123 research outputs found
Finite-Horizon Optimal Transmission Policies for Energy Harvesting Sensors
In this paper, we derive optimal transmission policies for energy harvesting
sensors to maximize the utility obtained over a finite horizon. First, we
consider a single energy harvesting sensor, with discrete energy arrival
process, and a discrete energy consumption policy. Under this model, we show
that the optimal finite horizon policy is a threshold policy, and explicitly
characterize the thresholds, and the thresholds can be precomputed using a
recursion. Next, we address the case of multiple sensors, with only one of them
allowed to transmit at any given time to avoid interference, and derive an
explicit optimal policy for this scenario as well.Comment: Appeared in IEEE ICASSP 201
Spatial CSMA: A Distributed Scheduling Algorithm for the SIR Model with Time-varying Channels
Recent work has shown that adaptive CSMA algorithms can achieve throughput
optimality. However, these adaptive CSMA algorithms assume a rather simplistic
model for the wireless medium. Specifically, the interference is typically
modelled by a conflict graph, and the channels are assumed to be static. In
this work, we propose a distributed and adaptive CSMA algorithm under a more
realistic signal-to-interference ratio (SIR) based interference model, with
time-varying channels. We prove that our algorithm is throughput optimal under
this generalized model. Further, we augment our proposed algorithm by using a
parallel update technique. Numerical results show that our algorithm
outperforms the conflict graph based algorithms, in terms of supportable
throughput and the rate of convergence to steady-state.Comment: This work has been presented at National Conference on Communication,
2015, held at IIT Bombay, Mumbai, Indi
Collaborative Learning of Stochastic Bandits over a Social Network
We consider a collaborative online learning paradigm, wherein a group of
agents connected through a social network are engaged in playing a stochastic
multi-armed bandit game. Each time an agent takes an action, the corresponding
reward is instantaneously observed by the agent, as well as its neighbours in
the social network. We perform a regret analysis of various policies in this
collaborative learning setting. A key finding of this paper is that natural
extensions of widely-studied single agent learning policies to the network
setting need not perform well in terms of regret. In particular, we identify a
class of non-altruistic and individually consistent policies, and argue by
deriving regret lower bounds that they are liable to suffer a large regret in
the networked setting. We also show that the learning performance can be
substantially improved if the agents exploit the structure of the network, and
develop a simple learning algorithm based on dominating sets of the network.
Specifically, we first consider a star network, which is a common motif in
hierarchical social networks, and show analytically that the hub agent can be
used as an information sink to expedite learning and improve the overall
regret. We also derive networkwide regret bounds for the algorithm applied to
general networks. We conduct numerical experiments on a variety of networks to
corroborate our analytical results.Comment: 14 Pages, 6 Figure
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