Maximum Likelihood Estimation of SNR for Diffusion-Based Molecular Communication

Tiwari, Satish Kumar; Upadhyay, Prabhat K.

doi:10.1109/lwc.2016.2553034

Cited by 18 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[52] estimated the SNR in the MC system, which considered the noise induced by the ISI. In [52], the SNR was defined as…”

Section: B Channel Performance Governing Parametersmentioning

confidence: 99%

A Survey on Estimation Schemes in Molecular Communications

Huang,

Fang,

Yang

2021

Preprint

View full text Add to dashboard Cite

This survey paper focuses on the estimation schemes in molecular communications (MC) systems. The existing studies in estimation schemes can be divided into parameter estimation (e.g., distance, diffusion coefficient, and flow velocity) and channel estimation. In this paper, we present, for the first time, a comprehensive survey on i) distance estimation, since distance is the most widely estimated parameter in current studies, ii) estimation of other parameters (i.e. the parameters excluding distance), and iii) channel estimation that focuses on the channel impulse response (CIR). Moreover, we examine the noise that may impact on the estimation performance and the metrics applied to evaluate the performance of different estimation schemes. Numerical results are provided to compare the performance of different distance estimation schemes. In addition, future research directions in parameter estimation and channel estimation are identified and discussed.

show abstract

“…[52] estimated the SNR in the MC system, which considered the noise induced by the ISI. In [52], the SNR was defined as…”

Section: B Channel Performance Governing Parametersmentioning

confidence: 99%

A Survey on Estimation Schemes in Molecular Communications

Huang,

Fang,

Yang

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Thus, the decoding process is mainly affected by additive counting noise and inter-symbolinterference (ISI). Usually, the counting noise follows a Normal distribution [4] [5]. In such cases, classical Hamming-distance based forward error correction (FEC) codes can be applied [6].…”

Section: Current State-of-the-art 1molecule-rich Macro-systemmentioning

confidence: 99%

“…In the second simulation, we first consider the special case of our Hamming-LT code, i.e. the rateless Hamming-LT code of K = 4 and Ω = 0 may degenerate to a (7,4) Hamming code. Parameters of the code scheme are configured to v = 0.3 and δ = 0.5019.…”

Section: Hamming-lt Vs Lt Codementioning

confidence: 99%

Hamming–Luby rateless codes for molecular erasure channels

Wei

et al. 2020

Nano Communication Networks

View full text Add to dashboard Cite

Nano-scale molecular communications encode digital information into discrete macro-molecules. In many nano-scale systems, due to limited molecular energy, each information symbol is encoded into a small number of molecules. As such, information maybe lost in the process of diffusion-advection propagation through complex topologies and membranes. Existing work has considered Hamming-distance codes for additive counting noise and Hamming-weight codes for transposition errors. Current linear-group codes are not well suited to combating the aforementioned erasure errors. In this paper, we design a novel lowcomplexity erasure combating encoding scheme: the rateless Hamming-Luby Transform code. The proposed rateless code combines the superior efficiency of Hamming codes with the performance guarantee advantage of Luby Transform (LT) codes. We design an iterative soft decoding scheme via successive cancelation to further improve the performance. Numerical simulations show this new rateless code can outperform both standard LT and cascaded Hamming-LT (Raptor) codes, while incurring a lower decoder computational complexity, which is useful for the envisaged resources constrained nano-machines.

show abstract

“…In this paper, we use the Binomial distribution to accurately describe the number of received molecules at the absorbing receiver [18], [19]. In addition, the Poisson and Gaussian distributions are also used since they provide an approximation of the number of received molecules which is much easier to analyze [6], [7], [9]- [11], [13], [14], [20], [21]. However, note that the accuracy of the Poisson and Gaussian distributions does not always hold, as discussed in [18] and [22].…”

Section: Introductionmentioning

confidence: 99%

Optimal Detection Interval for Absorbing Receivers in Molecular Communication Systems with Interference

Cao¹,

Zlatanov²,

Yeoh³

et al. 2020

Preprint

View full text Add to dashboard Cite

We consider a molecular communication system comprised of a transmitter, an absorbing receiver, and an interference source. Assuming amplitude modulation, we analyze the dependence of the bit error rate (BER) on the detection interval, which is the time within one transmission symbol interval during which the receiver is active to absorb and detect the number of information-carrying molecules. We then propose efficient algorithms to determine the optimal detection interval that minimizes the BER of the molecular communication system assuming no inter-symbol interference (ISI). Simulation and numerical evaluations are provided to highlight further insights into the optimal results. For example, we demonstrate that the optimal detection interval can be very small compared to the transmission symbol interval. Moreover, our numerical results show that significant BER improvements are achieved by using the optimal detection interval for systems without and with ISI.

show abstract

Maximum Likelihood Estimation of SNR for Diffusion-Based Molecular Communication

Cited by 18 publications

References 11 publications

A Survey on Estimation Schemes in Molecular Communications

A Survey on Estimation Schemes in Molecular Communications

Hamming–Luby rateless codes for molecular erasure channels

Optimal Detection Interval for Absorbing Receivers in Molecular Communication Systems with Interference

Contact Info

Product

Resources

About