4,091 research outputs found
Diffusive MIMO Molecular Communications: Channel Estimation, Equalization and Detection
In diffusion-based communication, as for molecular systems, the achievable
data rate is low due to the stochastic nature of diffusion which exhibits a
severe inter-symbol-interference (ISI). Multiple-Input Multiple-Output (MIMO)
multiplexing improves the data rate at the expense of an inter-link
interference (ILI). This paper investigates training-based channel estimation
schemes for diffusive MIMO (D-MIMO) systems and corresponding equalization
methods. Maximum likelihood and least-squares estimators of mean channel are
derived, and the training sequence is designed to minimize the mean square
error (MSE). Numerical validations in terms of MSE are compared with Cramer-Rao
bound derived herein. Equalization is based on decision feedback equalizer
(DFE) structure as this is effective in mitigating diffusive ISI/ILI.
Zero-forcing, minimum MSE and least-squares criteria have been paired to DFE,
and their performances are evaluated in terms of bit error probability. Since
D-MIMO systems are severely affected by the ILI because of short transmitters
inter-distance, D-MIMO time interleaving is exploited as countermeasure to
mitigate the ILI with remarkable performance improvements. The feasibility of a
block-type communication including training and data equalization is explored
for D-MIMO, and system-level performances are numerically derived.Comment: Accepted paper at IEEE transaction on Communicatio
Observation of coherent delocalized phonon-like modes in DNA under physiological conditions
Underdamped terahertz-frequency delocalized phonon-like modes have long been suggested to play a role in the biological function of DNA. Such phonon modes involve the collective motion of many atoms and are prerequisite to understanding the molecular nature of macroscopic conformational changes and related biochemical phenomena. Initial predictions were based on simple theoretical models of DNA. However, such models do not take into account strong interactions with the surrounding water, which is likely to cause phonon modes to be heavily damped and localized. Here we apply state-of-the-art femtosecond optical Kerr effect spectroscopy, which is currently the only technique capable of taking low-frequency (GHz to THz) vibrational spectra in solution. We are able to demonstrate that phonon modes involving the hydrogen bond network between the strands exist in DNA at physiologically relevant conditions. In addition, the dynamics of the solvating water molecules is slowed down by about a factor of 20 compared with the bulk
High-dimensional metric combining for non-coherent molecular signal detection
In emerging Internet-of-Nano-Thing (IoNT), information will be embedded and conveyed in the form of molecules through complex and diffusive medias. One main challenge lies in the long-tail nature of the channel response causing inter-symbolinterference (ISI), which deteriorates the detection performance. If the channel is unknown, existing coherent schemes (e.g., the state-of-the-art maximum a posteriori, MAP) have to pursue complex channel estimation and ISI mitigation techniques, which will result in either high computational complexity, or poor estimation accuracy that will hinder the detection performance. In this paper, we develop a novel high-dimensional non-coherent detection scheme for molecular signals. We achieve this in a higher-dimensional metric space by combining different noncoherent metrics that exploit the transient features of the signals. By deducing the theoretical bit error rate (BER) for any constructed high-dimensional non-coherent metric, we prove that, higher dimensionality always achieves a lower BER in the same sample space, at the expense of higher complexity on computing the multivariate posterior densities. The realization of this high-dimensional non-coherent scheme is resorting to the Parzen window technique based probabilistic neural network (Parzen-PNN), given its ability to approximate the multivariate posterior densities by taking the previous detection results into a channel-independent Gaussian Parzen window, thereby avoiding the complex channel estimations. The complexity of the posterior computation is shared by the parallel implementation of the Parzen-PNN. Numerical simulations demonstrate that our proposed scheme can gain 10dB in SNR given a fixed BER as 10-4, in comparison with other state-of-the-art methods
Diffusive Molecular Communications with Reactive Signaling
This paper focuses on molecular communication (MC) systems where the
signaling molecules may participate in a reversible bimolecular reaction in the
channel. The motivation for studying these MC systems is that they can realize
the concept of constructive and destructive signal superposition, which leads
to favorable properties such as inter-symbol interference (ISI) reduction and
avoiding environmental contamination due to continuous release of molecules
into the channel. This work first derives the maximum likelihood (ML) detector
for a binary MC system with reactive signaling molecules under the assumption
that the detector has perfect knowledge of the ISI. The performance of this
genie-aided ML detector yields an upper bound on the performance of any
practical detector. In addition, two suboptimal detectors of different
complexity are proposed. The proposed ML detector as well as one of the
suboptimal detectors require the channel response (CR) of the considered MC
system. Moreover, the CR is needed for the performance evaluation of all
proposed detectors. However, analyzing MC with reactive signaling is
challenging since the underlying partial differential equations that describe
the reaction-diffusion mechanism are coupled and non-linear. Therefore, an
algorithm is developed in this paper for efficient computation of the CR to any
arbitrary transmit symbol sequence. The accuracy of this algorithm is validated
via particle-based simulation. Simulation results using the developed CR
algorithm show that the performance of the proposed suboptimal detectors can
approach that of the genie- aided ML detector. Moreover, these results show
that MC systems with reactive signaling have superior performance relative to
those with non-reactive signaling due to the reduction of ISI enabled by the
chemical reactions.Comment: This paper has been submitted to IEEE International Conference on
Communications (ICC) 201
Non-linear signal detection for molecular communications
Molecular communications convey information via diffusion propagation. The inherent long-tail channel response causes severe inter-symbol interference, which may seriously degrade signal detection performances. Traditional linear signal detection techniques, unfortunately, require both high complexity and a high signal-to-noise (SNR) ratio to operate. In this paper, we proposed a new non-linear signal processing paradigm inspired by the biological systems that achieves low-complexity signal detection even in low SNR regimes. First, we introduce a stochastic resonance inspired non-linear filtering scheme for molecular communications, and show that it significantly improves the output SNR by transforming the noise energy into useful signals. Second, we design a novel non-coherent detector by exploiting the transient features of molecular signaling, which are independent of channel response and involves only lowcomplexity linear summation operations. Numerical simulations show that this new scheme can improve the detection performance remarkably (approx. 7dB gain), even when compared against linearly optimal coherent methods. This is one of the first attempts to demodulate molecular signals from an entirely biological point of view, and the designed non-linear noncoherent paradigm will provide significant potential to the design and future implementation of nano-systems in noisy biological environments
Symbol Synchronization for Diffusive Molecular Communication Systems
Symbol synchronization refers to the estimation of the start of a symbol
interval and is needed for reliable detection. In this paper, we develop a
symbol synchronization framework for molecular communication (MC) systems where
we consider some practical challenges which have not been addressed in the
literature yet. In particular, we take into account that in MC systems, the
transmitter may not be equipped with an internal clock and may not be able to
emit molecules with a fixed release frequency. Such restrictions hold for
practical nanotransmitters, e.g. modified cells, where the lengths of the
symbol intervals may vary due to the inherent randomness in the availability of
food and energy for molecule generation, the process for molecule production,
and the release process. To address this issue, we propose to employ two types
of molecules, one for synchronization and one for data transmission. We derive
the optimal maximum likelihood (ML) symbol synchronization scheme as a
performance upper bound. Since ML synchronization entails high complexity, we
also propose two low-complexity synchronization schemes, namely a peak
observation-based scheme and a threshold-trigger scheme, which are suitable for
MC systems with limited computational capabilities. Our simulation results
reveal the effectiveness of the proposed synchronization~schemes and suggest
that the end-to-end performance of MC systems significantly depends on the
accuracy of symbol synchronization.Comment: This paper has been accepted for presentation at IEEE International
Conference on Communications (ICC) 201
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