142,214 research outputs found
Leveraging Physical Layer Capabilites: Distributed Scheduling in Interference Networks with Local Views
In most wireless networks, nodes have only limited local information about
the state of the network, which includes connectivity and channel state
information. With limited local information about the network, each node's
knowledge is mismatched; therefore, they must make distributed decisions. In
this paper, we pose the following question - if every node has network state
information only about a small neighborhood, how and when should nodes choose
to transmit? While link scheduling answers the above question for
point-to-point physical layers which are designed for an interference-avoidance
paradigm, we look for answers in cases when interference can be embraced by
advanced PHY layer design, as suggested by results in network information
theory.
To make progress on this challenging problem, we propose a constructive
distributed algorithm that achieves rates higher than link scheduling based on
interference avoidance, especially if each node knows more than one hop of
network state information. We compare our new aggressive algorithm to a
conservative algorithm we have presented in [1]. Both algorithms schedule
sub-networks such that each sub-network can employ advanced
interference-embracing coding schemes to achieve higher rates. Our innovation
is in the identification, selection and scheduling of sub-networks, especially
when sub-networks are larger than a single link.Comment: 14 pages, Submitted to IEEE/ACM Transactions on Networking, October
201
Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints
We investigate two new optimization problems -- minimizing a submodular
function subject to a submodular lower bound constraint (submodular cover) and
maximizing a submodular function subject to a submodular upper bound constraint
(submodular knapsack). We are motivated by a number of real-world applications
in machine learning including sensor placement and data subset selection, which
require maximizing a certain submodular function (like coverage or diversity)
while simultaneously minimizing another (like cooperative cost). These problems
are often posed as minimizing the difference between submodular functions [14,
35] which is in the worst case inapproximable. We show, however, that by
phrasing these problems as constrained optimization, which is more natural for
many applications, we achieve a number of bounded approximation guarantees. We
also show that both these problems are closely related and an approximation
algorithm solving one can be used to obtain an approximation guarantee for the
other. We provide hardness results for both problems thus showing that our
approximation factors are tight up to log-factors. Finally, we empirically
demonstrate the performance and good scalability properties of our algorithms.Comment: 23 pages. A short version of this appeared in Advances of NIPS-201
Distributed Model Predictive Control Using a Chain of Tubes
A new distributed MPC algorithm for the regulation of dynamically coupled
subsystems is presented in this paper. The current control action is computed
via two robust controllers working in a nested fashion. The inner controller
builds a nominal reference trajectory from a decentralized perspective. The
outer controller uses this information to take into account the effects of the
coupling and generate a distributed control action. The tube-based approach to
robustness is employed. A supplementary constraint is included in the outer
optimization problem to provide recursive feasibility of the overall controllerComment: Accepted for presentation at the UKACC CONTROL 2016 conference
(Belfast, UK
Belief Tree Search for Active Object Recognition
Active Object Recognition (AOR) has been approached as an unsupervised
learning problem, in which optimal trajectories for object inspection are not
known and are to be discovered by reducing label uncertainty measures or
training with reinforcement learning. Such approaches have no guarantees of the
quality of their solution. In this paper, we treat AOR as a Partially
Observable Markov Decision Process (POMDP) and find near-optimal policies on
training data using Belief Tree Search (BTS) on the corresponding belief Markov
Decision Process (MDP). AOR then reduces to the problem of knowledge transfer
from near-optimal policies on training set to the test set. We train a Long
Short Term Memory (LSTM) network to predict the best next action on the
training set rollouts. We sho that the proposed AOR method generalizes well to
novel views of familiar objects and also to novel objects. We compare this
supervised scheme against guided policy search, and find that the LSTM network
reaches higher recognition accuracy compared to the guided policy method. We
further look into optimizing the observation function to increase the total
collected reward of optimal policy. In AOR, the observation function is known
only approximately. We propose a gradient-based method update to this
approximate observation function to increase the total reward of any policy. We
show that by optimizing the observation function and retraining the supervised
LSTM network, the AOR performance on the test set improves significantly.Comment: IROS 201
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