In order to solve the problem of spectrum and power allocation for D2D communication in cellular networks when the prior knowledge is not available, a method based on machine learning is proposed in this paper. Q-learning, which is one of the most important algorithms in machine learning, is proposed to solve the radio resource management in underlay mode to find the optimal strategy in time series. In this mode, Q-learning is used to finish the channel assignment and the power allocation at the same time. And the simulation results show that greater system capacity can be achieved through the method proposed in this paper.
Default dependency structure is crucial in pricing multi-name credit derivatives as well as in credit risk management. In this paper, we extend the first passage model for one name with stochastic volatility (Fouque-Sircar-Sølna, Applied Mathematical Finance 2006) to the multi-name case. Correlation of defaults is generated by correlation between the Brownian motions driving the individual names as well as through common stochastic volatility factors. A numerical example for the loss distribution of a portfolio of defaultable bonds is examined after stochastic volatility is incorporated.
Phononic crystals (PnC) are a remarkable example of acoustic metamaterials with superior wave attenuation mechanisms for piezoelectric micro-electro-mechanical systems (MEMS) resonators to reduce the energy dissipation. Herein, a spider web-like PnC (SW-PnC) is proposed to sufficiently isolate the wave vibration. Finite-element analysis is performed to gain insight into the transmission property of finite PnC, and band characteristics by infinite periods. In comparison with the circle hole PnC at a similar bandgap, due to its already very lightweight PnC structure compared with previously reported PnCs, the proposed PnC offers a significantly lighter weight, smaller lattice constant, and greater energy leakage inhibition. More specifically, the resonator with the SW-PnC plate as the anchoring substrate exhibited a quality factor as high as 66569.7 at 75.82 MHz.
Gaussian copula is by far the most popular copula used in the financial industry in default dependency modeling. However, it has a major drawback -it does not exhibit tail dependence, a very important property for copula. The essence of tail dependence is the interdependence when extreme events occur, say, defaults of corporate bonds. In this paper we show that some tail dependence can be restored by introducing stochastic volatility on a Gaussian copula. Using perturbation methods we then derive an approximate copula -called perturbed Gaussian copula in this paper.
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