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

Channel Estimation for Diffusive MIMO Molecular Communications

In diffusion-based communication, as for molecular systems, the achievable data rate is very low due to the slow nature of diffusion and the existence of severe inter-symbol interference (ISI). Multiple-input multiple-output (MIMO) technique can be used to improve the data rate. Knowledge of channel impulse response (CIR) is essential for equalization and detection in MIMO systems. This paper presents a training-based CIR estimation for diffusive MIMO (D-MIMO) channels. Maximum likelihood and least-squares estimators are derived, and the training sequences are designed to minimize the corresponding Cram\'er-Rao bound. Sub-optimal estimators are compared to Cram\'er-Rao bound to validate their performance.Comment: 5 pages, 5 figures, EuCNC 201

Detection Algorithms for Molecular MIMO

In this paper, we propose a novel design for molecular communication in which both the transmitter and the receiver have, in a 3-dimensional environment, multiple bulges (in RF communication this corresponds to antenna). The proposed system consists of a fluid medium, information molecules, a transmitter, and a receiver. We simulate the system with a one-shot signal to obtain the channel's finite impulse response. We then incorporate this result within our mathematical analysis to determine interference. Molecular communication has a great need for low complexity, hence, the receiver may have incomplete information regarding the system and the channel state. Thus, for the cases of limited information set at the receiver, we propose three detection algorithms, namely adaptive thresholding, practical zero forcing, and Genie-aided zero forcing.Comment: 6 pages, 6 figures, 2015 IEEE ICC accepte

Multiple Antenna Techniques for Terahertz Nano-Bio Communication

Using higher frequency bands becomes an essential demand resulting from the explosive wireless traffic needs and the spectrum shortage of the currently used bands. This paper presents an overview on the terahertz technology and its application in the area of multi-input multi-output antenna system and in-vivo nano-communication. In addition, it presents a preliminary study on applying multiple input-single output (MISO) antenna technique to investigate the signal propagation and antenna diversity techniques inside the human skin tissues, which is represented by three layers: stratum corneum (SC), epidermis, and dermis layers, in the terahertz (THz) frequency range (0.8-1.2) THz. The spatial antenna diversity is investigated in this study to understand MISO system performance for two different in-vivo channels resulting from the signal propagation between two transmitting antennas, located at the dermis layer, and one receiving antenna, located at epidermis layer. Three techniques are investigated: selection combining (SC), equal-gain combing (EGC), and maximum-ratio combining (MRC). The initial study indicates that using multiple antenna technique with THz might be not useful for in-vivo nano-communication