Nano Communication Networks最新文献

Intra-body flow-guided nanonetworks are nanonetworks that aim to deploy the Internet of Nanothings (IoNT) into the human cardiovascular system. In these nanonetworks, nano-nodes flow in blood vessels (including arteries and veins) for detecting sensitive biological/physical data. Nano-nodes dispatch data to each other and outside devices via Terahertz (THz) waves. Monitoring of different biomarkers, detection of infectious agents, localization of cancer cells, accurate drug delivery, and other medical applications are all potential applications utilizing such networks. However, the physical limitations of the nano-nodes and the high attenuation of terahertz waves in the blood limit data transmission. Therefore, based on the characteristic of laminar blood flow in blood vessels, we proposed a central high-speed lane routing protocol in Yao et al. (2023), which utilized high-speed nano-nodes in the central layer of the blood flow to form a directional relay chain for other non-centric nano-nodes. In this paper, the proposed protocol is studied in depth, described in detail, and evaluated in the parameters of the hand vein scenario. The proposed protocol works well in new scenarios and proves its efficiency in intra-body communication.

In recent times, the Multiple-Input Multiple-Output (MIMO) system has played a vital role in wireless communication. MIMO antenna uses multiple antennas on both the sides of the transmitter and receiver. Mutual coupling occurs between two antenna elements to reduce the performance. The passage of current in the same direction on both neighbouring sides of the antennas increases the rate of mutual coupling. To decrease the mutual coupling, Split Ring Resonators (SRR) are located on the MIMO antenna. In this research paper, a MIMO antenna with SRR for Ultra Wide Band (UWB) applications with different frequency notches is proposed. Also, bandwidth and isolation are considered essential metrics for MIMO antennas. In the proposed 4 × 4 MIMO antenna design, SRR is located at the antenna's two sides, and the defected ground structure is on the bottom portion with a permittivity of 4.4 (FR4 substrate). The defected ground structure is proposed with stubs to enhance the characteristics of bandwidth and create notch bands in the frequency range of 3.1 GHz to 10.6 GHz. Placing SRRs adjacent to the feed line enhances the gain performance. The experimental results show that the ECC of the proposed antenna is less than 0.25, DG is higher than 9.8 dB, and the peak realized gain for 9.38 GHz is 11.3 dB, which is more reliable than other antenna designs. Therefore, experimental outcomes of the proposed antenna design enhance the applicability of band notching for wideband communication.

This manuscript presents a novel model for generating synthetic data for a biological molecular communication (MC) system to train a Neural Network (NN) for the purpose of discriminating transmitted bits. To achieve this, a deep learning algorithm was trained using the synthetic data and tested against experimentally measured data. The polynomial curve fitting coefficients are chosen as features. The featurization stage is followed by a NN that captures different aspects of the temporal correlation of the received signals. The real data was collected from an MC testbed that employed transfected Escherichia coli (E. coli) bacteria expressing the light-driven proton pump gloeorhodopsin from Gloeobacter violaceus. By stimulating the bacteria with externally controlled light, protons were secreted, which changed the pH level of the environment. A pH detector was then used to measure the pH of the environment. We propose the use of a deep convolutional neural network to detect the transmitted bits. This paper discusses the data augmentation, processing, and NNs that are pertinent to practical MC problems. The trained algorithm demonstrated an accuracy of over 99.9% in detecting transmitted bits from received signals at a bit rate of 1 bit/min, without requiring any specific knowledge of the underlying channel.

Metal oxide semiconductor (MOS) technology has reached its maximum profitable limits due to increase in leakage current, static power dissipation, limited switching frequency. One of the better solutions to overcome these problems is the quantum-dot cellular automata (QCA) technology, it boasts the absence of physical transportation charges, relying solely on Coulombic force for interaction between the cells also it is a transistor less technology does not require any external voltage bias. In the current integrated circuits, the data being transferred more than ever, the error correction and coding techniques find significance in reliable data communication. Recognizing the increasing importance of error correction in data communication, particularly with the widespread data transfer, this research specifically focuses on the implementation of an enhanced convolutional encoder using QCA for error correction in data transmission. Comparative study with the state-of-art is also carried out to examine performance of proposed design. As a result of our study, we were able to reduce the cell count by 33.34% and power dissipation is reduced by 77% with the proposed 1/2 rate encoder and the proposed 1/3 rate encoder has 15.9% less cell count and power dissipation is reduced by 72% as compared to existing design.

As an emerging nanotechnology, Quantum-dot Cellular Automata (QCA) has attracted extensive attentions due to its characteristics of high density, high speed, and low energy consumption. Modern high-performance System-on-Chips (SoCs) with multiple processors require interconnection networks to connect each core for improving data throughput and reducing latency, while the crossbar network is broadly used as a non-blocking interconnection architecture with high efficiency. In this paper, a method to design an efficient QCA-based N × N crossbar network utilizing optimally designed N:1 multiplexers (MUXs) is proposed, followed by a multi-layer and a single-layer implementation of its 8 × 8 design. A simplified matrix model is then introduced to provide a concise and intuitive switch control strategy, and expressions for cell count, area, latency, QCA cost, and QCA complexity of the proposed crossbar networks are derived according to the size N. Experimental results manifest that the proposed 8 × 8 crossbar networks have significant advantages on most performance parameters compared with other existing QCA-based networks.

The widespread use of graphene patch antennas is escalating as evidence of their applicability in areas like 6 G communications and THz spectroscopy. Geometric uncertainty and fabrication issues while downsizing makes terahertz antenna design problematic. Graphene's electromagnetic and mechanical qualities make it ideal for miniaturizing antennas for Terahertz use. In the THz spectrum, a graphene antenna requires careful dielectric material selection since performance fall, especially efficiency. This paper compares dual band multi-layered genetic algorithm-based optimized antennas for the THz applications, especially spectroscopy and 6 G utilizing sole layer duple substrates concept, i.e., two distinct substrates at the same level between the ground and patch. Different antennas are designed using various substrates like Rogers RO3010, RO3210, RT5880, RT5880LZ, TMM 13i, Taconic TLY-3, RF-10, Silicon, & Teflon. Two segments of four antennas are planned; one has silicon as a common substrate with four additional materials, and another has Teflon. The proposed antenna's performance is assessed in terms of bandwidth, beamwidth, directivity, efficiency, gain, radiation pattern, return loss, and VSWR. The results reveal that Silicon and Rogers RT5880 LZ substrates-based antenna perform better in a segment I, with bandwidth (GHz) of 150.1 and 156.9, directivity (dBi) of 5.93 and 4.23, efficiency (%) of 76.65 and 78.98, and gain (dB) of 4.97 and 3.3. While in segment II, Teflon and Taconic RF-10-based antenna perform better with features 158 and 198 bandwidth, 6.43 and 4.43 directivity, 74 and 83 efficiency, and 4.67 and 3.65 gain.

This paper presents a significant contribution to the field of nanoscale computing by proposing an innovative reversible Arithmetic and Logic Unit (ALU) implemented in Quantum-Dot Cellular Automata (QCA). Reversible logic and QCA technology offer promising alternatives to conventional CMOS technology, addressing the challenges of operating at nanoscale dimensions. The primary objective is to develop a highly efficient ALU capable of performing 26 distinct arithmetic and logical operations. The ALU design is based on a novel reversible full adder-subtractor optimized for minimal quantum cost, which is crucial for energy-efficient quantum computation. The evaluation encompasses various criteria related to reversibility, such as gate count, number of constant inputs, number of garbage outputs, and quantum cost. QCA-specific criteria, including cell count, occupied area, and clock cycles, are also considered. The outcomes of this research contribute to the advancement of cell-efficient nanoscale computing, with implications for quantum computation, emerging technologies, and future integrated circuit design.

Nanodevices are the focus of research enhancing the detection and treatment of diseases in the human body. Focusing on the scenario where nanosensors are flowing with the blood in the human circulatory system (HCS), in this work, we investigate a model to predict their distribution along the various vessel segments. Although various approaches report solutions for localizing nanosensors in the body, it is also relevant to derive their stationary distribution along the vessel segments as a prior step to assess their actuation and sensing capabilities in the body. We use a Markov chain formulation to derive the stationary distribution of nanosensors. We evaluate the transition probabilities relying on the representation of vessels with electric circuit components. We implement the electric circuit representation of the left ventricle in the heart and the arteries to find the blood flow at vessel bifurcations and then compute the Markov chain probabilities. Our system also allows to reveal the dynamics of the movement of nanosensors with the human activity. We illustrate results in two regimes, as low and high activity, to mimic the case when being at rest or doing sports.

In the evolving landscape of terahertz communication, the behavior of channels within indoor environments, particularly through glass doors, has garnered significant attention. This paper comprehensively investigates terahertz channel performance under such conditions, employing a measurement setup operational between 113 and 170 GHz. Analyzing scenarios frequently induced by human activity and environmental factors, like door movements, we established a comprehensive theoretical model. This model seamlessly integrates transmission, reflection, absorption, and diffraction mechanisms, leveraging the Fresnel formula, multi-layer transmission paradigm, and knife-edge diffraction theory. Our experimental results and theoretical predictions harmoniously align, revealing intricate dependencies, such as increased power loss at higher frequencies and larger incident angles. Furthermore, door interactions, whether opening or oscillations, significantly impact the terahertz channel. Notably, door edges lead to a power blockage surpassing the transmission loss of the glass itself but remaining inferior to metallic handle interferences. This paper's insights are pivotal for the design and fabrication of terahertz communication systems within indoor settings, pushing the boundaries of efficient and reliable communication.

The application of DNA monitoring has recently expanded into the information technology realm of DNA computing and storage systems, leveraging its capabilities for molecular computing and high-density data storage. Essential to this advancement are multi-color fluorescent signals attached to informational DNA, enabling simultaneous multidata reading in the DNA memory or storage system. Various fluorescent detection techniques, such as real-time polymerase chain reaction, next-generation sequencing, and fluorescence resonance energy transfer, have been investigated for DNA reactions and sequence data. Although proximity (less than 10 nm) between different fluorescent signals can lead to interference, controlling the distance of fluorescent molecules attached to DNA is a feasible solution. This study demonstrates a DNA molecular memory system using multiple fluorescent molecules. We examined the independent hybridization of three different fluorescent DNA molecules to DNA templates with three sites for fluorescent attachment on 17 nt DNAs. The study focused on two multi-bit DNA molecules hybridized to the template DNA, assessing their fluorescence emission intensities at various excitation wavelengths. Two multi-bit DNA molecules, which were hybridized onto the template DNA, were investigated for fluorescence emission intensities by various excitation wavelengths. Although the emission intensities of the two multi-bit DNA molecules were not significantly increased by another fluorescent molecule, each excitation wavelength has provided more effective emission intensity levels for DNA signal detection. Furthermore, we developed a three-bit DNA molecular memory system using triple-level DNA molecules. These multi-color DNA systems could be extended to arithmetic and logical computing.