A compressed sensing (CS)-based detector is proposed for the low-density parity-check (LDPC) coded single carrier frequency division multiple access (SC-FDMA) scheme. The proposed CS-based detector can be employed at the receiver of LDPC-coded SC-FDMA systems for efficient image communications over vehicular channels. The proposed detector employs a suitable sparse recovery algorithm. We have considered both the discrete Fourier transform (DFT)-based and the discrete cosine transform (DCT)-based SC-FDMA for mitigating the channel-induced dispersion at a low peak to average power ratio (PAPR). Additionally, both the linear equalizer (LE) and the decision feedback equalizer (DFE)-based SC-FDMA have been considered for image communication. The performance of the proposed technique is investigated using a number of image quality metrics. The qualities of the received images are also compared visually. The complexity of the proposed detector and that of the benchmark detectors are quantified. Furthermore, the performance and the complexity of the proposed system using some of the sparse recovery techniques are investigated and compared. Our simulations demonstrate that LDPC coded SC-FDMA using the compressed sampling matching pursuit (CoSaMP)-based CS detector can significantly improve the performance of image communication over vehicular channels.

针对低密度奇偶校验 (LDPC) 编码的单载波频分多址 (SC-FDMA) 方案提出了一种基于压缩感知 (CS) 的检测器。所提出的基于 CS 的检测器可用于 LDPC 编码的 SC-FDMA 系统的接收器，以通过车辆通道进行有效的图像通信。所提出的检测器采用了合适的稀疏恢复算法。我们已经考虑了基于离散傅里叶变换 (DFT) 和基于离散余弦变换 (DCT) 的 SC-FDMA，用于在低峰均功率比 (PAPR) 下减轻信道引起的色散。此外，基于线性均衡器 (LE) 和判决反馈均衡器 (DFE) 的 SC-FDMA 都已被考虑用于图像通信。使用许多图像质量指标来研究所提出技术的性能。接收到的图像的质量也在视觉上进行比较。提出的检测器和基准检测器的复杂性被量化。此外，研究和比较了使用一些稀疏恢复技术的建议系统的性能和复杂性。我们的模拟表明，LDPC 编码的 SC-FDMA 使用基于压缩采样匹配追踪 (CoSaMP) 的 CS 检测器可以显着提高车辆通道上图像通信的性能。