20,531 research outputs found
Maximum Likelihood Detection for Cooperative Molecular Communication
In this paper, symbol-by-symbol maximum likelihood (ML) detection is proposed
for a cooperative diffusion-based molecular communication (MC) system. In this
system, a fusion center (FC) chooses the transmitter's symbol that is more
likely, given the likelihood of the observations from multiple receivers (RXs).
We propose three different ML detection variants according to different
constraints on the information available to the FC, which enables us to
demonstrate trade-offs in their performance versus the information available.
The system error probability for one variant is derived in closed form.
Numerical and simulation results show that the ML detection variants provide
lower bounds on the error performance of the simpler cooperative variants and
demonstrate that majority rule detection has performance comparable to ML
detection when the reporting is noisy.Comment: 7 pages, 4 figurs. This work has been accepted by the IEEE ICC 201
Symbol-by-Symbol Maximum Likelihood Detection for Cooperative Molecular Communication
In this paper, symbol-by-symbol maximum likelihood (ML) detection is proposed
for a cooperative diffusion-based molecular communication (MC) system. In this
system, the transmitter (TX) sends a common information symbol to multiple
receivers (RXs) and a fusion center (FC) chooses the TX symbol that is more
likely, given the likelihood of its observations from all RXs. The transmission
of a sequence of binary symbols and the resultant intersymbol interference are
considered in the cooperative MC system. Three ML detection variants are
proposed according to different RX behaviors and different knowledge at the FC.
The system error probabilities for two ML detector variants are derived, one of
which is in closed form. The optimal molecule allocation among RXs to minimize
the system error probability of one variant is determined by solving a joint
optimization problem. Also for this variant, the equal distribution of
molecules among two symmetric RXs is analytically shown to achieve the local
minimal error probability. Numerical and simulation results show that the ML
detection variants provide lower bounds on the error performance of simpler,
non-ML cooperative variants and demonstrate that these simpler cooperative
variants have error performance comparable to ML detectors.Comment: 15 pages, 7 figures; submission for possible IEEE publication. arXiv
admin note: text overlap with arXiv:1704.0562
Simplified Cooperative Detection for Multi-Receiver Molecular Communication
Diffusion-based molecular communication (MC) systems experience significant
reliability losses. To boost the reliability, an MC scheme where multiple
receivers (RXs) work cooperatively to decide the signal of a transmitter (TX)
by sending the same type of molecules to a fusion center (FC) is proposed in
this paper. The FC observes the total number of molecules received and compares
this number with a threshold to determine the TX's signal. The proposed scheme
is more bio-realistic and requires relatively low computational complexity
compared to existing cooperative schemes where the RXs send and the FC
recognizes different types of molecules. Asymmetric and symmetric topologies
are considered, and closed-form expressions are derived for the global error
probability for both topologies. Results show that the trade-off for simplified
computations leads to a slight reduction in error performance, compared to the
existing cooperative schemes.Comment: 5 pages, 4 figures, Will be presented as an invited paper at the 2017
IEEE Information Theory Workshop in November 2017 in Kaohsiung, Taiwa
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
Optimal Receiver Design for Diffusive Molecular Communication With Flow and Additive Noise
In this paper, we perform receiver design for a diffusive molecular
communication environment. Our model includes flow in any direction, sources of
information molecules in addition to the transmitter, and enzymes in the
propagation environment to mitigate intersymbol interference. We characterize
the mutual information between receiver observations to show how often
independent observations can be made. We derive the maximum likelihood sequence
detector to provide a lower bound on the bit error probability. We propose the
family of weighted sum detectors for more practical implementation and derive
their expected bit error probability. Under certain conditions, the performance
of the optimal weighted sum detector is shown to be equivalent to a matched
filter. Receiver simulation results show the tradeoff in detector complexity
versus achievable bit error probability, and that a slow flow in any direction
can improve the performance of a weighted sum detector.Comment: 14 pages, 7 figures, 1 appendix. To appear in IEEE Transactions on
NanoBioscience (submitted July 31, 2013, revised June 18, 2014, accepted July
7, 2014
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