An efficient method for blind classification of space time block codes （STBCs） based on fourth-order cumulants is proposed for a single receiver antenna. This paper presents a model of received STBCs signals in mult...An efficient method for blind classification of space time block codes （STBCs） based on fourth-order cumulants is proposed for a single receiver antenna. This paper presents a model of received STBCs signals in multiple input single output （MISO） communication systems and applies the characteristics of coding matrices to derive analytical expressions for the fourth-order cumu- lants to be used as the basis of an algorithm. The fourth-order cumulants at various delay vectors present non-null values that depend on the transmitted STBCs. Tests of nullity are accomplished by hypothesis testing. The proposed algorithm avoids the need for a priori information of modulation scheme, channel coefficients, and noise power. Consequently, it is well suited for non-cooperative scenarios. Simulations show that this method performs well even at low signal-to-noise ratios （SNRs）.展开更多
State-of-the-arts studies on sentiment classification are typically domain-dependent and domain-restricted. In this paper, we aim to reduce domain dependency and improve overall performance simultaneously by proposing...State-of-the-arts studies on sentiment classification are typically domain-dependent and domain-restricted. In this paper, we aim to reduce domain dependency and improve overall performance simultaneously by proposing an efficient multi-domain sentiment classification algorithm. Our method employs the approach of multiple classifier combination. In this approach, we first train single domain classifiers separately with domain specific data, and then combine the classifiers for the final decision. Our experiments show that this approach performs much better than both single domain classification approach （using the training data individually） and mixed domain classification approach （simply combining all the training data）. In particular, classifier combination with weighted sum rule obtains an average error reduction of 27.6% over single domain classification.展开更多
基金co-supported by the Taishan Scholar Special Foundation of China(No.ts201511020)
文摘An efficient method for blind classification of space time block codes （STBCs） based on fourth-order cumulants is proposed for a single receiver antenna. This paper presents a model of received STBCs signals in multiple input single output （MISO） communication systems and applies the characteristics of coding matrices to derive analytical expressions for the fourth-order cumu- lants to be used as the basis of an algorithm. The fourth-order cumulants at various delay vectors present non-null values that depend on the transmitted STBCs. Tests of nullity are accomplished by hypothesis testing. The proposed algorithm avoids the need for a priori information of modulation scheme, channel coefficients, and noise power. Consequently, it is well suited for non-cooperative scenarios. Simulations show that this method performs well even at low signal-to-noise ratios （SNRs）.
基金Supported by the National Natural Science Foundation of China under Grant No.61003155Start-Up Grant for Newly Appointed Professors under Grant No.1-BBZM in The Hong Kong Polytechnic University
文摘State-of-the-arts studies on sentiment classification are typically domain-dependent and domain-restricted. In this paper, we aim to reduce domain dependency and improve overall performance simultaneously by proposing an efficient multi-domain sentiment classification algorithm. Our method employs the approach of multiple classifier combination. In this approach, we first train single domain classifiers separately with domain specific data, and then combine the classifiers for the final decision. Our experiments show that this approach performs much better than both single domain classification approach （using the training data individually） and mixed domain classification approach （simply combining all the training data）. In particular, classifier combination with weighted sum rule obtains an average error reduction of 27.6% over single domain classification.
