Molecular communication (MC) enables information transfer through molecules at the nano-scale. This paper presents new and optimized source coding (data compression) methods for MC. In a recent paper, prefix source coding was introduced into the field, through an MC-adapted version of the Huffman coding. We first show that while MC-adapted Huffman coding improves symbol error rate (SER), it does not always produce an optimal prefix codebook in terms of coding length and power. To address this, we propose optimal molecular prefix coding (MoPC). The major result of this paper is the Molecular Arithmetic Coding (MoAC), which we derive based on an existing general construction principle for constrained arithmetic channel coding, equipping it with error correction and data compression capabilities for any finite source alphabet. We theoretically and practically show the superiority of MoAC to SAC, our another adaptation of arithmetic source coding to MC. However, MoAC's unique decodability is limited by bit precision. Accordingly, a uniquely-decodable new coding scheme named Molecular Arithmetic with Prefix Coding (MoAPC) is introduced. On two nucleotide alphabets, we show that MoAPC has a better compression performance than MoPC. MC simulation results demonstrate the effectiveness of the proposed methods.

翻译：分子通信（MC）能够在纳米尺度上通过分子实现信息传输。本文为MC提出了新颖且优化的信源编码（数据压缩）方法。在近期的一篇论文中，通过一种适用于MC的霍夫曼编码变体，前缀信源编码被引入该领域。我们首先证明，虽然MC适配的霍夫曼编码改善了符号错误率（SER），但它在编码长度和功率方面并不总能产生最优的前缀码本。为解决此问题，我们提出了最优分子前缀编码（MoPC）。本文的主要成果是分子算术编码（MoAC），我们基于一种现有的、用于受限算术信道编码的通用构造原理推导出该编码，使其能够为任何有限信源字母表提供纠错和数据压缩能力。我们从理论和实践上证明了MoAC优于SAC（我们为MC适配的另一种算术信源编码）。然而，MoAC的唯一可译性受限于比特精度。因此，我们引入了一种新的、具有唯一可译性的编码方案，称为带前缀编码的分子算术编码（MoAPC）。在两个核苷酸字母表上，我们表明MoAPC具有比MoPC更好的压缩性能。MC仿真结果验证了所提方法的有效性。