Utility Maximizing Sequential Sensing Over a Finite Horizon
For researchers in sequential decision-making, this provides a tractable solution to a resource allocation problem with applications in spectrum access and marketing.
The paper addresses optimal sequential sensing and exploitation of multiple binary-state resources over a finite horizon, formulating it as a POMDP. The proposed low-complexity policy achieves near-optimal performance in simulations.
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.