Nano Communication Networks最新文献

Terahertz (THz) band is an important range in photonics, and provides numerous advantages in various applications. One of the most popular detection methods for THz pulses is electro-optic sampling (EOS). EOS provides many benefits; however, in this method distortion damages the output signal, and limits the bandwidth of this technique. In this article, a calculation-based approach is proposed to remove the effect of distortion in EOS detection process. This manner is based on definition of a base-level spectrum which modifies the output of EOS to eliminate unwanted disorders. The introduced method is computational, inexpensive, feasible, fast, adaptable, and effective. Moreover, a detailed comprehensive step-by-step model of THz time-domain spectroscopy (THz-TDS) with a simple and obvious perspective for ZnTe and GaP crystals is provided.

In nano communication, fault-tolerant networks play a crucial role in error control. A significant practical challenge for nanocircuits is their ability to transmit information over networks to different endpoints. Fault-tolerant and reversible circuits have control error problems. The advantage of a quantum gate-based architecture is that it prevents heat loss, and it has been extensively researched. In this article, we have developed reversible multiplexers (mux's), half-adder (HA), and full-adder (FA) and latches that are fault-tolerant by making use of new gate and implementing them on the IBM Qiskit platform. A power-efficient and fault-tolerant mux's and latches is proposed that uses reversible gates to preserve parity. Multiplexer kinds such as 2:1, 4:1, and n:1 is covered in depth by the new Parity Preserving Multiplexer (PPM) gate and verified by IBM-Qiskit. An algorithmic design for an n:1 multiplexer is invented. In order to assess a PPM gate effectiveness, 13 standard Boolean functions and 8 standard types of gates are implemented. The PPM quantum gate is built using quantum assembly code (QAC), which runs on IBM Quantum Lab and IBM Quantum Composer platforms to measure the output qubits. Additional HA, muxes, and latches design led to the code creation in the Qiskit platform, which was used to measure the output qubits. A comparison of the D-latch, T-latch, JK-latch, and mux designs with existing circuits shows a reduction in quantum cost (qc) and junk output (go) and the implementation of a custom design in the IBM-Qiskit platform to measure output qubits is a first time in literature.

A nanonetwork is a multi-hop network composed of tiny communicating components, whose energy budget is rather limited. A method to reduce the energy used by nodes is to reduce the number of packets transmitted. In this article, we propose a dynamic 3D scheme to reduce the number of forwarders during routing. In this scheme, potential forwarders are found on a ring around transmitter nodes. We analyze the effectiveness of our 3D scheme for four routing protocols in multi-source scenarios. We analyze its memory cost, both theoretically and by simulation. Results show that the proposed scheme works in 3D, and reduces the number of forwarders while maintaining almost the same packet delivery ratio.

In this paper, a multiband THz Multi-Input Multi-Output (MIMO) antenna is designed with dimensions of 80×100×10.8μm³. The antenna is made-up on a gold-plated Arlon AD410 substrate with a relative permittivity of 4.1.It operates at three resonant frequencies, namely 1.45THz, 2.25THz, and 3.25THz, achieved through the integration of Complementary Split-Ring Resonator (CSRR) and Substrate Integrated Waveguide (SIW) technologies. The two-element MIMO configuration of the antenna ensures exceptional performance, offering high throughput with data rates of 25.23Gbps for the Quadrature Phase Shift Keying (QPSK) scheme and 56.68Gbps for the 16-Quadrature Amplitude Modulation (QAM) scheme. It also exhibits remarkable channel capacity, approximately 8.2bps/Hz at Signal-to-Noise Ratio (SNR) = 20dB, surpassing the capabilities of single-element antennas. Moreover, it demonstrates excellent diversity performance for judging the MIMO antenna performance. This is evident through the following key metrics: Envelope Correlation Coefficient (ECC) < 0.02, indicating that less than 1 % of power is transferred from the excited antenna to the second 50Ω terminated antenna when antenna-1 is excited; Directive Gain (DG) >9.95dB; Total Active Reflection Coefficient (TARC) < -10dB, ensuring that a minimum of 90 % of the power is delivered to the patch port; and Channel Capacity Loss (CCL) < 0.35 bits/sec/Hz, guaranteeing reliable wireless communication. The antenna boasts peak gains of 3.83dBi, 4.06dBi, and 6.82dBi at 1.45THz, 2.25THz, and 3.25THz, respectively, along with a radiation efficiency of approximately 37, 58, and 51 % at the corresponding frequencies. Notably, the first two bands (1.34-1.51THz and 2.20-2.28THz) exhibit narrow bandwidths with quality factors above 80, making them particularly suitable for sensing applications in biomedical. Band-1 offers an average sensitivity of 3222.22 GHz/RIU and an FOM of 17.89, while Band-2 provides an average sensitivity of 2578.68 GHz/RIU and an FOM of 14.38. These characteristics make it well-suited for near-field Nano-communications and sensing applications.

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.