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 co...
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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 codinglength 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 optimized MoPC. MC simulation results demonstrate the effectiveness of the proposed methods.
The effect of transition noise on the soft bit error rate (BER) and the signal-to-noise ratio (SNR) in magnetooptic (MO) recording systems is studied, With high-density MO recording, transition noise and intersymbol i...
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The effect of transition noise on the soft bit error rate (BER) and the signal-to-noise ratio (SNR) in magnetooptic (MO) recording systems is studied, With high-density MO recording, transition noise and intersymbol interference (ISI) become problems, To study the effect of transition noise on the BER, we model the MO channel in the presence of transition noise, We develop separate models for the peak-position modulation (PPM) and pulse-width modulation (PWM) in order to understand the differences in the performances of the two different writing formats. We also describe how run-length-limited (RLL) coding can be incorporated into the models, Using the models developed, we study the effect of transition noise on the BER and the SNR for the decision feedback equalizer (DFE) method and the adaptive threshold detection scheme, Our study indicates that the DFE yields better SNR than the adaptive threshold detector, Also, the BER of PWM is better than the BER of PPM for the same user density, This implies that higher user densities can be achieved with PWM than with PPM for the same quality disk.
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