Nano Communication Networks最新文献

MIMO systems in molecular communication are proposed and exhaustively investigated to increase the data rate. However, the misalignment problem between antennas decreases the performance of the system and has not been investigated adequately. This paper aims to correct the received signal in misaligned and distance-varying molecular 4x4 MIMO systems. By using multiple transmitter antennas and receivers, we can increase the data rate by utilizing higher-order modulation techniques that exploit spatial diversity. For the spatial modulation techniques, if there is a misalignment among the corresponding antennas, the signal quality degrades and decoding errors occur more frequently. Therefore, our main goal is to decode the received signal, while considering and compensating the misalignment of the system. To do so, we first estimate the misalignment along the rotation axis of our system and the distance between transmitter antennas and receivers. Then using these values, we process the received signal to eliminate the effects of misalignment. Additionally, we propose and investigate a method to detect active transmitter antennas that can be utilized for molecular index modulation under misalignment. We compare the performance of our proposed methods with the existing misalignment correction techniques.

Diffusion-based molecular communication (MC) system present immense potential and broad application prospects in the field of biomedicine, such as drug delivery. Molecular multiple-input multiple-output (MIMO) communication system can improve the reliability of communication in the environment. However, the channel modeling for diffusion-based molecular MIMO communication system is challenging. Most of the work on the modeling of molecular MIMO channels focuses on the traditional derivation of the channel impulse response (CIR). In this paper, we take into account an M × N molecular MIMO communication system with symmetric and asymmetric topologies. A deep neural networks (DNN) based model and Transformer-based model are proposed to predict the channel parameters in the CIR of this molecular MIMO system under different molecule types (DMT) and same molecule types (SMT), respectively. Simulation results show that the DNN-based model has best accuracy of prediction than the Transformer-based model and Long Short-Term Memory (LSTM) model under DMT. In particular, the Transformer-based model outperforms the DNN-based model and LSTM model under SMT.

The analysis is generally conducted in stationary receiver and transmitter models in a diffusion environment for the fundamental Molecular communication (MOC) models. However, a mobile MOC model is employed in this study, deviating from the existing literature. This mobile MOC model considers the mobility of all variables in the diffusion environment, including the transmitter, receiver, and molecules. Firstly, a novel MOC model is proposed, departing from the conventional normal distribution for the mobility of variables. Instead, alternative distribution functions such as the Pareto distribution, extreme value distribution, t-distribution, and generalized extreme value distribution are employed. Furthermore, the system's performance is enhanced by optimizing the distribution function and model parameters, such as the diffusion coefficient, using the optimization of optimization (OtoO) approach. In this approach, the Multi-Verse Optimization (MVO) algorithm serves as the primary algorithm, while the Grey Wolf Optimization (GWO) algorithm functions as the auxiliary algorithm. Essentially, the MVO algorithm optimizes the parameters of the MOC model, while simultaneously, the GWO algorithm optimizes the impact of the optimization processes of MVO on the parameters ``p'' and ``N'' as well as the constant parameter of the distribution function. By optimizing both the parameters of the MOC model and the distribution function, the number of received molecules is significantly increased. Therefore, this study not only improves the results of the MOC model structure based on different distribution functions but also optimizes all parameters of the proposed model using the MVO-GWO OtoO approach.

This paper introduces a novel ultra-broadband Metamaterial Absorber (UBMA) demonstrating significant absorption capabilities across a wide terahertz frequency range from 2.42 THz to 6.11 THz. The 3.7 THz bandwidth represents 87% of the central frequency. The proposed UBMA comprises three layers: a star-shaped metal patch on top, a dielectric substrate in the middle, and a metallic ground plane below. Simulations using CST Microwave Studio software reveal that the design achieves high absorption at five distinct frequencies: 2.47, 3.45, 4.89, 6.01, and 6.87 THz, with absorption rates of 99% for the first four peaks and 90% for the fifth peak. The study of electric field and surface current distribution provides insights into the absorption mechanism. While the UBMA exhibits polarization-independent performance, its angular response shows some sensitivity to the incident angle, especially beyond 30° Despite this, the absorber maintains over 70% absorptivity up to a 45° incidence angle for both TE and TM polarizations within specific frequency ranges. The simple structure combined with high absorption efficiency makes the UBMA suitable for THz imaging, detection, and stealth applications, although its angular sensitivity must be considered for certain applications.

The dielectric properties of environmental surfaces, including walls, floors and the ground, etc., play a crucial role in shaping the accuracy of terahertz (THz) channel modeling, thereby directly impacting the effectiveness of communication systems. Traditionally, acquiring these properties has relied on methods such as terahertz time-domain spectroscopy (THz-TDS) or vector network analyzers (VNA), demanding rigorous sample preparation and entailing a significant expenditure of time. However, such measurements are not always feasible, particularly in novel and uncharacterized scenarios. In this work, we propose a new approach for channel modeling that leverages the inherent sensing capabilities of THz channels, specifically by obtaining channel measurement data through the analysis of refractive indices. By comparing the results obtained through channel sensing with that derived from THz-TDS measurements, we demonstrate the its ability to yield dependable surface property information. Integrating it into a ray-tracing algorithm for channel modeling in both a miniaturized cityscape scenario and an indoor environment, the results show consistency with experimental measurements, thereby validating its effectiveness in real-world settings.