Nano Communication Networks最新文献

This paper presents a highly efficient tunable dipole antenna with omnidirectional radiation. The main radiator of the hybrid dipole is designed using a perfect electric conductor, whereas tunability has been achieved using graphene strips in the antenna’s proximity. The dipole antenna resonates at 1.3785 THz and provides a bandwidth (BW) of 8.58% for the graphene’s chemical potential (${\mu }_c$) equal to 0.6 eV. The peak gain and total efficiency (${\eta }_{Total}$) are 1.46 dBi and 83.13%, respectively. The proposed dipole provides tunability from 1.32 to 1.411 THz by varying ${\mu }_c$ from 0.4 to 0.7 eV. Further, a compact dipole-driven tunable Yagi–Uda antenna has been designed with end-fire radiation. The proposed Yagi–Uda antenna has a size of only 90 $\mu$m $\times$ 60 $\mu$m, i.e., 0.61${\lambda }_g$ $\times 0.38{\lambda }_g$, where ${\lambda }_g$ is the guided wavelength calculated at 1.3631 THz and provides tunability from 1.328 to 1.5 THz. The peak gain, front-to-back ratio (FBR) and ${\eta }_{Total}$ at 1.3631 THz for the ${\mu }_c$ = 0.6 eV are found to be 4.93 dBi, 17.3 dB, and 63.36%, respectively. A practical parallel plate DC biasing configuration with a common ground plane has also been proposed to independently tune the ${\mu }_c$ of each element in the passive Yagi–Uda array. The proposed Yagi antenna provides reasonable gain and FBR to cater for high propagation loss in the terahertz regime.

Transmitting information in engineered neural communication systems is a promising solution to delay-sensitive applications for the Internet of Bio-Nanothings (IoBNTs). As widely used in wired and wireless communication systems, introducing multiplexing into neural communication system could improve channel transmission efficiency. In this paper, we model a neural communication system for IoBNTs and propose a neural signal multiplexing scheme for this system, based on frequency-division multiplexing (FDM) principles. The whole system including channel modeling, neural encoding, demultiplexing scheme, and decoding method using kernel density estimation (KDE) are presented. The optimal parameters for KDE and bit error probability are analyzed, and the performance of the proposed strategy is evaluated in terms of error rate and mutual information rate. The work can help researchers better understanding the underlying mechanism of neural multiplexing and pave the way for the implementation of IoBNT applications.

Benefiting by the fast development of nanotechnology, molecular communication (MC) has received great attention in recent years. In many potential applications of MC, such as drug delivery and pollution prevention, it is essential to locate or trace the target. In this paper, we consider a 3D diffusive MC environment consisting of several obstacles, a molecule-releasing source (RS) and a mobile molecule sensor (MS) which aims to find the RS within a time constraint. The problem is reformulated using Markov Decision Process (MDP) and an adaptive multi-layer reward based Q-Learning (AMR-Q Learning) approach is proposed. By exploiting information from the number of received molecules and adaptively setting multi-layer rewards, MS with AMR-Q Learning can find the RS efficiently, unlike the gradient based method which is usually trapped in locally optimal points. Numerical results demonstrate that the proposed AMR-Q Learning approach outperforms existing path-planning schemes with significantly reduced training overhead.

In this paper, graphene based tunable bandpass and bandstop filters are designed in terahertz frequency regime using a triangular microstrip patch resonator. Initially, a bandpass filter is designed at an operating frequency of 1.65 THz with a bandwidth of 65 GHz. Further a dual-band bandstop filter is designed with resonance frequencies of 1.25 THz and 2.14 THz. In both designs, the triangular patch is coupled to the transmission line to achieve bandpass and bandstop characteristics. A graphene layer is deposited between the dielectric and the conductor layer to enhance the propagation of plasmonic waves. The simulation results reveal that the designed filters are capable of achieving the desired frequency response. By varying the graphene’s chemical potential, a shift in the transmission response’s resonance frequency is observed. The proposed filters have the potential to be used as key components for future terahertz band communications systems.

Monitoring of patient’s health in the medical industry can be enabled using wireless body area networks (WBANs), which are already used for various purposes, including assisting in human safety. It is imperative to use better power management strategies since the body sensors are small and the battery cannot hold a charge for a long time. Due to the vast amounts of information generated by medical sensors, resource-constrained networks face a significant challenge when guaranteeing the specified quality of service (QoS). Moreover, the WBAN regularly meets the primary hassle of QoS degradation because of congestion WBAN structure can easily compromise heterogeneous and complex networks. Either inappropriate data collection or using energy effectively to transmit medical data without the expense of travel and length has become an important one. To address this issue, the present research work ‘Link Quality and Energy Efficient Optimal Clustering-Multipath (LEOC-MP)’ scheme tries to explore an answer. The main goals of the LEOC-MP (Optimal Link Quality and Energy Efficient Optimal Clustering-Multipath) system are to guarantee node-to-node link quality, lengthen network life, and compute high-performing cluster heads to guarantee reliable multi path data transfer. This work was executed in three phases. First, an optimal simplified clustering technique for data collection from body sensors using an improved pelican optimization (ICO) algorithm is introduced. Next, multiple design constraints for node rank computation, energy efficiency, link quality, path loss, distance, and delay are used. Besides, an Auto-Regressive Probabilistic Neural Network (AR-PNN) is introduced to optimize those design constraints and compute the cluster head (CH) of each cluster. Multipath firing is then performed using a moderated puffer-fish optimization (MPO) algorithm that finds the closest optimal and shortest node to transmit optimal drug data. The work is simulated using an NS-3 environment, and the results are obtained. The outcome of this work is analyzed with existing methodologies, and the results prove that the present work consistently outperforms the existing methodologies.

The rates of chemical reactions involved in cell-to-cell communication can serve as a powerful tool for advanced theranostics and in establishing a molecular communication link between bio-transceivers. Reaction rates are usually experimentally measured by quantifying chemical products, which is challenging when several signal transduction mechanisms are involved in the signaling pathway. Without loss of generality, we focus on extracellular vesicle (EV) cell-to-cell signaling and propose a computational method to estimate the chemical reaction rates which characterize a process by which EVs are taken by cells. The method is based on measuring only the time-course of environmental EVs, and eliminates the need to measure either bound or internalized EVs which is usually essential for experimental evaluation of the rates by using advanced molecular imaging modalities. As an alternative to a proposed approximation by a linear system model, our computation exploits a nonlinear system model in which the impact of limited receptor sites on the recipient cell membrane is incorporated. The reaction rates are obtained through a suggested linear and iterative approach as well as a novel way of applying Michaelis–Menten kinetics in the frequency domain. The range of validity of each technique is evaluated by varying the number of free binding sites on the cell membrane in relation to the initial number of environmental EVs. In conclusion, the proposed methods are very effective in assessing the dynamics of the EV uptake using a simple in vitro platform.

Molecular communication (MC) is a novel paradigm for nano-communication networks. Compared with diffusion-based single-input single-out (SISO) systems, multiple-input multiple-output (MIMO) MC with drift systems can effectively mitigate the negative effects of inter symbol interference (ISI), inter link interference (ILI) and noise, further improving transmission efficiency. The modeling complexity of MIMO MC systems inspires the application of deep learning (DL) techniques to establish end-to-end architectures for signal recovery. However, training of the entire end-to-end system is limited by the unknown channel and small training sample size. In this paper, aiming at signal recovery of the newly developed mathematical MIMO MC with drift system model, a Kullback–Leibler divergence (KLD) evolutionary generative adversarial network (EGAN)-based end-to-end learning method is proposed. The end-to-end architecture can be trained offline with both the sampled and fake signals generated by KLD EGAN, even with a small training sample size, and then used to recover online transmitted signals directly. Besides, two traditional detection algorithms denoted as the maximum a posterior (MAP) detector and fixed threshold (FT) detector, are proposed as well for theoretical comparison. Experiments of the effect of different model parameters on the system performance have been carried out. Results validate the effectiveness and robustness of our proposed method compared to other DL-based methods, including the deep neural networks (DNN)-based, the original GAN-based, and the original EGAN-based, in terms of transmission accuracy.

Clique problem has a wide range of applications due to its pattern matching ability. There are various formulations of clique problem like $k$-clique problem, maximum clique problem, etc. The $k$-Clique problem determines whether an arbitrary network has a clique or not whereas maximum clique problem finds the largest clique in a graph. It is already exhibited in the literature that the $k$-clique or maximum clique problem (NP-problem) can be solved asymptotically faster by using quantum algorithms compared to conventional computing. Quantum computing with higher dimensions is gaining popularity due to its large storage capacity and computation power. In this article, we have shown an improved quantum circuit implementation for the $k$-clique problem and maximum clique problem (MCP) with the help of higher-dimensional intermediate temporary qudits for the first time to the best of our knowledge. The cost of the state-of-the-art quantum circuit for the $k$-clique problem is colossal due to a huge number of $n$-qubit Toffoli gates. We have exhibited an improved cost and depth over the circuit by applying a generalized $n$-qubit Toffoli gate decomposition with intermediate ququarts (4-dimensional qudits).

This paper proposes a targeted drug delivery system based on mobile molecular communication (MMC). The system consists of a mobile transmitter and a mobile reactive receiver. The transmitter can sense the required drug concentration and send commands to the receiver for drug delivery in the extracellular fluid (ECF). The commands are sent in the form of bits and the received signal is prone to noise and inter-symbol interference (ISI). Hence, at the receiver, two detection techniques, differential amplitude detector (DAD) and differential energy detector (DED) with ISI mitigation are proposed for MMC. Manchester-coded bits are transmitted using modified concentration shift keying (MCSK). In the proposed detection mechanism, an adaptive threshold technique is used for estimating the number of signaling molecules using the maximum a posteriori probability (MAP) rule. Further in each bit interval, dynamic distance estimation, signal reconstruction, and ISI mitigation are performed. Particle-based simulation for reactive receiver is also carried out to validate the results. A low bit error rate (BER) in the MMC system signifies the promising performance of the drug delivery system.