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arXiv · 1705.05960

Utility Maximizing Sequential Sensing Over a Finite Horizon

Lorenzo Ferrari·Qing Zhao·Anna Scaglione
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Abstract

We consider the problem of optimally utilizing $N$ resources, each in an unknown binary state. The state of each resource can be inferred from state-dependent noisy measurements. Depending on its state, utilizing a resource results in either a reward or a penalty per unit time. The objective is a sequential strategy governing the decision of sensing and exploitation at each time to maximize the expected utility (i.e., total reward minus total penalty and sensing cost) over a finite horizon $L$. We formulate the problem as a Partially Observable Markov Decision Process (POMDP) and show that the optimal strategy is based on two time-varying thresholds for each resource and an optimal selection rule for which resource to sense. Since a full characterization of the optimal strategy is generally intractable, we develop a low-complexity policy that is shown by simulations to offer near optimal performance. This problem finds applications in opportunistic spectrum access, marketing strategies and other sequential resource allocation problems.

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Lorenzo Ferrari, Qing Zhao, Anna Scaglione. 2017-05-17. Utility Maximizing Sequential Sensing Over a Finite Horizon. https://doi.org/10.1109/tsp.2017.2692725

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