arXiv subjects

Melih Şahin

Publications and source records attributed to Melih Şahin.

5 recordsLinked to original sources

arXiv2026

Low-Complexity Run-Length-Limited ISI-Mitigation (RLIM) Codes for Molecular Communication

Molecular communication suffers from severe inter-symbol interference, which makes constrained coding essential for reliable transmission. Run-length-limited ISI-mitigation codes are attractive because they select low-weight constrained codebooks, reducing ISI while allowing more molecules to be assigned to each transmitted $1$-symbol under the usual molecular-communication normalization. Previous results showed strong bit-error-rate performance for these codes, but their original realization required full codebook generation and storage. This exponential storage growth is unsuitable for resource-constrained molecular communication channels and also limits the exploration of larger information dimensions. This is particularly important for nano-scale molecular communication, where transmitter and receiver nodes are expected to operate under severe memory and computational constraints. This paper removes that realization bottleneck by replacing full codebook storage with an enumerative realization based on Cover's ranking framework, constant-weight run-length-limited counting, and cumulative weight-layer offsets. The resulting encoder and decoder preserve the selected RLIM codebooks and the original projection-based decoding behavior while storing only polynomial-size counting tables. Storage and runtime measurements confirm the resulting exponential-to-polynomial reduction, while diffusion-based molecular-communication simulations show that the newly accessible larger information dimensions can improve the best mean bit-error-rate performance attainable by individual RLIM orders in several tested channel regimes.

cs.IT↗

arXiv2026

Masked Neural Detection for Run-Length-Limited Channel Coding in Molecular Communication

Molecular communication (MC) suffers from severe diffusion memory because molecules released for one symbol may arrive during later symbol intervals. Neural sequence detectors, especially sliding bidirectional recurrent neural networks (SBRNNs), substantially outperform threshold detection in such channels. This raises a central question for MC channel coding: does a code whose superiority was established under threshold detection retain it when both coded and uncoded transmission are evaluated with neural detection? This letter answers this question for run-length-limited ISI-mitigation (RLIM) codes by proposing a decoder-aware training mask that removes the positions the RLIM decoder has a high probability of deterministically overwriting, steering compact-SBRNN capacity toward the information-bearing positions. The masked RLIM$_2$-SBRNN beats the best uncoded receiver (threshold or SBRNN) at 40 of 57 operating points; gains peak at 43$\times$ under favorable channels, while losses, confined to the most adverse, never exceed 2.7$\times$. Masking improves the unmasked RLIM$_2$-SBRNN in 56 of 57 matched comparisons. Finally, with storage counted equally in SBRNN weights and MLSE table entries, the masked RLIM$_2$-SBRNN is the more accurate receiver up to a few thousand stored values despite using no channel knowledge; channel-state-aware uncoded MLSE moves ahead only beyond tens of thousands.

cs.IT↗

arXiv2026

Local Differential Privacy for Molecular Communication Networks

Molecular communication (MC) enables information exchange in nanoscale sensor networks operating in biological environments, yet privacy remains largely unaddressed. We integrate local differential privacy (LDP) into diffusion-based MC by privatizing each user's measurement at the transmitter and conveying the resulting randomized report over the MC channel. To our knowledge, this is the first systematic LDP implementation for diffusion-based MC, enabling privacy-preserving aggregate data analysis for in-body health monitoring and other population-scale sensing applications. We benchmark major LDP mechanisms under a realistic channel model. Simulation results show that k-ary Randomized Response (KRR) and Optimized Local Hashing (OLH) achieve the lowest average $\ell_1$ distribution-estimation error under the MC channel: OLH is preferable when channel resources are sufficient and the number of possible user values (alphabet size) $k$ is moderate to large, whereas the KRR is more robust as the MC transmission quality deteriorates. We further propose RLIM-LDP, which combines run-length-limited ISI-mitigation (RLIM) coding with LDP coding. Extensive simulation results demonstrate that RLIM-LDP improves end-to-end reliability and reduces the final distribution-estimation error when time and molecule resources are limited.

cs.IT↗

arXiv2026

Run-Length-Limited ISI-Mitigation (RLIM) Coding for Molecular Communication

Inter-symbol interference (ISI) limits reliability in diffusion-based molecular communication (MC) channels. We propose RLIM, a family of run-length-limited (RLL) codes that form fixed-size codebooks by minimizing the total number of 1-bits, increasing the per-symbol molecule budget under standard power normalizations and thus improving reliability. We develop a provably optimal linear-time greedy decoder that is equivalent to Viterbi decoding under a deterministic last-wins tie-break and has lower computational complexity; empirically, it outperforms first-wins and random Viterbi variants on RLL baselines. Extensive binomial and particle-tracking simulations show that RLIM achieves lower bit error rate (BER) than classical RLL and other prominent coding schemes across a broad range of scenarios.

cs.IT↗

arXiv2024

Molecular Arithmetic Coding (MoAC) and Optimized Molecular Prefix Coding (MoPC) for Diffusion-Based Molecular Communication

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.

cs.IT↗