Utility Maximizing Sequential Sensing Over a Finite Horizon

作者：Ferrari Lorenzo^*; Zhao Qing; Scaglione Anna

来源：IEEE Transactions on Signal Processing, 2017, 65(13): 3430-3445.

DOI：10.1109/TSP.2017.2692725

• Optimum sequential testing
• opportunistic spectrum access
• multi-channel sensing
• cognitive radio

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 and show that the optimal strategy is based on two time-varying thresholds for each resource and an optimal selection rule to sense a particular resource. Since a full characterization of the optimal strategy is generally intractable, we develop a low-complexity policy that is shown by simulations to offer a near optimal performance. This problem finds applications in opportunistic spectrum access, marketing strategies, and other sequential resource allocation problems.

• 出版日期2017-7-1