The existing recognition algorithms of space-time block code(STBC)for multi-antenna(MA)orthogonal frequencydivision multiplexing(OFDM)systems use feature extraction and hypothesis testing to identify the signal types ...The existing recognition algorithms of space-time block code(STBC)for multi-antenna(MA)orthogonal frequencydivision multiplexing(OFDM)systems use feature extraction and hypothesis testing to identify the signal types in a complex communication environment.However,owing to the restrictions on the prior information and channel conditions,these existing algorithms cannot perform well under strong interference and noncooperative communication conditions.To overcome these defects,this study introduces deep learning into the STBCOFDM signal recognition field and proposes a recognition method based on the fourth-order lag moment spectrum(FOLMS)and attention-guided multi-scale dilated convolution network(AMDCNet).The fourth-order lag moment vectors of the received signals are calculated,and vectors are stitched to form two-dimensional FOLMS,which is used as the input of the deep learning-based model.Then,the multi-scale dilated convolution is used to extract the details of images at different scales,and a convolutional block attention module(CBAM)is introduced to construct the attention-guided multi-scale dilated convolution module(AMDCM)to make the network be more focused on the target area and obtian the multi-scale guided features.Finally,the concatenate fusion,residual block and fully-connected layers are applied to acquire the STBC-OFDM signal types.Simulation experiments show that the average recognition probability of the proposed method at−12 dB is higher than 98%.Compared with the existing algorithms,the recognition performance of the proposed method is significantly improved and has good adaptability to environments with strong disturbances.In addition,the proposed deep learning-based model can directly identify the pre-processed FOLMS samples without a priori information on channel and noise,which is more suitable for non-cooperative communication systems than the existing algorithms.展开更多
The emerging ultra-wideband (UWB) system offers a great potential for the design of high-speed short-range communications.Compared with great progress at physical layer,the corresponding medium access control (MAC) la...The emerging ultra-wideband (UWB) system offers a great potential for the design of high-speed short-range communications.Compared with great progress at physical layer,the corresponding medium access control (MAC) layer designs are naturally placed on the schedules.We focus on the optimal power load scheme,which is an integral part of the MAC layer protocol design,for UWB space-time coded (STC) orthogonal frequency-division multiplexing (OFDM) transmissions.Assumed the transmitter has perfect or partial channel stage information (CSI).Based on the optimization criteria of maximizing capacity,three kinds of power load schemes were presented with different tradeoff among performance,complexity and feedback bandwidth overhead.The proposed schemes are verified and compared under the channel prototype proposed by IEEE 802.15.3a Task Group.展开更多
以正交频分复用(orthogonal frequency division multiplexing,OFDM)调制为基础的宽带电力线通信技术,为智能电网、智能家居以及"最后一公里"提供了高效可靠的信息传输方案,然而OFDM对同步错误极度敏感,针对此问题,采用多相...以正交频分复用(orthogonal frequency division multiplexing,OFDM)调制为基础的宽带电力线通信技术,为智能电网、智能家居以及"最后一公里"提供了高效可靠的信息传输方案,然而OFDM对同步错误极度敏感,针对此问题,采用多相序列作为主同步信号的多用户帧结构,利用一种新的多相序列族(用于用户群搜索)作为主同步信号,序列族的设计不仅要考虑每个序列都有更优良的自相关特性即更高的峰值对旁瓣峰值比,还要求有很好的互相相关特性以对抗其他用户训练序列引起的用户间干扰。多用户系统中不同用户采用不同的序列作为同步序列,这种方法对系统的复杂性没有任何影响。在作者自制的硬件平台上实现了该算法,实验和仿真结果均表明该同步设计更适用于电力线环境。展开更多
基金supported by the National Natural Science Foundation of China(91538201)the Taishan Scholar Foundation of China(ts201511020).
文摘The existing recognition algorithms of space-time block code(STBC)for multi-antenna(MA)orthogonal frequencydivision multiplexing(OFDM)systems use feature extraction and hypothesis testing to identify the signal types in a complex communication environment.However,owing to the restrictions on the prior information and channel conditions,these existing algorithms cannot perform well under strong interference and noncooperative communication conditions.To overcome these defects,this study introduces deep learning into the STBCOFDM signal recognition field and proposes a recognition method based on the fourth-order lag moment spectrum(FOLMS)and attention-guided multi-scale dilated convolution network(AMDCNet).The fourth-order lag moment vectors of the received signals are calculated,and vectors are stitched to form two-dimensional FOLMS,which is used as the input of the deep learning-based model.Then,the multi-scale dilated convolution is used to extract the details of images at different scales,and a convolutional block attention module(CBAM)is introduced to construct the attention-guided multi-scale dilated convolution module(AMDCM)to make the network be more focused on the target area and obtian the multi-scale guided features.Finally,the concatenate fusion,residual block and fully-connected layers are applied to acquire the STBC-OFDM signal types.Simulation experiments show that the average recognition probability of the proposed method at−12 dB is higher than 98%.Compared with the existing algorithms,the recognition performance of the proposed method is significantly improved and has good adaptability to environments with strong disturbances.In addition,the proposed deep learning-based model can directly identify the pre-processed FOLMS samples without a priori information on channel and noise,which is more suitable for non-cooperative communication systems than the existing algorithms.
基金This work was partially supported by NSF under Grant 60496315 and national "863" projects under Grant2003AA12331005
文摘The emerging ultra-wideband (UWB) system offers a great potential for the design of high-speed short-range communications.Compared with great progress at physical layer,the corresponding medium access control (MAC) layer designs are naturally placed on the schedules.We focus on the optimal power load scheme,which is an integral part of the MAC layer protocol design,for UWB space-time coded (STC) orthogonal frequency-division multiplexing (OFDM) transmissions.Assumed the transmitter has perfect or partial channel stage information (CSI).Based on the optimization criteria of maximizing capacity,three kinds of power load schemes were presented with different tradeoff among performance,complexity and feedback bandwidth overhead.The proposed schemes are verified and compared under the channel prototype proposed by IEEE 802.15.3a Task Group.
文摘以正交频分复用(orthogonal frequency division multiplexing,OFDM)调制为基础的宽带电力线通信技术,为智能电网、智能家居以及"最后一公里"提供了高效可靠的信息传输方案,然而OFDM对同步错误极度敏感,针对此问题,采用多相序列作为主同步信号的多用户帧结构,利用一种新的多相序列族(用于用户群搜索)作为主同步信号,序列族的设计不仅要考虑每个序列都有更优良的自相关特性即更高的峰值对旁瓣峰值比,还要求有很好的互相相关特性以对抗其他用户训练序列引起的用户间干扰。多用户系统中不同用户采用不同的序列作为同步序列,这种方法对系统的复杂性没有任何影响。在作者自制的硬件平台上实现了该算法,实验和仿真结果均表明该同步设计更适用于电力线环境。
