Concentration shift keying (CSK) is a widely studied modulation technique for molecular communication-based nanonetworks, which is a key enabler for the Internet of Bio-NanoThings (IoBNT). Existing CSK methods, while offering optimal error performance, suffer from increased operational complexity that scales poorly as the number of transmitters, $K$, grows. In this study, a novel $M$-ary CSK method is proposed: CSK with common detection thresholds (CSK-CT). CSK-CT uses \textit{common} thresholds, set sufficiently low to guarantee the reliable detection of symbols from all transmitters, regardless of distance. Closed-form expressions are derived to obtain the common thresholds and release concentrations. To further enhance error performance, the release concentration is optimized using a scaling exponent that also optimizes the common thresholds. The performance of CSK-CT is evaluated against the benchmark CSK across various $K$ and $M$ values. CSK-CT has an error probability between $10^{-7}$ and $10^{-4}$, which is a substantial improvement from that of the benchmark CSK (from $10^{-4}$ to $10^{-3}$). In terms of complexity, CSK-CT is $\mathcal{O}\big(n\big)$ and does not scale with $K$ but $M$ ($M\ll K$), whereas the benchmark is $\mathcal{O}\big(n^2\big)$. Furthermore, CSK-CT can mitigate inter-symbol interference (ISI), although this facet merits further investigation. Owing to its low error rates, improved scalability, reduced complexity, and potential ISI mitigation features, CSK-CT is particularly advantageous for IoBNT applications focused on data gathering. Its effectiveness is especially notable in scenarios where a computationally limited receiver is tasked with collecting vital health data from multiple transmitters.

翻译：浓度偏移键控(CSK)是分子通信纳米网络中广泛研究的调制技术，该技术是生物纳米物联网(IoBNT)的关键使能者。现有CSK方法虽能提供最优误码性能，但随着发射机数量K的增加，其操作复杂度会急剧上升。本研究提出一种新型多进制CSK方法：具有公共检测阈值的浓度偏移键控(CSK-CT)。CSK-CT采用足够低的公共阈值，确保能够可靠检测所有发射机（无论距离远近）发送的符号。通过推导闭合表达式获得公共阈值与释放浓度。为进一步提升误码性能，利用缩放指数优化释放浓度，同时实现公共阈值的最优配置。在不同K和M值下，将CSK-CT与基准CSK进行性能对比。CSK-CT的误码率介于10^{-7}至10^{-4}之间，相较基准CSK（10^{-4}至10^{-3}）有显著提升。在复杂度方面，CSK-CT为O(n)且仅随M增长而非K（M≪K），而基准方法的复杂度为O(n^2)。此外，CSK-CT可缓解符号间干扰(ISI)，但该特性尚需进一步研究。凭借其低误码率、优良可扩展性、低复杂度及潜在的ISI抑制能力，CSK-CT特别适用于面向数据采集的IoBNT应用场景，尤其在计算受限接收机需从多个发射机收集关键健康数据时优势显著。