IEEE Transactions on Wireless
Communication
Symbol-level and Multicast Precoding for Multiuser Multiantenna Downlink: A Survey, Classification and Challenges
Precoding has been conventionally considered as an effective means of
mitigating the interference and efficiently exploiting the available in the
multiantenna downlink channel, where multiple users are simultaneously served
with independent information over the same channel resources. The early works
in this area were focused on transmitting an individual information stream to
each user by constructing weighted linear combinations of symbol blocks
(codewords). However, more recent works have moved beyond this traditional view
by: i) transmitting distinct data streams to groups of users and ii) applying
precoding on a symbol-per-symbol basis. In this context, the current survey
presents a unified view and classification of precoding techniques with respect
to two main axes: i) the switching rate of the precoding weights, leading to
the classes of block- and symbol-level precoding, ii) the number of users that
each stream is addressed to, hence unicast-/multicast-/broadcast- precoding.
Furthermore, the classified techniques are compared through representative
numerical results to demonstrate their relative performance and uncover
fundamental insights. Finally, a list of open theoretical problems and
practical challenges are presented to inspire further research in this area.Comment: Submitted to IEEE Communications Surveys & Tutorial
Load Balancing User Association in Millimeter Wave MIMO Networks
User association is necessary in dense millimeter wave (mmWave) networks to
determine which base station a user connects to in order to balance base
station loads and maximize throughput. Given that mmWave connections are highly
directional and vulnerable to small channel variations, user association
changes these connections and hence significantly affects the user's
instantaneous rate as well as network interference. In this paper, we introduce
a new load balancing user association scheme for mmWave MIMO networks which
considers this dependency on user association of user's transmission rates and
network interference. We formulate the user association problem as mixed
integer nonlinear programming and design a polynomial-time algorithm, called
Worst Connection Swapping (WCS), to find a near-optimal solution. Simulation
results confirm that the proposed user association scheme improves network
performance significantly by moving the traffic of congested base stations to
lightly-loaded ones and adjusting the interference accordingly. Further, the
proposed WCS algorithm outperforms other generic algorithms for combinatorial
programming such as the genetic algorithm in both accuracy and speed at several
orders of magnitude faster, and for small networks where exhaustive search is
possible it reaches the optimal solution.Comment: 15 pages, 8 figures, Submitted to IEEE Transactions on Wireless
Communication
On the Packet Allocation of Multi-Band Aggregation Wireless Networks
The use of heterogeneous networks with multiple radio access technologies
(RATs) is a system concept that both academia and industry are studying. In
such system, integrated use of available multiple RATs is essential to achieve
beyond additive throughput and connectivity gains using multi-dimensional
diversity. This paper considers an aggregation module called opportunistic
multi-MAC aggregation (OMMA). It resides between the IP layer and the air
interface protocol stacks, common to all RATs in the device. We present a
theoretical framework for such system while considering a special case of
multi-RAT systems, i.e., a multi-band wireless LAN (WLAN) system. An optimal
packet distribution approach is derived which minimizes the average packet
latency (the sum of queueing delay and serving delay) over multiple bands. It
supports multiple user terminals with different QoS classes simultaneously. We
further propose a packet scheduling algorithm, OMMA Leaky Bucket, which
minimizes the packet end-to-end delay, i.e., the sum of average packet latency
and average packet reordering delay. We also describe the system architecture
of the proposed OMMA system, which is applicable for the general case of the
multi- RAT devices. It includes functional description, discovery and
association processes, and dynamic RAT update management. We finally present
simulation results for a multi-band WLAN system. It shows the performance gains
of the proposed OMMA Leaky Bucket scheme in comparison to other existing packet
scheduling mechanisms.Comment: The final publication is available at Springer via
https://link.springer.com/article/10.1007/s11276-017-1486-
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