摘要
针对电力线通信中,传统频偏估计复杂度较高问题,提出了低复杂度时域同步正交频分复用(TDSOFDM)频偏估计算法。首先,对电力线网络特性进行解析,采用三段等长循环伪随机噪声序列(PN)构造帧头填充保护间隔;其次,帧头与帧体分别基于二进制相移键控(BPSK)和正交振幅调制(QAM);最后,与传统基于循环前缀(CP)与一般PN频偏估计算法相比,改进算法只需对一段循环PN长度作相关,减少自相关运算次数,且可达到较好频偏估计性能。仿真表明:误码率(BER)为10^(-4)时,改进算法较传统基于CP及一般PN算法约有5 d B和1 d B的增益。当插入总序列及循环序列长度分别为420与165时,改进算法每帧相关运算次数减少1 186次。由理论分析及仿真结果可知,所提算法有效降低计算复杂度,减少传输过程实现成本,提高通信速率。
Concerning the high complexity of the traditional frequency estimation algorithm, a new frequency offset estimation algorithm of Time Domain Synchronous Orthogonal Frequency Division Multiplexing （TDS-OFDM） with low complexity for power line communication was proposed. Firstly, the characteristics of power line network were analyzed, and a frame head was constructed with three equal-length cyclic Pseudo-random Noise （PN） sequences. Secondly, the frame head and body were based on Binary Phase Shift Keying （BPSK） and Quadrature Amplitude Modulation （QAM） modes. Finally, compared with the traditional algorithms based on Cyclic Prefix （CP） or general PN, only the lengths of part of PN were calculated, so the number of autocorrelations was reduced, and better performance could be guaranteed. The simulation results show that, at Bit Error Rate （BER） of 10 -4, the improved algorithm has about 5 dB and 1 dB gains while comparing with the algorithms based on CP and general PN, respectively. And compared with algorithm with general PN, when the lengths of inserted sequence and cyclic sequence were 420 and 165, the number of correlations per frame was reduced by 1 186. The theoretical analysis and simulation results show that proposed algorithm can effectively reduce the computational complexity and cost of process, meanwhile improves the communication rate.
出处
《计算机应用》
CSCD
北大核心
2017年第7期1877-1882,共6页
journal of Computer Applications
基金
国家自然科学基金资助项目(61471152)
浙江省自然科学基金资助项目(LZ14F010003)
浙江省公益性技术应用研究计划项目(2015C31103)
杭州电子科技大学研究生科研创新基金资助项目(CXJJ2016032)~~
关键词
时域同步正交频分复用
循环伪随机噪声序列
自相关
频偏估计
Time Domain Synchronous Orthogonal Frequency Division Multiplexing （TDS-OFDM）
cyclic Pseudorandom Noise （PN） sequence
autocorrelation
frequency offset estimation
