Given the dense distribution of points of interest in cities, it is difficult to semantically label trajectories by distance. This paper proposes a classification semantic labeling algorithm based on hidden Markov model (HMM). An algorithm based on time and space constraints is used to identify the stay points in the trajectory, and a density-based clustering algorithm is used to identify the user’s individual significant places. On the basis of the identification of individual points of interest according to the stay time and stay period, a method based on HMM is used to label public points of interest for the dense areas of points of interest. When calculating the observation probability matrix, a dynamic time function is used to simulate the influence radius of public points of interest in different periods. The simulation result shows that compared with the traditional method, the model has a significant improvement in the accuracy of labeling individual points of interest. At the same time, in areas with dense points of interest, the algorithm improves the accuracy of labeling by 6% compared with the traditional HMM.
In directional modulation (DM) networks, conventional power allocation strategy is actually an equal-power allocation method. However, in practice, each antenna is equipped with its own power amplifier and is restricted. This paper considers a more practical situation that the power of each antenna sent to the receiver is different. Here, we study the optimal power allocation (OPA) strategy with multiple-input single-output (MISO) in DM communication system. Explicitly, we first formulate a secrecy rate (SR) maximization problem subject to the total power constraint, with designing the useful information power vector and artificial noise covariance matrix. For the non-convex problem, we propose the maximizing secrecy rate based on successive convex approximation (Max-SR-SCA) method to solve it. Subsequently, in order to reduce the computational complexity, we propose the maximizing product of signal-to-leakage-plus-noise-ratio (SLNR) and artificial-noise-to-leakage-plus-noise-ratio (ANLNR) (Max-SLNR-ANLNR) method to obtain a suboptimal solution. Simulation results show that the proposed two methods have better SR performance than the conventional PA factor scheme. In addition, the proposed Max-SLNR-ANLNR method forms a single energy main peak at Eve position, which indicates it realizes precise jamming.
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