89 research outputs found
Dynamic Rate and Channel Selection in Cognitive Radio Systems
In this paper, we investigate dynamic channel and rate selection in cognitive
radio systems which exploit a large number of channels free from primary users.
In such systems, transmitters may rapidly change the selected (channel, rate)
pair to opportunistically learn and track the pair offering the highest
throughput. We formulate the problem of sequential channel and rate selection
as an online optimization problem, and show its equivalence to a {\it
structured} Multi-Armed Bandit problem. The structure stems from inherent
properties of the achieved throughput as a function of the selected channel and
rate. We derive fundamental performance limits satisfied by {\it any} channel
and rate adaptation algorithm, and propose algorithms that achieve (or
approach) these limits. In turn, the proposed algorithms optimally exploit the
inherent structure of the throughput. We illustrate the efficiency of our
algorithms using both test-bed and simulation experiments, in both stationary
and non-stationary radio environments. In stationary environments, the packet
successful transmission probabilities at the various channel and rate pairs do
not evolve over time, whereas in non-stationary environments, they may evolve.
In practical scenarios, the proposed algorithms are able to track the best
channel and rate quite accurately without the need of any explicit measurement
and feedback of the quality of the various channels.Comment: 19 page
Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms
We consider stochastic multi-armed bandits where the expected reward is a
unimodal function over partially ordered arms. This important class of problems
has been recently investigated in (Cope 2009, Yu 2011). The set of arms is
either discrete, in which case arms correspond to the vertices of a finite
graph whose structure represents similarity in rewards, or continuous, in which
case arms belong to a bounded interval. For discrete unimodal bandits, we
derive asymptotic lower bounds for the regret achieved under any algorithm, and
propose OSUB, an algorithm whose regret matches this lower bound. Our algorithm
optimally exploits the unimodal structure of the problem, and surprisingly, its
asymptotic regret does not depend on the number of arms. We also provide a
regret upper bound for OSUB in non-stationary environments where the expected
rewards smoothly evolve over time. The analytical results are supported by
numerical experiments showing that OSUB performs significantly better than the
state-of-the-art algorithms. For continuous sets of arms, we provide a brief
discussion. We show that combining an appropriate discretization of the set of
arms with the UCB algorithm yields an order-optimal regret, and in practice,
outperforms recently proposed algorithms designed to exploit the unimodal
structure.Comment: ICML 2014 (technical report). arXiv admin note: text overlap with
arXiv:1307.730
Optimal Distributed Scheduling in Wireless Networks under the SINR interference model
Radio resource sharing mechanisms are key to ensuring good performance in
wireless networks. In their seminal paper \cite{tassiulas1}, Tassiulas and
Ephremides introduced the Maximum Weighted Scheduling algorithm, and proved its
throughput-optimality. Since then, there have been extensive research efforts
to devise distributed implementations of this algorithm. Recently, distributed
adaptive CSMA scheduling schemes \cite{jiang08} have been proposed and shown to
be optimal, without the need of message passing among transmitters. However
their analysis relies on the assumption that interference can be accurately
modelled by a simple interference graph. In this paper, we consider the more
realistic and challenging SINR interference model. We present {\it the first
distributed scheduling algorithms that (i) are optimal under the SINR
interference model, and (ii) that do not require any message passing}. They are
based on a combination of a simple and efficient power allocation strategy
referred to as {\it Power Packing} and randomization techniques. We first
devise algorithms that are rate-optimal in the sense that they perform as well
as the best centralized scheduling schemes in scenarios where each transmitter
is aware of the rate at which it should send packets to the corresponding
receiver. We then extend these algorithms so that they reach
throughput-optimality
Cluster-Aided Mobility Predictions
Predicting the future location of users in wireless net- works has numerous
applications, and can help service providers to improve the quality of service
perceived by their clients. The location predictors proposed so far estimate
the next location of a specific user by inspecting the past individual
trajectories of this user. As a consequence, when the training data collected
for a given user is limited, the resulting prediction is inaccurate. In this
paper, we develop cluster-aided predictors that exploit past trajectories
collected from all users to predict the next location of a given user. These
predictors rely on clustering techniques and extract from the training data
similarities among the mobility patterns of the various users to improve the
prediction accuracy. Specifically, we present CAMP (Cluster-Aided Mobility
Predictor), a cluster-aided predictor whose design is based on recent
non-parametric bayesian statistical tools. CAMP is robust and adaptive in the
sense that it exploits similarities in users' mobility only if such
similarities are really present in the training data. We analytically prove the
consistency of the predictions provided by CAMP, and investigate its
performance using two large-scale datasets. CAMP significantly outperforms
existing predictors, and in particular those that only exploit individual past
trajectories
Learning to Personalize in Appearance-Based Gaze Tracking
Personal variations severely limit the performance of appearance-based gaze
tracking. Adapting to these variations using standard neural network model
adaptation methods is difficult. The problems range from overfitting, due to
small amounts of training data, to underfitting, due to restrictive model
architectures. We tackle these problems by introducing the SPatial Adaptive
GaZe Estimator (SPAZE). By modeling personal variations as a low-dimensional
latent parameter space, SPAZE provides just enough adaptability to capture the
range of personal variations without being prone to overfitting. Calibrating
SPAZE for a new person reduces to solving a small optimization problem. SPAZE
achieves an error of 2.70 degrees with 9 calibration samples on MPIIGaze,
improving on the state-of-the-art by 14 %. We contribute to gaze tracking
research by empirically showing that personal variations are well-modeled as a
3-dimensional latent parameter space for each eye. We show that this
low-dimensionality is expected by examining model-based approaches to gaze
tracking. We also show that accurate head pose-free gaze tracking is possible
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