Search Results (563)

This work addresses the tailoring spectral response of grating-assisted co-directional couplers (GADCs) in the context of wavelength filtering for fiber-to-the-home (FTTH) applications. Design methods for spectral response engineering by means of coupling profile apodization-type weighting techniques and also more advanced rational transfer functions fitting a predefined spectral window template are presented. Modeling results based on coupled mode theory are then applied for the design and experimental fabrication of InGaAsP/InP GADCs targeting 1.3+/1.3− µm diplexer application in FTTH access networks. The experimental results are found to be in good agreement with the modeling predictions. The design tools presented are quite general and can be easily adapted to other technology platforms, such as silicon photonics for the use of GADCs as add-drop wavelength division multiplexers. The field of parity–time symmetry is another avenue where these types of gain–loss-assisted GADCs as active components are of interest for switching applications, and the design methods presented here may find utility. Full article

Background: Chronic venous disease (CVD) comprises a set of vascular disorders that affect the venous system with important local and systemic repercussions. A growing body of evidence displays the relationship between suffering from CVD and a marked deregulation of the immune inflammatory system. In this sense, the previous literature has reported some significant changes in the level of various circulating inflammatory parameters in these patients. However, more research is required to detail and deepen this complex relationship. Methods: In this work, we studied, using a multiplex technique, the levels of circulating cytokines and chemokines detectable in the serum of 40 patients with CVD and compared it with 38 healthy controls (HCs). In parallel, we performed Spearman’s correlation analysis to explore potential inflammatory networks in CVD. Results: In this study, we measured circulating cytokines and chemokines in CVD patients using a multiplex assay. Results showed increased levels of several pro-inflammatory mediators (IL-1β, IL-2, IL-5, IL-6, IL-7, IL-8, IL-12, IL-17A, IL-23, TNF-α, IFN-γ, fractalkine, ITAC, and GM-CSF) and a decrease in IL-13, with no significant changes in IL-4, IL-10, IL-21, MIP-1α, MIP-1β, or MIP-3α. The Spearman correlation analysis revealed strong, positive correlations among several inflammatory mediators in HC, particularly between TNF-alpha, IL-1β, IL-17A, and IL-23, forming a highly interconnected cytokine network. In contrast, CVD patients showed fewer, weaker, and distinct correlations, with new associations such as IFN-γ with IL-1β and IL-23, suggesting a disrupted inflammatory profile. Conclusions: The distinct inflammatory profile in CVD patients, characterized by altered cytokine and chemokine levels and a less coordinated cytokine network, underscores the reconfiguration of inflammatory pathways in this condition. These findings highlight potential therapeutic targets aimed at restoring immune balance and mitigating chronic inflammation in CVD. Full article

Integrated Sensing and Communication (ISAC) systems have emerged as a key component for Sixth Generation (6G) networks, enhancing resource efficiency and enabling diverse applications. Currently, ISAC systems have been recognized as a leading trend for future standardization, i.e., International Mobile Telecommunications (IMT)-2030. As in the previous IMT-2020 standardization, the emphasis has been on developing a methodology for assessing network conditions, with one of the crucial approaches incorporating system-level simulations. However, within this framework, there has been a notable absence of proposed abstractions for the physical layer of ISAC systems, which are valuable for system-level simulators. The physical abstraction process helps reduce computational simulation costs, enabling efficient and rapid evaluation of system conditions. Therefore, this paper aims to fill this gap by outlining the key aspects and metrics recommended for a physical layer abstraction in sensing applications within ISAC frameworks. Applying physical abstraction in the context of target localization and detection algorithms may enable an initial understanding and evaluation of ISAC system performance. These algorithms are proposed as an example of simulating the sensing functionalities to be abstracted, which are based on a stochastic geometric channel model. Orthogonal Frequency Division Multiplexing (OFDM) symbols play a crucial role in target position estimation. The findings show that doubling OFDM symbols improves the detection probability by 3 dB in terms of Signal to Noise Ratio (SNR). Finally, the proposed Physical Layer Abstraction (PLA) method produces performance metrics as figures and lookup tables tailored for system-level simulators. Full article

In this paper, we propose a random frequency division multiplexing (RFDM) method for multicarrier modulation in mobile time-varying channels. Inspired by compressed sensing (CS) technology which use a sensing matrix (with far fewer rows than columns) to sample and compress the original sparse signal simultaneously, while there are many reconstruction algorithms that can recover the original high-dimensional signal from a small number of measurements at the receiver. The approach choose the classic sensing matrix of CS–Gaussian random matrix to compress the signal. However, the signal is not sparse which makes the reconstruction algorithms ineffective. We take full account of the great power of deep neural networks (DNN) to detect the signal as it is an underdetermined equation. The proposed RFDM establishes a novel signal modulation and detection method to target better transmission efficiency, and the simulation results show that the proposed method can achieve good BER, offering a new research paradigm to improve the spectrum efficiency of a multi-subcarrier, multi-antenna, multi-user system. Full article

The e-commerce data structure is a typical multiplex graph network structure, which allows multiple types of edges between node pairs. However, existing methods that rely on message-passing frameworks are not sufficient to fully exploit the rich information in multiplex graphs. To improve the performance of link prediction, we propose a novel laminated heterogeneous graph convolutional network (LHGCN) consisting of three core modules: a laminate generation module (LGM), an adaptive convolution module (ACM), and a laminate fusion module (LFM). More specifically, the LGM generates symmetric laminates that cover diverse semantics to create rich node representations. Then, the ACM dynamically adjusts the node receptive field and flexibly captures local information, thereby enhancing the representation ability of the node. Through symmetric information propagation across laminates, the LFM combines multiple laminated features to optimize the global representation, which enables our model to accurately predict links. Moreover, an elaborate loss function, consisting of positive sample loss, negative sample loss, and L2 regularization loss, drives the network to preserve critical information. Extensive experiments on various benchmarks demonstrate the superiority of our method over state-of-the-art alternatives in terms of link prediction. Full article

The rapid increase in Internet usage has led to a growing demand for bandwidth. Optical microring resonators (MRRs) are emerging as a promising solution to meet this need. MRRs generate optical frequency combs (OFCs) that provide multiple wavelengths with high phase coherence, enabling communication via wavelength division multiplexing (WDM). Spectrum allocation methods, such as the Routing, Modulation Level, and Spectrum Assignment (RMLSA) approach, play a crucial role in executing this strategy efficiently. While current algorithms have improved allocation efficiency, further development is necessary to optimize network performance. This paper presents an integer linear programming (ILP)-based method for network resource allocation, aiming to maximize the number request and the bandwidth assigned to each. The proposed approach offers a flexible cost function that prioritizes system constraints such as transmission distance and bandwidth requirements, resulting in significant improvements to the bandwidth blocking rate (BBR). By integrating multilevel modulation and using wavelengths generated by MRRs, this method efficiently handles up to 1075 requests, achieving a BBR of zero. This dynamic and adaptable allocation strategy ensures optimal resource utilization, enhancing overall network performance. Full article

In this paper, we propose an underlay cognitive radio (CR) system that includes subscribers, termed secondary users (SUs), which are designed to coexist with the spectrum owners, termed primary users (PUs). The suggested network includes the PUs system and the SUs system. The coexistence between them is achieved by using a novel dynamic spectrum co-access multicarrier-based cognitive radio (DSCA-MC-CR) technique. The proposal uses a quadrature phase shift keying (QPSK) modulation technique within the orthogonal frequency-division multiplexing (OFDM) scheme that maximizes the system data rate and prevents data inter-symbol interference (ISI). The proposed CR transmitter station (TX) and the CR receiver node (RX) can use an advanced smart antenna system, i.e., a multiple-input and multiple-output (MIMO) system that provides high immunity against channel impairments and provides a high data rate through its different combining techniques. The proposed CR system is applicable to coexist within different existing communication applications like fifth-generation (5G) applications, emergence applications like the Internet of Things (IoT), narrow-band (NB) applications, and wide-band (WB) applications. The coexistence between the PUs system and the SUs system is based on using power donation from the SUs system to improve the quality of the PU signal-to-interference-and-noise ratios (SINRs). The green communication concept achieved in this proposal is compared with similar DSCA proposals from the literature. The simulations of the proposed technique show enhancement in the PUs system throughput and data rate along with the better performance of the SUs system. Full article

In the current context of global communications, HF (High Frequency) NVIS (Near Vertical Incidence Skywave) data networks can be of strategic importance, providing short- and medium-range communication capabilities independent of terrestrial configuration and existing conventional communications infrastructure. They are essential in critical conditions, such as natural disasters or conflicts, when terrestrial networks are unavailable. This paper investigates the development of such systems for HF NVIS data communications by introducing a customized Orthogonal Frequency Division Multiplexing (OFDM) protocol with parameters adapted to HF ionospheric propagation, implemented on Software-Defined Radio (SDR) systems, which provide extensive configurability and high adaptability to varying HF channel conditions. This work presents an innovative approach to the application of OFDM narrow-channel aggregation in the HF spectrum, a technique that significantly enhances system performance. The aggregation enables a more efficient utilization of the available spectrum and an increase in the data transmission rate, which represents a substantial advancement in NVIS communications. The implementation was realized using an SDR system, which allows flexible integration of the new OFDM protocol and dynamic adaptation of resources. The work also includes the development of a messaging application capable of using this enhanced HF communication system, taking advantage of the new features of channel aggregation and SDR flexibility. This application demonstrates the applicability of the protocol in real-world scenarios and provides a robust platform for data transmission under conditions of limited access to other means of communication. Thus, this study contributes to the technological advancement of NVIS communications and opens new research and deployment directions in HF communications. Full article

Terahertz (THz) communication is a crucial technique in sixth generation (6G) mobile networks, which allow for ultra-wide bandwidths to enable ultra-high data rate wireless communication. However, the current subcarrier spacing and the size of fast Fourier transform (FFT) of the orthogonal frequency division multiplexing (OFDM) in 5G NR are insufficient regarding the bandwidth requirements of terahertz scenarios. In this paper, a novel waveform is proposed to address the ultra-wideband issue, namely the generalized filter bank orthogonal frequency division multiplexing (GFB-OFDM) waveform. The main advantages are summarized as follows: (1) The K-point IFFT is implemented by two levels of IFFTs in smaller sizes, i.e, performing M-point IFFT in N times and performing N-point IFFT in M times, where $K=N\times M$. (2) The proposed waveform can accommodate flexible subcarrier spacings and different numbers of the subbands to provide various services in a single GFB-OFDM symbol. (3) Different bandwidths can be supported using a fixed filter since the filtering is performed on each subband. In contrast, the cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) in 4G/5G requires various filters. (4) The existing detection for CP-OFDM can be directly employed as the detector of the proposed waveform. Lastly, the comprehensive simulation results demonstrate that GFB-OFDM outperforms CP-OFDM in terms of the out-of-band leakage, complexity and error performance. Full article

In recent years, the increasing number of internet-connected devices has exceeded the capacity of fourth-generation (4G) cellular networks, leading to the development of fifth-generation (5G) technology, designed to offer higher speeds, greater bandwidth, and lower latency. In this context, this study evaluated Universal Filtered Multi-Carrier (UFMC) and Generalized Frequency Division Multiplexing (GFDM) techniques, implementing them in a radio-over-fiber (RoF) system and a Next-Generation Radio Access Network (NG-RAN) fronthaul link, and compared the results using communication quality metrics such as bit error rate (BER). Additionally, through signal generation and processing in Matlab, the performance of UFMC and LTE signals was analyzed, confirming that simultaneous transmission over an RoF channel allows for efficient signal separation in the frequency domain, with the UFMC giving power to LTE. Full article

Index modulation (IM) is considered a promising approach for fifth-generation wireless systems due to its spectral efficiency and reduced complexity compared to conventional modulation techniques. However, IM faces difficulties in environments with unpredictable channel conditions, particularly in accurately detecting index values and dynamically adjusting index assignments. Deep learning (DL) offers a potential solution by improving detection performance and resilience through the learning of intricate patterns in varying channel conditions. In this paper, we introduce a robust detection method based on a hybrid DL (HDL) model designed specifically for orthogonal frequency-division multiplexing with IM (OFDM-IM) in challenging channel environments. Our proposed HDL detector leverages a one-dimensional convolutional neural network (1D-CNN) for feature extraction, followed by a bidirectional long short-term memory (Bi-LSTM) network to capture temporal dependencies. Before feeding data into the network, the channel matrix and received signals are preprocessed using domain-specific knowledge. We evaluate the bit error rate (BER) performance of the proposed model using different optimizers and equalizers, then compare it with other models. Moreover, we evaluate the throughput and spectral efficiency across varying SNR levels. Simulation results demonstrate that the proposed hybrid detector surpasses traditional and other DL-based detectors in terms of performance, underscoring its effectiveness for OFDM-IM under uncertain channel conditions. Full article

The integration of communication and sensing (ICAS) in optical networks is an inevitable trend in building intelligent, multi-scenario, application-converged communication systems. However, due to the impact of nonlinear effects, co-fiber transmission of sensing signals and communication signals can cause interference to the communication signals, leading to an increased bit error rate (BER). This paper proposes a noncoherent solution based on the alternate polarization chirped return-to-zero frequency shift keying (Apol-CRZ-FSK) modulation format to realize a 4 × 100 Gbps dense wavelength division multiplexing (DWDM) optical network. Simulation results show that compared to traditional modulation formats, such as chirped return-to-zero frequency shift keying (CRZ-FSK) and differential quadrature phase shift keying (DQPSK), this solution demonstrates superior resistance to nonlinear effects, enabling longer transmission distances and better transmission performance. Moreover, to meet the transmission requirements and signal sensing and recognition needs in future optical networks, this study employs the Inception-ResNet-v2 convolutional neural network model to identify three modulation formats. Compared with six deep learning methods including AlexNet, ResNet50, GoogleNet, SqueezeNet, Inception-v4, and Xception, it achieves the highest performance. This research provides a low-cost, low-complexity, and high-performance solution for signal transmission and signal recognition in high-speed optical networks designed for integrated communication and sensing. Full article

Compared with high spectrum efficiency faster-than-Nyquist (FTN) backbone network, an enhanced carrier phase recovery based on dual pilot tones is more sensitive to capital cost in FTN metropolitan areas as well as inter-datacenter optical networks. The use of distributed feedback (DFB) lasers is a way to effectively reduce the cost. However, under high symbol rate FTN systems, equalization-enhanced phase noise (EEPN) induced by a DFB laser with large linewidth will significantly deteriorate the system performance. What is worse, in FTN systems, tight filtering introduces inter-symbol interference so severe that the carrier phase estimation (CPE) algorithm of the FTN systems is more sensitive to EEPN, thus it will lead to a more serious cycle slip problem. In this paper, an enhanced carrier phase recovery based on dual pilot tones is proposed to mitigate EEPN and suppress cycle slip, in which the chromatic dispersion (CD)-aware Tx and LO laser phase noise is estimated, respectively. Offline experiments results under 40 Gbaud polarization multiplexing (PM) 16-quadrature amplitude modulation (QAM) FTN wavelength division multiplexing (FTN-WDM) systems at 0.9 acceleration factor, 5 MHz laser linewidth, and 500 km transmission demonstrate that the proposed algorithm could bring about 0.65 dB improvement of the required SNR for the normalized generalized mutual information of 0.9 compared with the training sequence-based cycle slip suppression carrier phase estimation (TS-CSS) algorithm. Full article

To achieve higher visible light communication (VLC) traffic capacity, using the wide bandwidth light-emitting diode (LED) and spectral efficiency modulation signal, is currently the most commonly used method. In this demonstration, we apply the orthogonal frequency division multiplexing quadrature amplitude modulation (OFDM-QAM) with bit- and power-loading algorithm on single blue LED to achieve >1 Gbit/s VLC capacity, when a 400 MHz bandwidth avalanche photodiode (APD)-based receiver (Rx) is exploited for decoding. Here, the higher sensitivity APD can be applied to compensate for the wireless VLC link length in the proposed LED VLC system, and due to the lower LED illumination (255 to 40 lux), is used for the indoor access network after passing the wireless link length of 1 to 4 m. As a result, using single blue LED can achieve 0.962 to 1.057 Gbit/s OFDM rate with available 400 MHz bandwidth APD in poorly illuminated condition indoors without applying analogy equalization. Full article

Background: In response to inflammation, the absorption of nutritional iron is restricted. Since the pathophysiological significance of the presence and uptake of iron in chronic inflammation is still unknown, we tested the effect of a low iron diet on the clinical course of arthritis in the mouse model of collagen-induced arthritis (CIA). Methods: Six- to eight-week-old male DBA/1 mice were fed either a normal (51 mg/kg) or a low iron diet (5 mg/kg) starting four weeks before the first immunization. From day 4 after the second collagen booster made on day 25, the development of arthritis was regularly monitored until the end of the experiment (day 34), using a standard clinical arthritis score. Concentrations of mouse anti-bovine and anti-mouse collagen type 2 IgG antibodies were measured by ELISA; blood cell counts were performed and mediators of inflammation, tissue matrix degradation, oxygenation and oxidative stress were measured in the mouse sera of both diet groups at the end of the experiment by bead-based multiplex assay. Fe²⁺, Fe³⁺, oxidized and reduced glutathione (GSH and GSSG) and malondialdehyde (MDA) were quantified in whole paw tissue by ELISA. Quantitative PCR was performed in the tissues for glutathione peroxidase 4 and other key regulator genes of iron metabolism and ferroptosis. We used nonparametric tests to compare cross-sectional data. Nonlinear regression models were used for longitudinal data of the arthritis scores. Results: Mice fed a low iron diet showed a significantly less severe course of arthritis compared to mice fed a normal iron diet (p < 0.001). The immune response against bovine and mouse type 2 collagen did not differ between the two diet groups. Mice fed a low iron diet exhibited significantly lower serum levels of tissue inhibitor of metalloproteinase-1 (TIMP-1), a central regulator of inflammation and tissue matrix degradation (p < 0.05). In addition, a low iron diet led to a significant reduction in red blood cell indices, indicating restricted iron uptake and latent iron deficiency, but had no effect on hemoglobin concentrations or red blood cell counts. There were no differences between the dietary groups in Fe²⁺ or Fe³⁺ content in the paws. Based on calculation of the GSH/GSSG ratio and high MDA levels, high oxidative stress and lipid peroxidation were likewise detected in the paws of both diet groups of mice. Consequently, no differences associated with gene expression of key regulators of iron metabolism and ferroptosis could be detected between the paws of both diet groups. Conclusions: Restricted dietary iron intake alleviates immune-mediated inflammation in CIA without causing anemia. This finding suggests a promising option for dietary treatment of arthritis in inflammation. The underlying mechanism causing reduced arthritis may be linked to the complex regulatory network of TIMP-1 and appears to be independent from the local iron levels, oxidative stress and ferroptosis in the synovial tissues. Full article