In emerging Internet-of-Nano-Things (IoNT) and molecular communication (MC), digital information will be embedded in chemical molecules in order to propagate through complex and absorbing media. However, two main challenges in signal detection are the intensive inter-symbol-interference (ISI) and the severe background noise, which may heavily deteriorate the reliability of molecular communications. This may be even worse when facing unknown channel impulse response (CIR), which is common in the industrial practice of MCs, rendering most of coherent signal detection schemes in traditional wireless communications less attractive. In this paper, we propose an improved non-coherent signal detection scheme, aiming at addressing ISI and noise contamination in the absence of CIR. Specifically, we utilized three non-coherent metrics to characterize transient features of received signals, and furthermore, designed an optimized combination scheme for more reliable signal detections. Given that the significance of each non-coherent metric remains unknown, we devise a self-adaption method premised on the concept of deflection coefficient, which can recursively determine the optimal combination weights. Numerical simulations demonstrate that our proposed scheme significantly improves the detection performance in terms of bit error rate (BER), and outperforms the adaptive threshold detection (ATD) method over 7 dB.

在新兴的物联网（IoNT）和分子通信（MC）中，数字信息将被嵌入化学分子中，以便通过复杂的吸收性介质传播。但是，信号检测中的两个主要挑战是强烈的符号间干扰（ISI）和严重的背景噪声，这可能会严重降低分子通信的可靠性。当面对未知信道冲激响应（CIR）时，情况甚至更糟，这在MC的工业实践中很常见，这使得传统无线通信中的大多数相干信号检测方案都缺乏吸引力。在本文中，我们提出了一种改进的非相干信号检测方案，旨在解决在没有CIR的情况下的ISI和噪声污染。特别，我们利用三个非相干度量来表征接收信号的瞬态特征，此外，还设计了一种优化的组合方案以实现更可靠的信号检测。鉴于每个非相干度量的重要性仍然未知，我们设计了一种基于偏转系数概念的自适应方法，该方法可以递归确定最佳组合权重。数值仿真表明，我们提出的方案在误码率（BER）方面显着提高了检测性能，并且在7 dB的性能上优于自适应阈值检测（ATD）方法。我们设计了一种基于挠度系数概念的自适应方法，该方法可以递归确定最佳组合权重。数值仿真表明，我们提出的方案在误码率（BER）方面显着提高了检测性能，并且在7 dB的性能上优于自适应阈值检测（ATD）方法。我们设计了一种基于挠度系数概念的自适应方法，该方法可以递归确定最佳组合权重。数值仿真表明，我们提出的方案在误码率（BER）方面显着提高了检测性能，并且在7 dB的性能上优于自适应阈值检测（ATD）方法。