The existence of under-utilized spectrum and the congestion of certain spectrum give rise to development on cognitive radios as a promising technology to address these problems. However, the general precondition that secondary users should evacuate from the channel which primary users are willing to use, makes the cognitive ad hoc networks vulnerable and unstable since network partition may occur since the links affected by primary users may form a cut set of the network. In this paper, we address this problem through a hybrid channel assignment scheme, where channels are carefully assigned to each link to guarantee the connectivity and the capacity based on spanning trees. Since the optimal solution is NP-hard, we propose an effective approximation algorithm. Also, dynamic channel assignment is proposed to further improve the network performance.
We propose a data-driven framework to enable the modeling and optimization of human-machine interaction processes, e.g., systems aimed at assisting humans in decision-making or learning, work-load allocation, and interactive advertising. This is a challenging problem for several reasons. First, humans' behavior is hard to model or infer, as it may reflect biases, long term memory, and sensitivity to sequencing, i.e., transience and exponential complexity in the length of the interaction. Second, due to the interactive nature of such processes, the machine policy used to engage with human may bias possible data-driven inferences. Finally, in choosing machine policies that optimize interaction rewards, one must, on the one hand, avoid being overly sensitive to error/variability in the estimated human model, and on the other, being overly deterministic/predictable which may result in poor human 'engagement' in the interaction. To meet these challenges, we propose a robust approach, based on the maximum entropy principle, which iteratively estimates human behavior and optimizes the machine policy-Alternating Entropy-Reward Ascent (AREA) algorithm. We characterize AREA, in terms of its space and time complexity and convergence. We also provide an initial validation based on synthetic data generated by an established noisy nonlinear model for human decision-making.
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