Optical Fiber Technology最新文献

This work presents a new design framework for highly sensitive fiber gas pressure sensor. We construct a Flexible Fabry–Perot Interferometer (F-FPI), which consists of three polymer pillars and an end cap, on a fiber end face. A unique fiber-end photopolymerization and integration (FEPI) technique is adopted for fabricating the device. The varied gas pressure in the enclosed space not only changes the refractive index (RI) of the gas but also the length of the pillars. Therefore the difference in the optical path length of the FPI is more sensitive to pressure changes. Experimental results indicate that the F-FPI realizes a gas pressure sensitivity up to 79.0 pm/kPa in the range of 0–200 kPa. In addition, the Vernier effect is introduced to amplify the sensitivity to −409.7 pm/kPa. The temperature compensation is achieved by cascading a Fiber Bragg Grating (FBG) behind the F-FPI. Such a design framework provides a new idea for highly sensitivity gas pressure sensing.

Home-working has become a significant trend since COVID-19, enabling the rapid prosper of multicast services supported by 5th/Beyond 5th Generation Mobile Communication (5G/B5G) technologies, e.g., online conference and live streaming. However, these multicast services will transmit over multiple paths, resulting in more energy consumption overhead than unicast service. When multicast services are deployed for baseband functionality in 5G/B5G radio access network (RAN), this energy consumption overhead is particularly evident. To address this issue, multicast service oriented DU-CU deployment, multicast routing, modulation, and spectrum allocation (RMSA) is discussed over elastic optical network (EON) enabled RAN. Notably, although EON provides a fine-grained and on-demand spectrum allocation, it also brings more complex spectral constraints. To this end, a Mixed Integer Linear Programming (MILP) model is presented to minimize total energy consumption for multicast service in EON enabled 5G/B5G RAN. Moreover, a Graph Convolutional Network (GCN) enhanced Deep Reinforcement Learning (DRL) algorithm is proposed to solve the MILP model. Specifically, GCN improves the efficiency of DRL decision-making by fully extracting network features. Simulation results show that our proposed algorithm reduces energy consumption by about 30 % compared with traditional Deep Q-learning Network (DQN) based on Convolutional Neural Network (CNN). The proposed intelligence algorithm reduces the transmission delay by about 20 % compared with traditional CNN-DQN.

In this paper, we propose a novel probability shaping (PS) scheme for PAM-8 signal, making the PAM-8 signal approximately obey the Maxwell Boltzmann distribution. The advantage of this shaping scheme is that it does not require any multiplication or division operations during the process, but some simple bit flips, which greatly reduces the complexity of probability shaping and hardware resource consumption. Based on field programmable gate array (FPGA), we demonstrate PS-PAM8 signals transmission over 50 km standard single-mode fiber (SSMF) with the performance improvement compared to regular PAM-8 signals.

In this paper, we have proposed and experimentally demonstrated a linear array three-core fiber (L-TCF)-based sensor for curvature, torsion, and temperature sensing. The proposed sensor is fabricated by combining the multi-mode fibers (MMFs) and a L-TCF. The MMFs acts as a beam coupler and enhances the Mach-Zehnder interference, while the L-TCF serves as the enhanced sections of another Mach-Zehnder interference. Therefore, the spectrum response of the sensor exhibits a superposition effect of two Mach-Zehnder interferences (MZIs). The sensor with a total length of 12.0 mm forms two dips, and has high curvature, torsion, and temperature sensitivities of 24.187 nm/m⁻¹, 2.554 nm/(rad/m), 0.061 nm/℃, in the range of 0.325–0.726 m⁻¹, 0–3.571 rad/m, 30-100°C, respectively. As described above, the compact structure, simple fabricated method, high curvature and torsion sensitivity and ability to measure temperature, make our sensors can be applied to a variety of different complex environments.

In this paper, we analyze the impact of different node and link architectures (NLA) on the performance of multiband elastic optical networks (MB-EONs). These NLAs are widely used in the literature and comprise three possibilities which basically differ in the presence, or absence, of optical amplifiers along the nodes and in the number of compensated optical fiber sections in the optical links. For each of the three NLAs, we deduced the amplified spontaneous emission (ASE) noise power, generated by the presence of optical amplifiers in the network. Furthermore, six transmission bands (O, E, S, C, L and U) and three different topologies are considered in our analysis. The results are analyzed in terms of calls blocking probability as a function of the input optical power per slot and the ASE noise power distribution for each NLA, considering the longest route among all shortest routes of each topology. We observe that, for one of the most used NLAs in the context of MB-EONs, its performance is inferior, in terms of calls blocking probability, requiring higher input optical power per slot values and producing more ASE noise power compared to the others NLAs.

In this paper, we address the challenges posed by fiber nonlinearity, specifically self-phase modulation (SPM) and cross-phase modulation (XPM), for a polarization-division-multiplexed coherent optical orthogonal frequency division multiplexing (PDM CO-OFDM) system using a novel sparse Volterra method. Though CO-OFDM combined with PDM offers high spectral efficiency and is a promising technology for next-generation optical communication, it is susceptible to fiber nonlinear effects. We introduced an improved sparse Volterra method, employing the orthogonal search approach to simplify the model’s coefficients. This approach helps manage the complexity associated with compensating nonlinear impact for PDM CO-OFDM system. Additionally, a multi-train sequence method is proposed to enhance the compensator’s effectiveness, especially in the context of wavelength-division multiplexing (WDM) systems. The designed compensator is validated through numerous simulations, and a comparative analysis of bit error rate (BER) performance is conducted, considering sparse Volterra compensator, 1-th order Volterra method, and 3-th order Volterra method. The results demonstrated the efficacy of the proposed sparse Volterra model in mitigating nonlinear effects with evidently reduced complexity.

This study explores the intricate details of a high-order concatenation equation with cubic nonlinearity, which is crucial for understanding nonlinear Schrödinger equations. It highlights the importance of concatenation, which involves a sequence of operators vital for analyzing these equations’ behavior. The research illuminates the complex nature of nonlinear Schrödinger equations and their solitonic solutions through comprehensive examination. Investigating various operators such as Hirota, Lakshmanan–Porsezian–Daniel, and quintic, alongside advanced integration methods, including an addition to Kudryashov’s process and the unified Riccati equation approach, the study showcases their utility in real-world applications. It delves into the conditions for various bright, dark, and singular soliton types, assessing their properties. This study stands out because it analyzes a novel architecture within the dispersive concatenation model in birefringent fibers. The research is expected to significantly contribute to the advancement of polarization-maintaining fibers, which are crucial for maintaining the polarization state of light in various specialized applications. The results, enhanced by graphical representations of particular solitons, aim to push forward the field of optical fiber technology, focusing on creating materials, devices, and systems that utilize solitonic phenomena to boost communication and data processing technologies.

A Fabry-Perot (FP) fiber optic sensor utilizing ferrofluid nanomaterials is proposed for magnetic field measurements. A magnetic sensitive adhesive (MSA) is synthesized through the homogeneous blending of ferrofluid and UV glue, serving as a magnetic field-sensing material and adheres to the neatly cut single-mode fiber (SMF) surface to form a FP interferometer. With the increase of the magnetic field, the MSA cavity is subjected to the magnetic force, which causes the elongation of the MSA cavity and induces to the wavelength drift of the reflection spectrum. Experimental results show that the magnetic field sensor exhibits a sensitivity of up to 6.12 nm/mT within the range of 1 to 4 mT, with high linearity and low temperature crosstalk. The sensor’s compact structure, facile manufacturing, and adaptability render it suitable for detecting slit leakage magnetic fields and high-precision magnetic fields.

In intensity modulation direct detection (IM/DD) systems, an adaptively biased layered optical orthogonal frequency division multiplexing (ABLO-OFDM) scheme is proposed based on composite multi-mode index modulation (CMM-IM). The ABLO-OFDM has a simple structure, easy demodulation, low complexity, and effectively reduces the peak-to-average power ratio (PAPR). Since CMM-IM extends the index to the constellation domain, it increases the number of index bits to further improve the spectral efficiency (SE). By dynamically adjusting the number of activated subcarriers and the number of layers, various signal power can be achieved to enhance system flexibility and reliability. The system performance of four schemes is compared and analyzed in IM/DD systems. The result shows that, for the same SE, CMM-ABLO (4,3) achieves a 3 dB receiver sensitivity gain compared with CMM-ABLO (8,2). When L is 3, four schemes can transmit over about 50 km single-mode fiber under the soft decision forward error correction (SD-FEC) threshold. When L is 1 and the sample rate is less than 20 GSa/s, both CMM-ABLO (4,2) and CMM-ABLO (4,3) can achieve error-free transmission. Moreover, as the number of layers increases, PAPR decreases and it reaches a maximum reduction of 9.6 dB.

A noise reduction method based on the combination of improved complete ensemble empirical mode decomposition with adaptive noise and temporal local generalized maximum cross-correlation entropy and wavelet threshold (ICEEMDAN-TLGMCC-WT) is proposed to improve the noise rejection ratio (NRR) of phase-sensitive optical time-domain reflectometer ($\phi$-OTDR) system signals. Firstly, the principle of the proposed ICEEMDAN-TLGMCC-WT denoising method is introduced. On this basis, experiments are carried out to verify the feasibility of the noise reduction algorithm for non-stationary signals. and the results demonstrate that the vibration event can be detected with an improved average NRR of 72.18 dB and a root mean square error (RMSE) 0.017 within 5 km sensing distance by employing the ICEEMDAN-TLGMCC-WT denoising method, which confirm the effectiveness of proposed scheme for the performance improvement of $\phi$-OTDR system.