Millimeter-wave (mmWave) is capable of achieving gigabit/second communication capacity and centimeter-level sensing accuracy and has become one of the main frequency bands for integrated sensing and communications (IS...Millimeter-wave (mmWave) is capable of achieving gigabit/second communication capacity and centimeter-level sensing accuracy and has become one of the main frequency bands for integrated sensing and communications (ISAC) research. Hybrid beamforming techniques have attracted much attention for solving the high path loss of mmWave and further reducing the hardware cost of the system. However, the related studies based on multicarrier and fully-connected hybrid architectures are still limited. For this reason,this paper investigates the orthogonal frequency division multiplexing (OFDM) based mmWave fully-connected hybrid architecture ISAC system to form a stable communication beam and dynamically varying sensing beam. In order to realize the aforementioned multifunctional beams, the hybrid beamformer design problem based on weighted error minimization of multicarrier is proposed and solved efficiently using the penalty dual decomposition (PDD) algorithm. Meanwhile, based on the echo model, the multicarrier multiple signal classification (MUSIC) algorithm for target angle of arrival estimation and the two-dimensional discrete Fourier transform(2D-DFT)algorithm for distance and velocity estimation are proposed, respectively. Numerical simulation results show that by adjusting the weighting factor,a flexible trade-off can be formed between the communication spectrum efficiency and the sensing accuracy error.展开更多
In this paper we employ an improved Siamese neural network to assess the semantic similarity between sentences. Our model implements the function of inputting two sentences to obtain the similarity score. We design ou...In this paper we employ an improved Siamese neural network to assess the semantic similarity between sentences. Our model implements the function of inputting two sentences to obtain the similarity score. We design our model based on the Siamese network using deep Long Short-Term Memory (LSTM) Network. And we add the special attention mechanism to let the model give different words different attention while modeling sentences. The fully-connected layer is proposed to measure the complex sentence representations. Our results show that the accuracy is better than the baseline in 2016. Furthermore, it is showed that the model has the ability to model the sequence order, distribute reasonable attention and extract meanings of a sentence in different dimensions.展开更多
文摘Millimeter-wave (mmWave) is capable of achieving gigabit/second communication capacity and centimeter-level sensing accuracy and has become one of the main frequency bands for integrated sensing and communications (ISAC) research. Hybrid beamforming techniques have attracted much attention for solving the high path loss of mmWave and further reducing the hardware cost of the system. However, the related studies based on multicarrier and fully-connected hybrid architectures are still limited. For this reason,this paper investigates the orthogonal frequency division multiplexing (OFDM) based mmWave fully-connected hybrid architecture ISAC system to form a stable communication beam and dynamically varying sensing beam. In order to realize the aforementioned multifunctional beams, the hybrid beamformer design problem based on weighted error minimization of multicarrier is proposed and solved efficiently using the penalty dual decomposition (PDD) algorithm. Meanwhile, based on the echo model, the multicarrier multiple signal classification (MUSIC) algorithm for target angle of arrival estimation and the two-dimensional discrete Fourier transform(2D-DFT)algorithm for distance and velocity estimation are proposed, respectively. Numerical simulation results show that by adjusting the weighting factor,a flexible trade-off can be formed between the communication spectrum efficiency and the sensing accuracy error.
文摘In this paper we employ an improved Siamese neural network to assess the semantic similarity between sentences. Our model implements the function of inputting two sentences to obtain the similarity score. We design our model based on the Siamese network using deep Long Short-Term Memory (LSTM) Network. And we add the special attention mechanism to let the model give different words different attention while modeling sentences. The fully-connected layer is proposed to measure the complex sentence representations. Our results show that the accuracy is better than the baseline in 2016. Furthermore, it is showed that the model has the ability to model the sequence order, distribute reasonable attention and extract meanings of a sentence in different dimensions.
