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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
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