AI 中文总结
本研究针对超奈奎斯特信号检测问题，提出一种低复杂度双向单通路Elman循环神经网络检测器，其误码率接近M-BCJR算法，可降低硬件成本且无需复杂运算，是首个将经典Elman网络用于该场景的研究。
AI 中文摘要
本研究针对采用超奈奎斯特（Faster-than-Nyquist, FTN）信号传输的二进制相移键控（Binary Phase-Shift Keying, BPSK）信号，提出一种低复杂度双向单通路Elman循环神经网络检测器。由于超奈奎斯特信号的码间干扰具有短有限记忆特性，不含门控机制的经典Elman循环神经网络是一种足够且参数高效的模型。所提检测器在单次传输中同时沿前向和后向方向处理接收序列，形成最优BCJR前向后向递归的学习型对应方案。在加性高斯白噪声（Additive White Gaussian Noise, AWGN）信道下采用根升余弦（Root-Raised-Cosine, RRC）脉冲的场景中，针对两种压缩因子的仿真结果显示，仅含25至65个可训练参数的所提检测器，其误码率与M-BCJR算法非常接近，同时查找表（Look-Up-Table, LUT）硬件成本降低38%至46%，且无需显式除法或指数运算；更紧凑的配置可在性能略有损失的情况下实现高达67%的成本降低。据作者所知，这是首个将经典Elman循环神经网络架构应用于超奈奎斯特检测问题的研究。
英文摘要
This study proposes a low-complexity, bidirectional, single-pass Elman recurrent neural network detector for binary phase-shift keying signals transmitted with faster-than-Nyquist signaling. Since the faster-than-Nyquist intersymbol interference has a short, finite memory, the classical Elman recurrent neural network, which contains no gating mechanism, is a sufficient and parameter-efficient model. The proposed detector processes the received sequence in both forward and backward directions in a single pass, forming a learned counterpart of the optimal BCJR forward-backward recursion. Under a root-raised-cosine pulse over an additive white Gaussian noise channel, simulations for two compression factors show that the proposed detector, with only twenty-five to sixty-five trainable parameters, attains a bit error rate very close to that of the M-BCJR algorithm, while reducing the look-up-table hardware cost by thirty-eight to forty-six percent and using no explicit division or exponential operations. A more compact configuration offers up to a sixty-seven percent reduction at a small performance penalty. To the best of our knowledge, this is the first study to investigate the classical Elman recurrent neural network architecture for the faster-than-Nyquist detection problem.
Comments4 pages, 3 figures, 3 tables
