Abstract: An angular domain channel estimation method based on matrix reconstruction for a symmetrical nonuniform array is a combined two-stage channel estimation and channel equalization scheme provided based on an SNLA model. In a first stage, a matrix reconstruction method is used to estimate a path AOA, and compared with traditional channel estimation based on ULA, the matrix reconstruction method achieves a higher resolution ratio. In a second stage, an LS method is used to obtain a path gain. According to the angular domain channel estimation method, a mean square error of channel estimation, a bit error of data transmission and complexity of a traditional scheme are significantly reduced. A simulation result indicates that compared with the traditional method, the angular domain channel estimation method can achieve a lower MSE and BER.

Abstract: A system for short text identification can determine a plurality of topics and a representative noun that identifies each of the topics in a data repository. The system can determine a co-occurrence matrix for the training words stored in the corpus and determine a word vector embedding for each of the training words in the corpus to relate each of the training words in the corpus to other ones of the training words in the corpus in an n-dimensional vector space. The system can determine word tokens for words in short text in documents in the data repository that is separate and distinct from the corpus and determine sentence vectors for short text based on the word vectors in each short text and determine a plurality of topics in the documents based on clustering of sentence vectors, wherein the plurality of topics indicates topics that are predominant in the documents in the data repository.

Abstract: A system for short text identification can determine a plurality of topics and a representative noun that identifies each of the topics in a data repository. The system can determine a co-occurrence matrix for the training words stored in the corpus and determine a word vector embedding for each of the training words in the corpus to relate each of the training words in the corpus to other ones of the training words in the corpus in an n-dimensional vector space. The system can determine word tokens for words in short text in documents in the data repository that is separate and distinct from the corpus and determine sentence vectors for short text based on the word vectors in each short text and determine a plurality of topics in the documents based on clustering of sentence vectors, wherein the plurality of topics indicates topics that are predominant in the documents in the data repository.