In digital communication, synchronization between transmitter and receiver is essential for ensuring proper system performance. Error in the receiver symbol time sampling can significantly increase the bit error rate to unacceptable levels. In this paper, we propose a multilayer radial basis function neural network symbol-timing recovery (MRBFNN-STR). The proposed solution has been implemented for a 64-QAM (quadrature and amplitude modulation) system. Results show that the MRBFNN-STR improves the modulation error ratio up to 3.4 dB and reduces the bit error rate by almost one order of magnitude for 100 ppm (part per million) clock offset and signal to noise ratios above 25 dB compared to the classic widely used Gardner-Farrow’s approach. The MRBFNN is able to follow the system dynamics and to generalize, presenting good performance even when under operational situations not presented during the training phase (different clock offset, signal to noise ratio, etc.) and with lower-order modulation schemes, such as 32-QAM, 16-QAM, and QPSK (quadrature phase shift keying), without retraining. Due to the parallel nature of the MRBFNN architecture and the reduced complexity required for inference, it can be efficiently implemented in hardware and easily integrated into communication receivers, representing a feasible solution for receiver time synchronization.

在数字通信中，发射器和接收器之间的同步对于确保适当的系统性能至关重要。接收器符号时间采样中的错误会显着增加误码率到不可接受的水平。在本文中，我们提出了一种多层径向基函数神经网络符号定时恢复（MRBFNN-STR）。建议的解决方案已针对 64-QAM（正交和幅度调制）系统实施。结果表明，与经典相比，MRBFNN-STR 在 100 ppm（百万分之几）时钟偏移和高于 25 dB 的信噪比下将调制误差率提高了 3.4 dB，并将误码率降低了近一个数量级。广泛使用 Gardner-Farrow 的方法。MRBFNN 能够遵循系统动力学并进行概括，即使在训练阶段（不同的时钟偏移、信噪比等）和低阶调制方案（例如 32-QAM、16-QAM 和 QPSK（正交相位shift键控），无需再培训。由于 MRBFNN 架构的并行特性和推理所需的复杂性降低，它可以在硬件中高效实现并轻松集成到通信接收器中，代表了接收器时间同步的可行解决方案。