Search Results (508)

In this work, high-resolution sparse channel estimation in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems is addressed. Firstly, a block-structured compressed channel sensing (CCS) model with high spectral efficiency and high delay resolution is constructed. Then, by fully exploiting the temporal correlation and joint sparsity of the channels, a novel two-stage prior delay support-aided delay tracking and block residual norm minimization (PDSA-DT-BRNM) algorithm is proposed. In the first stage, with a limited number of pilots for each antenna and the delay grids within the prior delay support, an efficient delay tracking and block norm minimization algorithm is put forward to choose the common delay grids and estimate each block gain iteratively. In the second stage, by comprehensively utilizing the intermediate channel estimation results of the first stage and the prior delay support, an optimized channel estimation strategy is developed based on the block residual norm minimization (BRNM) criterion. Simulation results and theoretical analysis show the effectiveness of the proposed channel estimation scheme in terms of channel estimation performance, spectral efficiency and computational complexity. Full article

This paper investigates an adaptive pilot-assisted channel estimation framework for orthogonal frequency-division multiplexing (OFDM)-based high-speed railway (HSR) communications over non-stationary wideband channels. Within this framework, a channel-aware adaptive pilot insertion (CA-API) mechanism is combined with an linear minimum mean square error (LMMSE) shrinkage technique to adjust pilot density based on temporal channel variations. Using the refined pilot-domain observations, three time-domain channel estimators namely piecewise cubic Hermite interpolation (PCHIP), autoregressive (AR), and Gaussian process regression (GPR), are comparatively evaluated under measurement-based HSR channel models. Simulation results across Remote Area (RA), Closer Area (CEA), and Close Area (CA) conditions demonstrate that the benefit of adaptive pilot scheduling is strongly scenario-dependent. In RA and CEA, the CA-API scheme reduces overhead while maintaining channel reconstruction accuracy close to that of the fixed-pilot baseline, with average overhead reductions of 38% and 30%, respectively. Under the more dispersive CA condition, the adaptive mechanism tends to increase pilot density to preserve reliable channel tracking. Among the evaluated algorithms, GPR delivers the highest estimation accuracy, AR provides a balanced trade-off between accuracy and implementation complexity, and PCHIP is less accurate but remains attractive because of its low complexity. This study provides practical insights into the joint design of adaptive pilot scheduling and channel estimation for HSR wireless communication systems. Full article

In underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) systems, the orthogonality among subcarriers is highly susceptible to Doppler-induced scaling, leading to severe inter-carrier interference (ICI). This paper proposes a coarse-to-fine Doppler estimation approach for coded orthogonal frequency division multiplexing (OFDM) systems operating in underwater acoustic (UWA) channels. The proposed method first employs the fractional Fourier transform (FRFT) to obtain an initial Doppler factor estimate from a linear frequency modulation (LFM) probe, exploiting the energy concentration property of chirp signals in the fractional domain. This coarse estimate then guides a refinement stage that leverages the cyclic prefix (CP) inherent to each OFDM symbol, enabling symbol-by-symbol Doppler tracking without waiting for the entire packet. As a result, the required memory and processing latency are substantially lower than with full-packet resampling or iterative gradient-descent alternatives. Numerical simulations conducted under both time-invariant and time-variant Doppler conditions demonstrate that the proposed scheme achieves a mean squared error (MSE) below 0.5% at signal-to-noise ratios (SNR) of 5 dB and above. Moreover, the bit error rate (BER) remains within 0.2 dB of an ideal Doppler-free system at a BER of 10⁻³. The combination of low storage demand, symbol-level operation, and robust performance makes the proposed method well-suited for real-time underwater acoustic communication. Full article

To address the growing congestion of the radio frequency (RF) spectrum, Cognitive Radio (CR) systems employ Automatic Modulation Classification (AMC) to dynamically optimize spectrum utilization without introducing protocol overhead. In modern Orthogonal Frequency Division Multiplexing (OFDM) standards, effective AMC requires advanced signal-processing techniques capable of accurately identifying modulation schemes under dynamic channel conditions. Therefore, maintaining robust performance under realistic environments remains a fundamental challenge. This paper evaluates how dataset scale, synthetic impairments, and hardware-induced signal impairments affect the cross-domain generalization of a Convolutional Neural Network (CNN) architecture for OFDM Automatic Modulation Classification (AMC), using 2D amplitude-phase histograms for signal representation. To assess these effects, the CNN is trained on five distinct datasets, encompassing both synthetically generated signals with varying scales and synchronization impairments, as well as a conducted hardware dataset. The cross-domain generalization of the trained models is assessed by evaluating them on a completely unseen indoor Over-The-Air (OTA) dataset collected across 13 distinct positions. Statistical analysis demonstrates that the large-scale synchronization-impaired synthetic dataset achieves the best generalization performance, reaching a mean indoor OTA accuracy of 93.36% and outperforming the limited-size conducted hardware dataset. Overall, this study demonstrates the critical role of data-generation strategies and establishes a robust baseline for achieving reliable cross-domain generalization of CNN-based AMC. Full article

This paper presents a novel integration of quadrature amplitude modulation (QAM)-based fast optical orthogonal frequency-division multiplexing (F-OFDM) with machine learning (ML)-based equalization in spatial division multiplexing (SDM) applications, using few-mode fibers (FMFs). The FMFs support four LP modes, resulting in a total of 12 orthogonal modes, each accommodating two polarizations. A digital multiple-input multiple-output channel equalizer is employed at the receiver’s digital signal processing (DSP) unit to effectively mitigate channel crosstalk. The study harnesses supervised ML-DSP techniques, in particular recurrent neural networks (RNNs) and deep neural networks (DNNs), achieving substantial reductions in bit error rates (BERs). In addition, higher-complexity architectures, namely convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, are evaluated to assess the impact of advanced spatial and temporal feature extraction. It is shown that F-OFDM demonstrates superior performance over conventional optical OFDM, particularly when supported by ML techniques. Simulation results reveal that RNNs achieve a BER of 0.0019 over 15 km at 12 Gbaud (worst-case selected channel), showcasing a remarkable 52.5% improvement compared to linear equalization. DNNs achieve a BER of 0.0025, reflecting a 37.5% enhancement. While RNNs perform better, their computational demands pose challenges for real-time applications, and the more complex models (CNN and LSTM) do not provide additional performance gains. The paper also explores cyclic prefix management and subcarrier number strategies in F-OFDM to optimize performance, paving the way for future advancements in SDM networks. Full article

Cell-free network architectures are a promising candidate for sixth-generation (6G) communications, as densely distributed access points (APs) flexibly accommodate traffic demands and mitigate inter-cell interference. In practical cell-free systems employing direct-conversion receivers, however, performance is severely degraded by analog front-end impairments such as in-phase/quadrature (IQ) imbalance and carrier frequency offset (CFO). Conventional orthogonal frequency division multiplexing (OFDM)-based algorithms address these impairments separately, but their joint impact is insufficiently mitigated because IQ imbalance and CFO mutually interfere, leaving residual errors when either is estimated first. To overcome this, we extend our previously proposed adaptive compensation scheme based on time-frequency interferometry-OFDM (TFI-OFDM) by introducing a decision-feedback mechanism. Preliminary CFO estimation and compensation are first performed to suppress inter-symbol interference (ISI), followed by joint estimation and compensation of IQ imbalance and CFO via decision feedback, achieving accurate channel estimation with low pilot overhead. Simulation results demonstrate that the proposed scheme effectively mitigates the mutual interference of both impairments, achieving bit-error-rate (BER) performance close to an ideal impairment-free system. These results confirm that TFI-OFDM-based joint compensation with decision feedback is a promising approach for practical 6G cell-free deployments. Full article

A discrete Hartley transform (DHT)-based orthogonal frequency division multiplexing (OFDM) scheme is investigated for intensity modulation/direct detection (IM/DD) visible light communication (VLC) systems, where transmitted signals are required to be real-valued and non-negative. To address this constraint, a practical unipolar transmission framework with corresponding bipolar reconstruction is developed. By exploiting the real-valued and self-inverse properties of the DHT, the proposed scheme removes the need for Hermitian symmetry and enables full utilization of available subcarriers. Under equal-bandwidth conditions, this results in an approximately 50% reduction in computational complexity compared with conventional DCO-OFDM and ACO-OFDM schemes. Theoretical analysis and numerical results further show that the proposed approach achieves comparable bit error rate (BER) performance while exhibiting improved spectral confinement, as reflected by reduced out-of-band sidelobes under identical filtering conditions. In addition, it maintains spectral efficiency equivalent to DCO-OFDM under the same bandwidth constraint. These advantages are achieved at the cost of restricting subcarrier modulation to real-valued constellations, which may reduce flexibility in frequency-selective channels. Overall, these findings support DHT-OFDM as a low-complexity, spectrally confined multicarrier waveform for IM/DD VLC systems, particularly in scenarios where efficient spectrum utilization and reduced adjacent-channel interference are required. Full article

Multipath propagation presents a major challenge to acoustic communication, causing signal distortion, delay spread, and inter-symbol interference, which degrade data integrity. This study investigates the use of Orthogonal Frequency Division Multiplexing (OFDM) as a robust modulation strategy for communication in complex acoustic environments where radio frequency (RF) propagation is severely attenuated. Using a software-defined radio (SDR) platform implemented in GNU Radio, OFDM performance was experimentally evaluated against Binary Frequency Shift Keying (BFSK) and Binary Phase Shift Keying (BPSK) under simulated and real multipath conditions in materials including air, water, and steel. The results show that OFDM achieves consistently lower bit error rates (BERs) and greater resilience to multipath interference due to its sub-carrier orthogonality and cyclic-prefix structure. The research also highlights how the frequency selectivity and coherence bandwidth of acoustic channels influence modulation performance across different media. By implementing custom transducers and real-time baseband processing, the study demonstrates how software-defined acoustics can be adapted for highly reflective and frequency-dependent environments. The observed improvements in BER and signal stability validate OFDM’s effectiveness in maintaining data integrity despite time and frequency dispersion effects. These findings demonstrate that OFDM enables reliable acoustic data transmission across heterogeneous media and is well suited to sensor-network applications in RF-hostile environments such as railway infrastructure, sealed containers, and submerged systems. Future work will include quantitative channel characterisation—specifically measuring delay spread, coherence bandwidth, and impulse response profiles—to further optimise OFDM parameters and provide a generalisable framework for adaptive modulation in dynamic acoustic channels. Full article

In this paper, we propose a downlink (DL) channel estimation scheme for frequency-division duplex (FDD) multi-antenna orthogonal frequency-division multiplexing (OFDM) systems, leveraging atomic norm minimization (ANM) and deep neural networks (DNN). Unlike time-division duplex (TDD) systems, where uplink (UL) and DL channels are reciprocal, FDD systems do not share this reciprocity, leading to increased channel training overhead. However, both theoretical analyses and empirical evidence reveal that key channel characteristics—such as angles of arrival and departure, path delays, and the number of propagation paths—exhibit partial reciprocity between UL and DL. Building on this insight, we design a DL channel estimation scheme that exploits frequency-independent UL parameters along with estimated DL channel gains. Our method integrates ANM with DNN to enhance estimation accuracy and efficiency. Specifically, ANM formulates the estimation problem while avoiding the off-grid errors inherent in traditional grid-based methods. To further mitigate performance degradation in clustered-path channels and reduce computational complexity, we introduce a DNN-based architecture that predicts channel parameters. The DNN captures hidden relationships between received pilot signals and frequency-independent channel parameters, enabling accurate estimation with linear time complexity. During training, ANM assists in serving users, ensuring reliable performance. Once the DNN is fully trained, it takes over to balance quality of service (QoS) and latency, providing an efficient and accurate solution for DL channel estimation in FDD-OFDM systems. Full article

With the advancement of 5G and the Internet of Things (IoT), traditional upper-layer authentication mechanisms are vulnerable to attacks, while quantum computing threatens cryptographic security. Radio frequency fingerprint identification (RFFI) offers a physical-layer solution by exploiting inherent hardware imperfections. However, in complex electromagnetic environments, narrowband and especially agile interference (characterized by low power and narrow bandwidth) can severely distort fingerprint features, rendering conventional detection algorithms ineffective. To address this challenge, this paper proposes a novel interference detection framework tailored for Orthogonal Frequency Division Multiplexing (OFDM) systems. First, a signal transmission model incorporating non-ideal hardware characteristics (e.g., DC offset, I/Q imbalance) is established. Based on this model, we design an agile interference detection algorithm comprising two key components: (1) a time-series anomaly detection method that fuses multi-domain expert features (fractal, complexity, and high-order statistics) with machine learning, demonstrating superior performance over the traditional CME algorithm under narrowband interference, and (2) a progressive search segmental detection algorithm that, combined with reconstruction error features extracted by an autoencoder, effectively identifies low-power agile interference by appropriately trading-off computation time for detection sensitivity. Finally, an OFDM simulation platform is developed to validate the proposed methods. The results show that the segmental detection algorithm achieves reliable detection at a jammer-to-signal ratio (JSR) as low as −10 dB, significantly outperforming existing approaches and enhancing the robustness of RFFI in challenging interference environments. 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

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

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

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

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