Search Results (683)

Artificial intelligence computing systems increasingly demand high-bandwidth, high-extinction-ratio, chip-to-chip optical transceivers. Silicon microring modulators (MRMs) are attractive for such transmitters due to their compact footprint and wavelength-division multiplexing capability. However, for a specified extinction ratio, the optical bandwidth for high-Q MRMs and the driver’s RC time constant prevent conventional single-segment MRM drivers from supporting 100 GBaud class PAM4 transmission. This work presents a broadband driver exploiting the feedforward technique for dual-segment MRMs. It extends electro-optical bandwidth while maintaining a high Q-factor and extinction ratio. The input signal is split into low- and high-frequency components that drive the long and short segments of the MRM, respectively. The long segment uses a broadband low-pass driver, whereas the short segment employs a driver with a programmable bandpass response near the Nyquist frequency. The design space is obtained from an equivalent electro-optical model under constant group-delay constraints. Simulations at 1310 nm show that the 3 dB electro-optical bandwidth improves from ~50 to >70 GHz and that a 200 Gb/s PAM4 optical eye diagram exhibits an open eye; the energy efficiency is 1.44 pJ/bit, and the extinction ratio improves from 2 dB to 4.1 dB. The proposed technique provides a tunable electro-optical co-design approach for high-bandwidth-density, high-extinction-ratio silicon photonic transmitters. Full article

Addressing the low efficiency associated with single-frequency serial acquisition in urban exploration using traditional electrical resistivity tomography (ERT) instruments, this study introduces a Frequency Division Multiplexing Electrical Resistivity Tomography (FDM-ERT) method and hardware system. By utilizing transmission modules that simultaneously output AC excitation signals at distinct frequencies, coupled with receiver modules that enable multi-channel parallel acquisition and data transmission, the system achieves a “one-time layout, multi-frequency synchronous measurement” workflow. Laboratory tests under controlled conditions and preliminary field tests conducted at the Xiangjiang River beach demonstrate that this method maintains relatively high consistency with traditional single-frequency measurements. The relative error of apparent resistivity across frequency points remains below 2%, with an inversion root mean square error (RMSE) of 0.4%. Furthermore, the multi-frequency synchronous mode reduces total measurement time by approximately 66.7%. While these results were obtained in relatively controlled environments, they substantiate the core feasibility of the FDM-ERT system for multi-frequency synchronous measurement, providing a certain hardware foundation for subsequent validation and optimization in complex, real-world urban settings. Full article

In frequency division duplex (FDD) massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, the sub-array multi-user (MU) hybrid beamforming architecture is highly attractive because of its low hardware cost and high energy efficiency. However, downlink channel state information (CSI) acquisition and hybrid beamformer optimization remain challenging due to the large feedback overhead and the non-convexity of the beamforming design. To address these issues, we propose an end-to-end deep learning (DL) framework that jointly optimizes pilot training, CSI feedback, and hybrid beamforming, overcoming the limitations of conventional independently designed modules. At the core of the network, we introduce the star efficient location attention (StarELA) module, which combines the implicit high-dimensional representation capability of star operations (element-wise multiplication) with the fine-grained feature localization of efficient location attention (ELA). In addition, for wideband digital beamformer generation, we exploit inter-subcarrier correlation and design a frequency–domain seed generation and interpolation upsampling strategy, which significantly reduces network parameters. Experimental results show that the proposed method approaches the upper-bound performance of conventional hybrid beamforming with ideal CSI, while consistently outperforming existing benchmark methods. Full article

Urban geophysical exploration faces significant hurdles due to strong electromagnetic interference and limited operational space, which restrict the efficiency and depth of traditional Electrical Resistivity Tomography (ERT). To overcome these limitations, this paper presents a novel ERT measurement and control system based on the Frequency Division Multiplexing (FDM) principle. Unlike conventional time-domain methods, this instrument synchronously transmits three independent AC signals at distinct frequencies. The acquisition station utilizes Fast Fourier Transform (FFT) to isolate specific frequency responses, enabling the simultaneous retrieval of apparent resistivity data for three different electrode spacings from a single transmission. The system architecture integrates low-power STM32 microcontrollers with an Android-based control terminal via Bluetooth, Wi-Fi, and NB-IoT technologies. This wireless design supports real-time current monitoring and cloud-based data synchronization. Experimental results demonstrate that the FDM operating mode significantly enhances data acquisition efficiency and anti-interference capability through frequency-domain separation. Controlled indoor and preliminary field tests indicate that FDM mode substantially improves acquisition efficiency through concurrent multi-channel measurement while effectively resolving target signals from noise. This study demonstrates the system’s technical feasibility and provides a practical foundation for future geophysical detection in time-constrained urban environments. Full article

To address the issue that multipath effect severely restricts the performance of indoor visible light positioning (VLP) systems and multipath interference intensity varies significantly across different regions, this paper proposes a spatial adaptive multipath suppression scheme for the first time. At the transmitter, a hybrid transmission architecture of time division multiplexing (TDM) and direct current biased-orthogonal frequency division multiplexing (DCO-OFDM) is employed, providing ideal observation vectors for sparse channel modeling at the receiver through specialized pilot symbol design. At the receiver, a novel Spatial Adaptive–Main Path Energy Constraint–Orthogonal Matching Pursuit (SA-MPEC-OMP, SMO) algorithm is proposed to adapt to the spatial region characteristics with varying multipath intensities, enabling low-latency and accurate separation of Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) paths. Simulation results verify that the SMO algorithm achieves high main path extraction accuracy exceeding 90% in all regions, with its LOS energy ratio 2.7 to 3 times higher than that of the traditional OMP algorithm. Based on the results of the multipath suppression scheme, a high-precision 3D VLP scheme is proposed by integrating the SMO multipath suppression with intelligent algorithms. Specifically, a point classification model performs regional partitioning and dynamic threshold matching, while a height estimation model driven by LOS power extracted via SMO estimates the height of the target point. Finally, 3D coordinates are calculated using trilateration. Simulation results indicate that through the synergy of signal design and algorithm optimization, the proposed scheme achieves centimeter-level positioning across the entire space with a single positioning time of less than 18.7 ms, featuring strong multipath robustness and promising engineering application potential. Full article

The constant growth of IP data traffic, driven by sustained annual increases surpassing 26%, is pushing current optical transport infrastructures towards their capacity limits. Since the deployment of new fiber cables is economically demanding, ultra-wideband transmission is emerging as a promising cost-effective solution, enabled by multi-band amplifiers and transceivers spanning the entire low-loss window of standard single-mode fibers. In this scenario, an accurate modeling of the frequency-dependent fiber parameters is essential to reliably model optical signal propagation. In particular, the combined impact of attenuation variations with frequency and inter-channel stimulated Raman scattering (SRS) fundamentally shapes the power evolution of wide wavelength division multiplexing (WDM) combs and directly affects nonlinear interference (NLI) generation, as well as the amount of ASE noise. In this work, we review a set of analytical approximations, based on phenomenological approaches, for frequency-dependent attenuation and Raman scattering gain, and analyze their impact on achieving an effective balance between computational efficiency and physical fidelity. Through extensive analyses performed with the open-source software GNPy (version 2.12, Telecom Infra Project) on an optical line system exploring multi-band scenarios spanning C+L+S, C+L+E, and U-to-E transmission, we demonstrate that the proposed approximations reproduce the reference SRS power evolution and NLI profiles with root mean square errors (RMSEs) consistently below 0.03 dB, and down to the 10⁻³–10⁻² dB range for the most accurate configurations. Although the current implementation does not yet provide a direct reduction in computational time, the proposed framework lays the groundwork for future developments toward closed-form or semi-analytical solutions, enabling more efficient modeling and optimization of ultra-wideband optical transmission. Full article

This paper investigates M-PSK symbol detection in Orthogonal Frequency Division Multiplexing (OFDM) systems for wideband Vehicle-to-Vehicle (V2V) communications using lightweight convolutional neural networks. In doubly dispersive channels, Inter-Carrier Interference (ICI) degrades subcarrier orthogonality, rendering conventional equalization ineffective. Current ICI mitigation techniques face a trade-off between Bit-Error Rate (BER) performance and computational complexity, limiting their applicability in dynamic vehicular scenarios. To address this issue, a low-complexity MobileNetV3-based receiver is proposed, incorporating a signal-model-driven preprocessing stage that compensates for Doppler-induced phase distortions responsible for ICI. Simulation results show that the proposed receiver improves BER performance compared to conventional equalizers and recent neural-based schemes in the low-SNR regime (below 15 dB) while maintaining computational complexity close to linear least-squares detection. Full article

Beyond supporting ultra-high-capacity data transmission, metropolitan and access networks are expected to enable real-time infrastructure monitoring, driving the emergence of integrated sensing and communication (ISAC). Distributed acoustic sensing (DAS) has proven to be well-suited to urban sensing application requirements, yet its seamless integration into ISAC remains challenging—conventional high-peak-power sensing pulses in DAS induce nonlinear crosstalk in communication channels. DAS inherently suffers from interference fading due to single-frequency laser sources, which limits sensitivity. Here, we propose an ISAC architecture based on an electro-optic (EO) comb and a 7-core fiber, achieving nonlinearity-suppressed self-homodyne transmission and fading-suppressed DAS. Unmodulated comb lines and sensing pulses are polarization-multiplexed into orthogonal polarization states within the central core to minimize nonlinear crosstalk while delivering local oscillators (LOs) for wavelength division multiplexing (WDM) coherent transmission within six outer cores—achieving 10.56 Tbit/s capacity. In addition to supporting WDM transmission, the EO comb’s wavelength diversity is also exploited to enhance DAS performance. Specifically, a dual-pulse probe loaded onto four comb lines yields a 6 dB signal-to-noise ratio gain and a 64% reduction in fading occurrences, achieving a sensitivity of 1.72 $p\epsilon /\sqrt{\mathrm{Hz}}$ with 8 m spatial resolution. Moreover, our system supports simultaneous multi-wavelength backscatter detection in sensing and simplified digital signal processing in self-homodyne communication, reducing receiver complexity and cost. Our work presents a scalable, energy-efficient ISAC framework that unifies high-capacity communication with high-sensitivity sensing, providing a blueprint for future intelligent optical networks. Full article

This paper proposes a distributed vibration sensing system based on dual-chirped pulses and weak fiber Bragg gratings. Compared with conventional dual-pulse heterodyne detection techniques, the proposed approach utilizes the time-frequency characteristics of chirp signals, effectively relaxing the stringent requirements on the signal pulse width. In addition, when chirp signals with different chirp rates are adopted, frequency-division multiplexing technology can be realized to enhance the system’s response bandwidth. Last but not least, the sensing probe signal is generated in the optical domain using microwave photonic technology, which theoretically helps alleviate the dependence on arbitrary waveform generators. A simulation study on the system was conducted. Using a chirp signal of 200 MHz/μs and with a grating spacing of 50 m, the desired vibration signal is phased modulated onto a beat signal with a frequency of 100 MHz, and the allowable pulse width can be up to 500 ns. To verify frequency-division multiplexing capability further, an additional pair of chirped pulses with a chirp rate of 400 MHz/μs was introduced into the system, which was generated by frequency multiplication. The results demonstrate that the system response bandwidth is increased to twice the original bandwidth. The proposed scheme provides a new solution for performance enhancement in a distributed fiber optic vibration sensing system. Full article

This paper presents a comprehensive analysis and simulation of a cost-effective optical Intensity-Modulation/Direct-Detection (IM/DD) Orthogonal Frequency Division Multiplexing (OFDM) system. Implemented via a MATLABR2024a and OptiSystem 23 co-simulation environment, the study evaluates a 4-QAM modulated link over a 120 km transmission distance, providing detailed investigations into signal spectral properties and constellation characteristics. To address the critical performance limitation posed by high Peak-to-Average Power Ratio (PAPR), a novel Hybrid Whale Optimization Algorithm with Selective Mapping (HWOA-SLM) is proposed. Simulation results demonstrate that the proposed scheme significantly outperforms conventional reduction techniques; specifically, at a Complementary Cumulative Distribution Function (CCDF) of 10⁻² and a fixed computational budget of 256 evaluations, the HWOA-SLM achieves a PAPR reduction gain of 3.9 dB relative to the original OFDM signal. Furthermore, in terms of algorithmic efficiency, it outperforms standard Genetic Algorithm (GA) and WOA-based SLM techniques by approximately 0.4 dB under identical computational budgets. Parametric analysis further confirms that increasing population size and iteration numbers consistently improves convergence, thereby minimizing non-linear distortions and enhancing signal integrity. Moreover, the technique exhibits superior Bit Error Rate (BER) performance, delivering Optical Signal-to-Noise Ratio (OSNR) gains of 0.63 dB, 1.31 dB, and 2.0 dB over standard WOA-SLM, GA-SLM, and conventional SLM, respectively. Conclusively, the HWOA-SLM offers a favorable trade-off between computational complexity and reduction efficiency, validating its potential for reliable, high-speed optical communication networks. Full article

With the evolution of smart grids, power communication networks are increasingly required to support high-bandwidth and diversified services such as high-definition video, real-time control, and positioning—services that impose dual challenges of communication capacity and spectrum constraints—under severe resource limitations. Conventional orthogonal modulation schemes exhibit significant limitations in spectral efficiency and concurrent access capabilities, particularly in supporting high-density user environments. To address this, we propose a communication system based on non-orthogonal overlapped chirp modulation, in which the intrinsic symmetry properties of chirp waveforms are utilized to enhance system design and performance. We first construct the system architecture with a multi-symbol concurrent transmission scheme and introduce continuous orthogonal phase modulation to improve symbol distinguishability and mitigate inter-symbol interference—an approach that effectively harnesses signal symmetry for interference suppression. At the receiver, a low-complexity demodulation algorithm based on correlation matrix computation is developed, further improved through oversampling techniques that exploit temporal and spectral symmetry in signal design. Monte Carlo simulations confirm that the proposed system outperforms traditional orthogonal chirp and orthogonal frequency division multiplexing systems in bit error rate performance and spectral efficiency across varying signal-to-noise ratios and modulation schemes. The proposed NOOC system achieves spectral efficiency scaling linearly with concurrency level K, reaching up to 16 bits/s/Hz for K = 16 with BPSK, compared to 1 bit/s/Hz in orthogonal systems. The study provides both a theoretical foundation and practical insights for developing symmetry-aware, efficient, and reliable air interface technologies suitable for future power-private networks. Full article

Affine Frequency Division Multiplexing (AFDM) has been proposed for future high-mobility communication scenarios. However, existing AFDM channel estimation methods suffer significant performance degradation under fractional Doppler conditions due to path energy dispersion. To address this issue, we propose a deep learning network that adaptively learns path energy dispersion through a 1D processing module and a Transformer block, based on the diagonal reconstruction of the AFDM effective channel matrix. 1D processing module employs convolutions with different kernel sizes to extract pilot features, and Transformer block models vary energy dispersion patterns. The proposed method does not require prior knowledge of the number of paths and the assumption of distinct path delays. Simulation results demonstrate that at a Signal-to-Noise Ratio (SNR) of 25 dB, the proposed method achieves up to a 4 dB gain in Normalized Mean Square Error (NMSE) and an 6 dB improvement in Bit Error Rate (BER) over existing traditional methods under fractional Doppler conditions. Full article

Many existing healthcare facilities still rely on the legacy Wi-Fi 5 (IEEE 802.11ac) standard, which is based on Orthogonal Frequency-Division Multiplexing (OFDM). OFDM supports single-user-per-channel access, leading to increased contention, higher latency, jitter, and packet loss under dense device deployments commonly found in clinical environments. This study presents a quantitative performance evaluation of Wi-Fi 5 and Wi-Fi 6/7 by comparing the effectiveness of OFDM with Orthogonal Frequency-Division Multiple Access (OFDMA) and Target Wake Time (TWT) in a simulated dense IoMT environment. Simulations were conducted using Network Simulator 3 (NS-3), and relevant Quality of Service (QoS) metrics. The results demonstrated that OFDMA reduces average network delay by up to approximately 37%, improves throughput by approximately 20%, and reduces packet loss ratio by up to 85% compared to OFDM under high-density operations, while exhibiting marginally improved jitter performance (approximately 2%). In addition, the use of TWT achieved substantial reductions in device power consumption of up to approximately 90%, at the cost of reduced aggregate throughput of up to approximately 75% under high station densities. These results demonstrated that Wi-Fi 6/7 technologies can offer significant advantages in terms of QoS and energy efficiency over legacy Wi-Fi 5 for dense IoMT environments. Full article

In communication-centric integrated sensing and communication (ISAC) systems, passive radars exploit existing communication signals of opportunity for sensing. To compute delay-Doppler or range–velocity maps (DDMs and RVMs, respectively), modern orthogonal frequency division multiplexing (OFDM)-based sensing systems use the channel frequency response (CFR) originally estimated in communication receivers for equalization. In OFDM-based passive radars utilizing 4G LTE or 5G NR waveforms, CFR estimation typically relies only on reference signals. However, simulation-based studies that assume a priori knowledge of user data symbols indicate potential performance gains when incorporating user data and other downlink channels. In this work, we present an experimental evaluation of an OFDM-based passive radar that jointly utilizes all commonly present components of the 5G NR downlink waveform: synchronization signals (PSS and SSS), broadcast and control channels (PBCHs and PDCCHs, respectively), data channels (PDSCHs), and reference signals (PBCH DM-RSs, PDCCH DM-RSs, PDSCH DM-RSs, and CSI-RSs). Our results show that utilizing user data from fully occupied 5G downlink signals, under the assumption of full knowledge of PDSCH locations, significantly improves both the probability of detection (POD) and the peak height, measured by the peak-to-noise-floor ratio (PNFR), compared with pilot-only sensing. Since perfect knowledge of the user data payload is not assumed, we estimate the transmission bit error rate (BER) and analyze its impact on sensing performance. Finally, we investigate more realistic scenarios in which only a subset of PDSCH resource element locations is known, as in practical 5G deployments, and evaluate how partial data location knowledge affects the POD and PNFR under different BER conditions. Full article

As wireless communications become increasingly synonymous with everyday life, the demand for higher data rates, reliability, and efficiency continues to grow. This is further accelerated by the rapid rise in the Internet of Things (IoT) and industrial automation. However, traditional algorithm-based signal processing is limited by algorithmic complexity and its limited ability to adapt to and cope with increasingly adverse and congested channel conditions, thereby reducing the effectiveness of traditional digital signal processing techniques in real-world environments. To address these challenges, approaches using Deep Learning (DL) have rapidly gained attention as a promising alternative to traditional DSP techniques. DL techniques excel in adaptability and have demonstrated on-par or even superior performance compared to traditional approaches for various RF environments, particularly in challenging conditions such as low-SNR, high-mobility, and non-ideal channel scenarios. In this survey, we examine the various stages that comprise popular wireless transmission techniques, specifically Orthogonal Frequency Division Multiplexing (OFDM), which underpins numerous technologies, including Wi-Fi, 4G LTE, 5G, and DVB. We review recent research activities to implement the various stages of the OFDM receiver chain using DL methods, including synchronization, Cyclic Prefix (CP) removal, Fast Fourier Transform (FFT), channel estimation and equalization, demodulation, and decoding. We also review approaches that take a holistic view, aiming to use a unified DL approach across the entire signal processing chain. For each stage, we review existing Deep Learning-based methods and provide insights into how they aim to meet or exceed the performance of traditional approaches. This survey provides a comprehensive overview of the current development of deep learning-based OFDM systems, highlighting the potential benefits and challenges that remain in fully replacing conventional signal processing methods with modern deep learning approaches. Full article