摘要
Statistical Signal Transmission(SST)is a technique based on orthogonal frequency-division multiplexing(OFDM)and adopts cyclostationary features,which can transmit extra information without additional bandwidth.However,the more complicated environment in 5G communication systems,especially the fast time-varying scenarios,will dramatically degrade the performance of the SST.In this paper,we propose a fragmental weight-conservation combining(FWCC)scheme for SST,to overcome its performance degradation under fast time-varying channels.The proposed FWCC scheme consists of three phases:1、incise the received OFDM stream into pieces;2、endue different weights for fine and contaminated pieces,respectively;3、combine cyclic autocorrelation function energies of all the pieces;and 4、compute the final feature and demodulate data of SST.Through these procedures above,the detection accuracy of SST will be theoretically refined under fast time-varying channels.Such an inference is confirmed through numerical results in this paper.It is demonstrated that the BER performance of proposed scheme outperforms that of the original scheme both in ideal channel estimation conditions and in imperfect channel estimation conditions.In addition,we also find the experiential optimal weight distribution strategy for the proposed FWCC scheme,which facilitates practical applications.
Statistical Signal Transmission(SST) is a technique based on orthogonal frequency-division multiplexing(OFDM) and adopts cyclostationary features, which can transmit extra information without additional bandwidth. However, the more complicated environment in 5 G communication systems, especially the fast time-varying scenarios, will dramatically degrade the performance of the SST. In this paper, we propose a fragmental weight-conservation combining(FWCC) scheme for SST, to overcome its performance degradation under fast time-varying channels. The proposed FWCC scheme consists of three phases: 1) incise the received OFDM stream into pieces; 2) endue different weights for fine and contaminated pieces, respectively; 3) combine cyclic autocorrelation function energies of all the pieces; and 4) compute the final feature and demodulate data of SST. Through these procedures above, the detection accuracy of SST will be theoretically refined under fast time-varying channels. Such an inference is confirmed through numerical results in this paper. It is demonstrated that the BER performance of proposed scheme outperforms that of the original scheme both in ideal channel estimation conditions and in imperfect channel estimation conditions. In addition, we also find the experiential optimal weight distribution strategy for the proposed FWCC scheme, which facilitates practical applications.
基金
supported by the National Natural Science Foundation of China (Nos. 61801461, 61801460)
the Strategical Leadership Project of Chinese Academy of Sciences (grant No. XDC02070800)
the Shanghai Municipality of Science and Technology Commission Project (Nos. 18XD1404100, 17QA1403800)
