In molecular communications (MC), inter-symbol interference (ISI) and noise are key factors that degrade communication reliability. Although time-domain equalization can effectively mitigate these effects, it often entails high computational complexity concerning the channel memory. In contrast, frequency-domain equalization (FDE) offers greater computational efficiency but typically requires prior knowledge of the channel model. To address this limitation, this letter proposes FDE techniques based on long short-term memory (LSTM) neural networks, enabling temporal correlation modeling in MC channels to improve ISI and noise suppression. To eliminate the reliance on prior channel information in conventional FDE methods, a supervised training strategy is employed for channel-adaptive equalization. Simulation results demonstrate that the proposed LSTM-FDE significantly reduces the bit error rate compared to traditional FDE and feedforward neural network-based equalizers. This performance gain is attributed to the LSTM's temporal modeling capabilities, which enhance noise suppression and accelerate model convergence, while maintaining comparable computational efficiency.

翻译：在分子通信中，码间干扰和噪声是降低通信可靠性的关键因素。尽管时域均衡能有效缓解这些影响，但其通常涉及与信道记忆相关的高计算复杂度。相比之下，频域均衡具有更高的计算效率，但通常需要先验的信道模型知识。为解决这一局限，本文提出基于长短期记忆神经网络的频域均衡技术，通过建模分子通信信道中的时间相关性来改善码间干扰和噪声抑制。为消除传统频域均衡方法对先验信道信息的依赖，采用监督训练策略实现信道自适应均衡。仿真结果表明，与传统频域均衡及基于前馈神经网络的均衡器相比，所提出的LSTM-FDE方法显著降低了误码率。这一性能提升归因于LSTM的时间建模能力，其增强了噪声抑制效果并加速了模型收敛，同时保持了相当的计算效率。