摘要:
针对正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)水声通信系统的高峰值平均功率比(Peakto-Average Ratio,PAPR)问题,在发射端采用了压扩变换法和限幅法联合的方法降低PAPR。由于限幅法会产生带内干扰和带外噪声,降低通信系统的误码性能,因此利用限幅噪声的稀疏性,在接收端采用压缩感知(Compressed Sensing,CS)法对限幅噪声进行估计和恢复。限幅噪声的估计受信道估计准确性的影响,为提高限幅噪声估计的准确度,提出了重叠压缩感知算法,在恢复限幅噪声的过程中利用了压缩感知信道估计法估计所得的信道信息和发射数据对限幅噪声进行估计,有效降低了限幅法对系统误码性能的影响。仿真和水池实验验证了该算法的有效性。
Abstract:
Due to the characteristics of the underwater acoustic (UWA) channel especially the limited bandwidth, Orthogonal frequency division multiplexing (OFDM) is widely used because of its high spectrum efficiency and ability in anti-multipath fading. However, OFDM also has its drawbacks, one of which is the relatively high peak-to-average ratio (PAPR). The problem leads to saturation in the power amplifier and consequently distorts the signal which is not allowed in the underwater acoustic communication. Clipping and C companding as the most classic and convenience algorithm is widely applied to address the high PAPR. But traditional c companding is not suitable for the underwater acoustic field and meanwhile clipping introduces additional noise which degrades the system's performance. Based on these, an improved c companding combined with clipping is promoted. As clipping algorithm introduces clipping noise into the communication system which can be recognized as sparse, compressed sensing (CS) is proposed to estimate it. The scheme exploited pilot tones and data tones instead of reserve tones, which is different from the previous works and causes less loss of data rate. Also, to minimizing the influence of underwater acoustic channel, compressed sensing (CS) in channel estimating is also adopted during mitigating clipping noise, which can provide more accurate channel characteristics than LS or MMSE algorithm. The iterative CS algorithm can significantly improve the quality of the communication system even in low SNR.
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