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Wavemeters are very important for precise and accurate measurements of both pulses and continuous-wave optical sources. Conventional wavemeters employ gratings, prisms, and other wavelength-sensitive devices in their design. Here, we report a simple and low-cost wavemeter based on a section of multimode fiber (MMF). The concept is to correlate the multimodal interference pattern (i.e., speckle patterns or specklegrams) at the end face of an MMF with the wavelength of the input light source. Through a series of experiments, specklegrams from the end face of an MMF as captured by a CCD camera (acting as a low-cost interrogation unit) were analyzed using a convolutional neural network (CNN) model. The developed machine learning specklegram wavemeter (MaSWave) can accurately map specklegrams of wavelengths up to 1 pm resolution when employing a 0.1 m long MMF. Moreover, the CNN was trained with several categories of image datasets (from 10 nm to 1 pm wavelength shifts). In addition, analysis for different step-index and graded-index MMF types was carried out. The work shows how further robustness to the effects of environmental changes (mainly vibrations and temperature changes) can be achieved at the expense of decreased wavelength shift resolution, by employing a shorter length MMF section (e.g., 0.02 m long MMF). In summary, this work demonstrates how a machine learning model can be used for the analysis of specklegrams in the design of a wavemeter.

Fiber-optic interferometric sensors based on graded-index multimode fibers have very high refractive-index sensitivity, as we previously demonstrated. In this paper, spatial-frequency multiplexing of this type of fiber-optic refractive index sensors is investigated. It is estimated that multiplexing of more than 10 such sensors is possible. In the multiplexing scheme, one of the sensors is used to investigate the refractive index and temperature responses. The fast Fourier transform (FFT) of the combined reflective spectra is analyzed. The intensity of the FFT spectra is linearly related with the refractive index and is not sensitive to the temperature.

In this paper, a high-speed transmission system that combines a multimode fiber (MMF) with Free Space Optics (FSO) is proposed to accommodate the rapid growth of traffic. As Orbital Angular Beams Multiplexing (OAM) plays a crucial role in 6G networks by enhancing transmission capacity, four OAM beams ( LG 0 , 0 , LG 0 , 20 , LG 0 , 40 , and LG 0 , 40 ) are utilized in this study. Additionally, the Optical Code Division Multiple Access (OCDMA) technique is employed, known for its high level of confidentiality and the ability to allow multiple channels to transmit data simultaneously. To ensure ubiquitous data transmission, two channels: MMF and FSO are used. Accordingly, in our proposed MMF/FSO system, we employ four OAM beams, with each carrying four OCDMA channels assigned distinct Permutation Vector (PV) codes. With a data transmission rate of 10 Gbps on each OCDMA channel, the overall capacity of the system amounts to 160 Gbps (10 Gbps × 4 OCDMA channels × 4 OAM beams). Moreover, the performance is investigated and evaluated using two scenarios. The first assumes an FSO range of 5 km and Clear Air (CA) weather conditions, with a variable MMF length. In contrast, the second scenario maintains a fixed MMF length of 1 km while altering the FSO span. Weather conditions like CA and rain conditions; Light Rain (LR), Medium Rain (MR), and Heavy Rain (HR) are considered while evaluating the system performance in the second case. The bit error rate (BER), travelling distances in the channel either MMF or FSO, and eye diagrams are among the metrics used for evaluation. They all provide information about the received signal performance. Finally, the obtained results for the proposed MMF/FSO system, utilizing OAM beams and PV codes, indicate that in the first scenario, the system can support a total capacity of 160 Gbps across all channels. The performance is noteworthy, with a BER below 10 - 5 and a wide eye opening, enabling successful transmission over 6.2 km (1.2 km MMF length + fixed 5 km FSO span). For the second scenario, the achievable distance is decreased due to the attenuation caused by rain. It becomes 2.62 km (1 km MMF + 1.62 km FSO span), 5.2 km (1 km MMF + 1.2 km FSO span), and 1.77 km (1 km MMF + 0.77 km FSO span), under LR, MR, and HR, respectively.

A novel mode decomposition method for multimode fiber (MMF) is proposed by using a hybrid network, which combined deep-learning convolutional neural network (DL-CNN) with iterative gradient ascent algorithm (IGAA). DL-CNN is used as the global search for the rough modal amplitudes and relative phases. IGAA is designed as the local optimization to obtain the accurate values of modal decomposition. Although a mini-datasets are employed, the hybrid network shows very good accuracy of modal decomposition and fast convergence. For all of 3-, 5-, 6-, 8-, 10-mode cases, correlation coefficient of the reconstructed and the true near-field intensity patterns can be optimized to higher than 0.98.

Wheel-based network resilience passive optical network (PON) based on mode division multiplexing (MDM) can be integrated with optical code division multiple access (OCDMA) schemes efficiently for the fixed and backhaul traffic under normal and break/failure fiber operating conditions. In this work, a bidirectional 10/2.5 Gbit/s hybrid MDM-OCDMA-PON system using multi-weight zero cross-correlation (MWZCC) code is proposed. Donut modes 0 and 1 are incorporated by the MDM technique in the proposed system. The benefit of this work is to offer an inexpensive, high-bandwidth and advanced long-haul network with satisfactory resource utilization ability for fiber links with protection against faults and to improve the reliability along with survivability of the network. The simulation results show the successful realization of the multimode fiber (MMF) link at 1.6 km in the uplink and 1.2 km in the downlink directions under an acceptable bit error rate (BER). The minimum accepted received power of −31 dBm in uplink and −27 dBm in downlink over 1 km link at 10/2.5 Gbit/s rate is obtained. Moreover, the minimum received power of −20 dBm in uplink and −30 dBm downlink is achieved by using MWZCC code compared to other codes handling 58 simultaneous end users. Further, the influence of fiber impairments and connected devices on the proposed approach is numerically evaluated. Moreover, it is shown that the wheel based proposed approach performs well than other topologies for the bidirectional network resilience transmission.

We demonstrated a fiber in-line Mach-Zehnder interferometer (MZI) coated with platinum (Pt) for the simultaneous measurement of strain and temperature. The sensor was fabricated by splicing a section of multimode fiber (MMF) between two single mode fibers (SMFs) and the Pt coating was prepared by iron sputtering technology. Fine interference fringes of over 20 dB with a compact size of 20 mm were achieved. The experimental results of the two different resonant dips showed strain sensitivities of −2.06 pm/με and −2.21 pm/με, as well as temperature sensitivities of 55.2 pm/°C and 53.4 pm/°C, respectively. Furthermore, it was found that the Pt coating can improve the strain sensitivity significantly, resulting in an increase of about 54.5%. In addition, the sensor has advantages of easy fabrication, low cost, and high sensitivity, showing great potential for the dual-parameter sensing of strain and temperature.

Background: The high-precision analysis of multimode fibers (MMFs) is a critical task in numerous applications, including remote sensing, medical imaging, and environmental monitoring. In this study, we propose a novel deep interpretable network approach to reconstruct spectral images captured using CCD sensors. Methods: Our model leverages a Tiny-YOLO-inspired convolutional neural network architecture, specifically designed for spectral wavelength prediction tasks. A total of 1880 CCD interference images were acquired across a broad near-infrared range from 1527.7 to 1565.3 nm. To ensure precise predictions, we introduce a dynamic factor α and design a dynamic adaptive loss function based on Huber loss and Log-Cosh loss. Results: Experimental evaluation with five-fold cross-validation demonstrates the robustness of the proposed method, achieving an average validation MSE of 0.0149, an R2 score of 0.9994, and a normalized error (μ) of 0.0005 in single MMF wavelength prediction, confirming its strong generalization capability across unseen data. The reconstructed outputs are further visualized as smooth spectral curves, providing interpretable insights into the model’s decision-making process. Conclusions: This study highlights the potential of deep learning-based interpretable networks in reconstructing high-fidelity spectral images from CCD sensors, paving the way for advancements in spectral imaging technology.

Mode division multiplexing (MDM) is an emerging optical communication solution for high-capacity wired–wireless applications. Due to the presence of modal crosstalk and link impairments in MDM, this work aims to design a system that provides low complexity, an improved Shannon Capacity limit, and high spectral efficiency. In this work, a quad modal MDM system using an integrated parabolic index multimode fiber and free-space optics (PIMMF-FSO) link is presented. Four linearly polarized (LP) modes, LP01, LP22, LP03, and LP13 based on a 16 × 10 Gbps MDM system offering different sixteen channels, are realized. Results show that the system can sustain a 7.5 dB insertion loss over 100 m FSO and a 100 m fiber range for different LP modes under the impact of clear air, moderate haze, heavy rain and wet snow climates with weak turbulence. A faithful fiber range of 3000 m can be obtained successfully in the proposed system with a −10 dB link loss, −7.62 dBm received power and 10 dB noise. Compared to existing designs, the proposed design offers optimum performance in terms of high channel capacity and a high traffic rate with low complexity and high spectral efficiency. Additionally, high received power, with acceptable noise, link loss, FSO misalignments and fiber nonlinearities, is successfully obtained.

In spatial division multiplexing (SDM)-based communication systems, each spatial mode can act as an independent information-bearing carrier capable of scaling the total transmission capacity by several orders of magnitude. It has been reported that in SDM networks the signal amplitude depends upon the optical-path-length (OPL) difference between the various optical modes. In this work, we realize SDM technique using a multimode fiber (MMF), because MMFs have a potential to increase transmission capacity drastically by transmitting signals over large number of modes separately. The system performance is analyzed on the basis of following parameters: visualizer spatial profile, mode index profiles, fiber transfer function, refractive index profile, bit error rate, and quality factor. Also we measure changes in the optical path length due to a phase-shifting laser beam. We conclude that MMFs have huge scope for future ultrahigh-capacity transmission systems employing SDM.

In this paper, we present new tap configurations of a feedforward equalizer to mitigate mode coupling in a 60-Gbps 18-channel mode-wavelength division multiplexing system in a 2.5-km-long multimode fiber. The performance of the equalization is measured through analyses on eye diagrams, power coupling coefficients and bit-error rates.