摘要：
The logging cable is a typical selective frequency fading channel,its effective bandwidth is narrow,and there are serious at-tenuation to the signals whose frequency are above 100 kHz. In order to improve the error resilience of downhole high-speed cable trans-mission system,generally a forward error correction coding technology is introduced into the system. According to the channel character-istics of well logging cable,a hybrid convolutional coding scheme that can adjust coding constraint degree and decoding depth is pro-posed,and convolutional encoding and decoding is realized on FPGA. The simulation results show that the longer the constraint degree of convolutional coding is,the lower the bit error rate of the system is,but the decoding time grow faster;with the increase of decoding depth,the reduction of the bit error rate is not obvious,but the decoding time increases linearly;compared with convolutional encoding with single parameter,the proposed hybrid convolution encoding scheme can achieve higher encoding gain,the decoding time is reduced by more than 31％,and the optimization of error correction performance and coding and decoding complexity is achieved.%测井电缆是典型的频率选择性衰落信道,其有效带宽窄,对频率100 kHz以上的信号衰减严重.为提高井下高速电缆传输系统的抗误码能力,通常引入纠错能力较强的卷积编码技术.针对测井电缆信道特性,提出一种能够调整编码约束度与译码深度的混合卷积编码方案,并在FPGA上实现卷积编译码.仿真结果表明,卷积编码约束度越长,系统误比特率越低,但译码时间增长得更快;随着译码深度的增加,系统误比特率减小得不明显,但译码时间呈线性增加;与单一参数的卷积编码相比,所提出的混合卷积编码方案能获得更高的编码增益,且译码时间减小31％以上,实现了纠错性能与编译码复杂度的最优化.
