用于体内纳米网络中神经尖峰通信的基于阵列的分子脉冲编码
Array-Based Molecular Pulse Encoding for Neuro-Spike Communication in Intra-Body Nano-networks
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中文总结 AI 辅助
本文针对体内纳米网络神经尖峰通信需严格时间同步的问题，提出基于分子脉冲阵列编码的方案，消除了严格同步需求，性能优于现有符号同步模型，通信速率提升75%-150%。
中文摘要 AI 辅助
本文研究一种神经尖峰通信系统，旨在利用辅助纳米机器弥合受损神经元之间的断裂连接。天然神经尖峰通信通常依赖瞬时尖峰速率和时间间隔来传递信息，但这类时间编码方案要求发射机与接收机之间实现精确时间同步，这对资源受限的纳米机器构成重大挑战。为解决该问题，未来体内纳米网络亟需开发可在低阶同步（如符号同步）下运行的通信方案。本文提出一种新型基于阵列的神经尖峰通信方案，信息通过纳米机器发射的不同分子脉冲的特定排列进行编码，通过基于这些发射序列而非精确时间来区分符号，从而消除了严格时间同步的需求。本文通过推导符号间干扰（ISI）概率、错误概率和可达通信速率的表达式，从理论上分析了所提方案的性能。分析与数值结果表明，在不同扩散系数下，本文的基于阵列的方案显著优于此前提出的符号同步模型，通信速率提升了75%至150%。
英文摘要
In this paper, we investigate a neuro-spike communication system designed to bridge severed connections between damaged neurons using auxiliary nano-machines. Natural neuro-spike communication typically relies on instantaneous spike rates and temporal intervals to convey information. However, these temporal encoding schemes require exact time synchronization between the transmitter and receiver, a requirement that poses a significant challenge for resource-constrained nano-machines. To address this issue, it is imperative for future intra-body nano-networks to develop communication schemes that operate under reduced-order synchronization (e.g., symbol-synchronized). In this paper, we propose a novel neuro-spike array-based communication scheme where information is encoded through the specific arrangement of distinct molecular pulses emitted by nano-machines. By distinguishing symbols based on the sequence of these emissions rather than their exact timing, the need for stringent time synchronization is eliminated. We theoretically analyze the performance of the proposed scheme by deriving expressions for the probability of inter-symbol interference (ISI), error probability, and the achievable communication rate. Analytical and numerical results demonstrate that our array-based scheme significantly outperforms previously proposed symbol-synchronized models, providing a 75% - 150% enhancement in the communication rate across various diffusion coefficients.
