Due to the nonlinear distortion caused by radio-frequency (RF) components in the transceiver, detecting transmitted symbols for multiple-input and multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems can be challenging and resource consuming. In this work, we introduce a Deep Echo State Network (DESN) to serve as the symbol detector for 5G communication networks. Our DESN employs memristive synapses as the dynamic reservoir layer to accelerate the learning algorithm and computation. By cascading multiple dynamic reservoir layers in a hierarchical processing structure, our DESN processes received signal in both spatial and temporal domains. The resulting hybrid memristor-CMOS co-design provides the nonlinear computation required by the reservoir layer while significantly reduces the power consumption. From the benchmark on nonlinear system prediction, our DESN exhibits 10.31 X reduction on the prediction error compared to state-of-the-art neural network designs. Moreover, our DESN records a bit error rate (BER) of $5.76 \times 10^{-2}$ on the high-speed transmitted symbol detection task for MIMO-OFDM systems, yielding 47.73% more precise than state-of-the-art techniques in the literate for 5G communication networks.

中文翻译：

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迈向智能：通过深度回声状态网络检测 5G 系统上的符号

由于收发器中的射频 (RF) 组件会导致非线性失真，因此检测多输入多输出正交频分复用 (MIMO-OFDM) 系统的传输符号可能具有挑战性且消耗资源。在这项工作中，我们引入了深度回声状态网络 (DESN) 作为 5G 通信网络的符号检测器。我们的DESN采用忆阻突触作为动态存储层来加速学习算法和计算。通过在分层处理结构中级联多个动态储层，我们的 DESN 处理在空间和时间域中接收到的信号。由此产生的混合忆阻器-CMOS 协同设计提供了储层所需的非线性计算，同时显着降低了功耗。根据非线性系统预测的基准，我们的 DESN 与最先进的神经网络设计相比，预测误差减少了 10.31 倍。此外，我们的 DESN 在 MIMO-OFDM 系统的高速传输符号检测任务中记录了 5.76 美元\times 10^{-2}$ 的误码率 (BER)，比目前的精度高 47.73%。 5G 通信网络文学艺术技术。