In order to improve the edge caching efficiency of the fog radio access network (F-RAN), this paper put forward a distributed deep Q-learning-based content caching scheme based on user preference prediction and content popularity prediction. Given that the constraint that the storage capacity of each device is limited, and the optimization problem is formulated so as to maximize the caching hit rate. Specifically, by taking users' selfishness into consideration, user preference is predicted in an offline manner by applying popular topic models. Then, the online predicted content popularity is achieved by combining the network topology relationship together with the obtained user preference. Finally, with the predicted user preference and content popularity, the deep Q-learning network (DQN)-based content caching algorithm is proposed to achieve the optimal content caching strategy. Moreover, we further present a content update policy with user preference and content popularity prediction, so that the proposed algorithm can handle the variations of contents popularity in a timely manner. Simulation results demonstrate that the proposed scheme achieves better caching hit rate compared with existing algorithms.

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In this paper, the low-performance problem of two-dimensional (2-D) direction of arrival (DOA) estimation with non-Gaussian noise in low signal-to-noise ratio is addressed. For echo signals of the multiple satellites' passive radar, a robust 2-D DOA estimation method based on improved zero-order statistics fractional low-order cyclic correlation (IZOS-FLOCC) under the alpha-stable distribution noise environment is proposed. First, we employ a uniform plane array to establish the model of received signals. Then, the IZOS-FLOCC of the echo signals is constructed based on the cyclostationarity. Finally, the IZOS-FLOCC of signals subspace is obtained by the optimization method. Moreover, we derive the Cramer-Rao low bound of the 2-D DOA estimation of echo signals based on multiple satellites' passive radar in the presence of alpha-stable distribution noise. The simulation results show that the proposed method can effectively estimate the 2-D DOA of echo signals based on multi-satellite radiation sources in the alpha-stable distribution noise environment and achieve better performance than the existing methods. INDEX TERMS Direction of arrival (DOA), parameter estimation, alpha stable distribution noise, satellite, passive radar, Cramer-Rao low bound (CRLB).

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