Search Results (201)

Modulation identification plays a crucial role in contemporary wireless communication systems, especially within 5G and future-generation networks that utilize a variety of multicarrier waveforms. This study introduces an innovative algorithm for automatic modulation classification (AMC) built on a deep residual network (DRN) architecture. The approach is tailored to accurately identify advanced 5G waveform types such as Orthogonal Frequency-Division Multiplexing (OFDM), Filtered OFDM (FOFDM), Filter Bank Multicarrier (FBMC), Universal Filtered Multicarrier (UFMC), and Weighted Overlap and Add OFDM (WOLA), using both 16-QAM and 64-QAM modulation schemes. To our knowledge, this is the first application of deep learning in the classification of such a diverse set of complex 5G waveforms. The proposed model combines the deep learning capabilities of DRNs for feature extraction with Principal Component Analysis (PCA) for dimensionality reduction and feature refinement. A detailed performance evaluation is conducted using metrics like classification recall, precision, accuracy, and F-measure. When compared with traditional machine learning approaches reported in recent studies, our DRN-based method shows significantly improved classification accuracy and robustness. These results highlight the effectiveness of deep residual networks in improving adaptive signal processing and enabling automatic modulation recognition in future wireless communication technologies. Full article

Orthogonal frequency division multiplexing (OFDM) has been regarded as an attractive waveform for integrated sensing and communication (ISAC). However, suffering from its high peak-to-average power ratio (PAPR), sensitivity to phase noise (PN), and spectral efficiency saturation, the performance of OFDM in ISAC is limited. Against this background, this paper proposes a constellation-optimized index-modulated OFDM (CO-IM-OFDM) framework that leverages neural networks to design a constellation suitable for subcarrier activation patterns. A correlation model between index modulation and constellation is established, enabling adaptive constellation mapping in IM-OFDM. Then, Adam optimizer is employed to train the constellation tailored for ISAC, enhancing spectral efficiency under PN and PAPR constraints. Furthermore, a weighting factor is defined to characterize the joint communication–sensing performance, thus optimizing the overall system performance. Simulation results demonstrate that the proposed method can achieve improvements in bit error rate (BER) by over 4 dB and in Cramér–Rao bound (CRB) by 2% to 8% compared to traditional IM-OFDM constellation mapping. It overcomes fixed constellation constraints of conventional IM-OFDM systems, offering theoretical innovation waveform design for low-power communication–sensing systems in highly dynamic environments. Full article

Digital predistortion and nonlinear behavioral modeling of power amplifiers (PA) have been the subject of intensive research in the time domain (TD), in contrast with the limited number of works conducted in the frequency domain (FD). However, the adoption of orthogonal frequency division multiplexing (OFDM) as a prevalent modulation scheme in current wireless communication standards provides a promising avenue for employing an FD approach. In this work, a procedure to model nonlinear distortion in wireless OFDM systems in the frequency domain is demonstrated for general model structures based on a sparse Bayesian learning (SBL) algorithm to identify a reduced set of regressors capable of an efficient and accurate prediction. The FD-SBL algorithm is proposed to first identify the active FD regressors and estimate the coefficients of the PA model using a given symbol, and then, the coefficients are employed to predict the distortion of successive OFDM symbols. The performance of this proposed FD-SBL with a validation NMSE of $-47$ dB for a signal of 30 MHz bandwidth is comparable to $-46.6$ dB of the previously proposed implementation of the TD-SBL. In terms of execution time, the TD-SBL fails due to excessive processing time and numerical problems for a 100 MHz bandwidth signal, whereas the FD-SBL yields an adequate validation NMSE of −38.6 dB. Full article

The Automatic Modulation Classification (AMC) for underwater acoustic signals enables more efficient utilization of the acoustic spectrum. Deep learning techniques significantly improve classification performance. Hence, they can be applied in AMC work to improve the underwater acoustic (UWA) communication. This paper is based on the adoption of Hough Transform (HT) and Edge Detection (ED) to enhance modulation classification, especially for a small dataset. Deep neural models based on basic Convolutional Neural Network (CNN), Visual Geometry Group-16 (VGG-16), and VGG-19 trained on constellation diagrams transformed using HT are adopted. The objective is to extract features from constellation diagrams projected onto the Hough space. In addition, we use Orthogonal Frequency Division Multiplexing (OFDM) technology, which is frequently utilized in UWA systems because of its ability to avoid multipath fading and enhance spectrum utilization. We use an OFDM system with the Discrete Cosine Transform (DCT), Cyclic Prefix (CP), and equalization over the UWA communication channel under the effect of estimation errors. Seven modulation types are considered for classification, including Phase Shift Keying (PSK) and Quadrature Amplitude Modulation (QAM) (2/8/16-PSK and 4/8/16/32-QAM), with a Signal-to-Noise Ratio (SNR) ranging from −5 to 25 dB. Simulation results indicate that our CNN model with HT and ED at perfect channel estimation, achieves a 94% classification accuracy at 10 dB SNR, outperforming benchmark models by approximately 40%. Full article

This paper proposes a new carrier domain approach to suppress spreading first-order sea clutter in shipborne passive radar systems using Digital Radio Mondiale (DRM) signals as illuminators. The DRM signal is a broadcast signal that operates in the high-frequency (HF) band and employs orthogonal frequency-division multiplexing (OFDM) modulation. In shipborne DRM-based passive radar, sea clutter sidelobes elevate the noise level of the clutter-plus-noise covariance matrix, thereby degrading the target signal-to-interference-plus-noise ratio (SINR) in traditional space–time adaptive processing (STAP). Moreover, the limited number of space–time snapshots in traditional STAP algorithms further degrades clutter suppression performance. By exploiting the multi-carrier characteristics of OFDM, this paper proposes a novel algorithm, termed Space Time Adaptive Processing by Carrier (STAP-C), to enhance clutter suppression performance. The proposed method improves the clutter suppression performance from two aspects. The first is removing the transmitted symbol information from the space–time snapshots, which significantly reduces the effect of the sea clutter sidelobes. The other is using the space–time snapshots obtained from all subcarriers, which substantially increases the number of available snapshots and thereby improves the clutter suppression performance. In addition, we combine the proposed algorithm with the dimensionality reduction algorithm to develop the Joint Domain Localized-Space Time Adaptive Processing by Carrier (JDL-STAP-C) algorithm. JDL-STAP-C algorithm transforms space–time data into the angle–Doppler domain for clutter suppression, which reduces the computational complexity. Simulation results show the effectiveness of the proposed algorithm in providing a high improvement factor (IF) and less computational time. Full article

This paper proposes a modified polar coding-based selected mapping (PC-SLM) scheme to reduce the peak-to-average power ratio (PAPR) in orthogonal frequency-division multiplexing (OFDM) systems. In the proposed transmitter, modulated signal vector for a subset of frozen bits, termed PAPR bits, are precomputed, enabling a single polar encoder and modulator to generate multiple modulation symbols, thereby significantly reducing the hardware complexity compared to existing PC-SLM schemes. To achieve side information (SI)-free transmission, a novel belief propagation (BP)-based receiver is introduced, incorporating a G-matrix-based early termination criterion and a frozen bit check (BP-GF) for joint detection and decoding. Simulation results show that the proposed scheme significantly reduces PAPR across various code lengths, with greater gains as the number of PAPR bits increases. Furthermore, for PC-SLM schemes employing the partially frozen bit method, the BP-GF-based receiver achieves a PAPR reduction and error correction performance comparable to that of the successive cancellation (SC)-based receiver. Additionally, the BP-GF-based receiver exhibits lower decoding latency than the successive cancellation list (SCL)-based receiver. Full article

With the rapid development of visible light communication (VLC) technology, traditional modulation schemes can no longer meet the high demands for bandwidth efficiency and signal stability in complex application scenarios. In particular, in orthogonal frequency division multiplexing (OFDM) systems, issues such as the nonlinearity of Light-Emitting Diodes (LEDs) and carrier frequency offset have worsened system performance. To address these challenges, this paper proposes an N-order Minimum Shift Keying (NMSK) OFDM system with Fast Hartley Transform (FHT) for signal mapping. Monte Carlo simulations systematically compare the performance of low-order and high-order NMSK modulations under various conditions. The results indicate that low-order NMSK exhibits superior robustness against bit errors and interference, while high-order NMSK can maintain a stable PAPR and provide higher spectral efficiency in high-bandwidth demand scenarios. Further experiments validate the stability of high-order NMSK in high-density multi-user and Industrial Internet of Things (IIoT) environments, proving its adaptability and effectiveness in such scenarios. The high-order NMSK modulation scheme provides strong support for the reliability and bandwidth efficiency of future 6G VLC networks, offering significant application prospects. Full article

Modern cellular systems rely on high-capacity and low-latency optical networks to meet ever-increasing data demands. Centralized Radio Access Network (C-RAN) architectures offer a cost-effective approach for deploying mobile infrastructures. In this work, we propose a flexible and cost-efficient fronthaul topology that combines Wavelength Division Multiplexing (WDM) passive optical networks (PONs) with free-space optical (FSO) links. To enhance overall system performance, we introduce Low-Density Parity Check (LDPC) decoding, which provides robust error-correction capabilities against atmospheric turbulence and noise. Our system transmits 20 Gbps, 16-QAM intensity-modulated orthogonal frequency-division multiplexing (OFDM) signals, achieving a substantial reduction in bit error rate (BER). Numerical results show that the proposed WDM-PON-FSO architecture, augmented with LDPC decoding, maintains reliable transmission over 2 km under strong turbulence conditions. Full article

Commercial automotive radar systems for advanced driver assistance systems (ADASs) have relied on frequency-modulated continuous wave (FMCW) waveforms for years due to their low-cost hardware, simple signal processing, and established academic and industrial expertise. However, FMCW systems face several challenges, including limited unambiguous velocity, restricted multiplexing of transmit signals, and susceptibility to interference. This work introduces a unified automotive radar signal model and reviews the alternative modulation schemes such as phase-coded frequency-modulated continuous wave (PC-FMCW), phase-modulated continuous wave (PMCW), orthogonal frequency-division multiplexing (OFDM), orthogonal chirp division multiplexing (OCDM), and orthogonal time frequency space (OTFS). These schemes are assessed against key technological and economic criteria and compared with FMCW, highlighting their respective strengths and limitations. Full article

In this paper, we have proposed a filter-assisted self-coherent detection (FASCD) scheme that reconstructs the optical field of a dual-polarization complex-valued double-sideband (DP-CV-DSB) signal. At the receiver, the carrier is extracted using an optical bandpass filter (OBPF), and a pair of orthogonal carriers is constructed to achieve polarization-division multiplexing (PDM) by a Faraday rotator mirror (FRM). To address the issue of polarization crosstalk, channel estimation is performed using the least squares (LS) method, and the estimation results are further optimized through the intra-symbol frequency-domain averaging (ISFA) method. We demonstrate the system architecture and algorithms by simulation on a 224 Gbit/s 16-ary quadrature amplitude modulation DSB-PDM-OFDM system. The system performance is improved by 1 dB using the ISFA method. Full article

Frequency-modulated continuous wave (FMCW) and orthogonal frequency-division multiplexing (OFDM) technologies play significant roles in millimeter-wave radar and communication. Their combinations, however, are understudied in the literature. This paper introduces a novel OFDM-FMCW dual-functional radar-communications (DFRC) system that takes advantage of the merits of both technologies. Specifically, we introduce a baseband modulation to the traditional FMCW radar system architecture. This integration combines the advantages of both waveforms, enhancing the diversity of radar transmission waveforms without compromising high-resolution distance detection and enjoying the communication capabilities of OFDM in the meantime. We establish the system and signal models for the proposed DFRC and develop holistic methods for both sensing and communications to accommodate the integration. For radar, we develop an efficient radar sensing scheme, with the impacts of adding OFDM also being analyzed. A communication scheme is also proposed, utilizing the undersampling theory to recover the OFDM baseband signals modulated by FMCW. The theoretical model of the communication receive signal is analyzed, and a coarse estimation combined with a fine estimation method for Carrier Frequency Offset (CFO) estimation is proposed. System simulations validate the feasibility of radar detection and communication demodulation. Full article

The orthogonal frequency-division multiplexing (OFDM) transmission technique is well known to be efficient for data transmission but is susceptible to performance degradation due to factors such as high-order modulation schemes, multipath fading, and noise. In this paper, an approach for reconstructing images received by the OFDM transmission technique is proposed based on generative AI. The approach exploits a conditional diffusion model (CDM) that incorporates conditional factors reflecting the degree of distortion in the received images by the OFDM technique. Additionally, it employs a method to learn the variance in the reverse process during training, considering the level of distortion associated with various modulation schemes. Through this adaptability, the proposed model is experimentally demonstrated to optimize image reconstruction performance under various modulation schemes in low-SNR environments. The proposed conditional diffusion model can enhance the PSNR of OFDM-based received images by up to 8 dB in low-SNR conditions with various modulation schemes. Full article

6G is expected to provide ubiquitous connectivity, particularly in remote and inaccessible environments, by integrating satellite and aerial networks with existing terrestrial networks, forming Space–Air–Ground Integrated Networks (SAGINs). These networks, comprising satellites, unmanned aerial vehicles (UAVs), and high-speed terrestrial networks, introduce severe Doppler effects due to high mobility. Traditional modulation techniques like Orthogonal Frequency Division Multiplexing (OFDM) struggle to maintain reliable communication under such conditions. This paper investigates Orthogonal Time Frequency Space (OTFS) modulation as a robust alternative for high-mobility scenarios in SAGINs. Using 6G exploration library in MATLAB, this study compares the bit error rate (BER) performance of OTFS and OFDM under static and multipath channels with varying mobility scenarios from 20 km/h to 2000 km/h, and varying modulation orders (BPSK, QPSK, and 8-PSK). The results indicate that OTFS significantly outperforms OFDM, while maintaining signal integrity under extreme mobility conditions. OTFS modulates information symbols in the delay–Doppler domain, demonstrating a strong robustness against Doppler shifts and delay spreads. This makes it particularly suitable for high-mobility applications such as satellites, UAVs, and high-speed terrestrial networks. Conversely, while OFDM remains effective in static and low-mobility environments, it struggles with severe Doppler effects, common in the proposed SAGINs. These findings reinforce OTFS as a promising modulation technique for SAGINs in 6G and beyond. Full article

In this paper, delay Doppler (DD) domain is utilized for enabling an efficient handover-triggering mechanism in highly dynamic unmanned aerial vehicles (UAVs) or drones to ground networks. In the proposed scheme, the estimated DD channel gains using DD multi-carrier modulation (DDMC), e.g., orthogonal time frequency space (OTFS) modulation, are utilized for triggering the handover decisions. This is motivated by the fact that the estimated DD channel gain is time-invariant throughout the whole OTFS symbol despite the entity speed. This results in more stable handover decisions over that based on the time-varying received-signal strength (RSS) or frequency time (FT) channel gains using orthogonal frequency division multiplexing (OFDM) modulation employed in fifth-generation–new radio (5G-NR) and its predecessors. To mathematically bind the performance of the proposed scheme, we studied its performance under channel estimation errors of the most dominant DD channel estimators, i.e., least square (LS) and minimum mean square error (MMSE), and we prove that they have marginal effects on its performance. Numerical analyses demonstrated the superiority of the proposed DD-based handover-triggering scheme over candidate benchmarks in terms of the handover overhead, the achievable throughput, and ping-pong ratio under different simulation conditions. Full article

This paper presents algorithms to estimate the signal-to-noise ratio (SNR) in the time domain and frequency domain that employ a modified Constant Amplitude Zero Autocorrelation (CAZAC) synchronization preamble, denoted as CAZAC-TD and CAZAC-FD SNR estimators, respectively. These SNR estimators are invoked in a space–time block coding (STBC)-assisted multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. These SNR estimators are compared to the benchmark frequency domain preamble-based SNR estimator referred to as the Milan-FD SNR estimator when used in a non-adaptive $2\times 2$ STBC-assisted MIMO-OFDM system. The performance of the CAZAC-TD and CAZAC-FD SNR estimators is further investigated in the non-adaptive $4\times 4$ STBC-assisted MIMO-OFDM system, which shows improved bit error rate (BER) and normalized mean square error (NMSE) performance. It is evident that the non-adaptive $2\times 2$ and $4\times 4$ STBC-assisted MIMO-OFDM systems that invoke the CAZAC-TD SNR estimator exhibit superior performance and approach closer to the normalized Cramer–Rao bound (NCRB). Subsequently, the CAZAC-TD SNR estimator is invoked in an adaptive modulation scheme for a $2\times 2$ STBC-assisted MIMO-OFDM system employing M-PSK, denoted as the AM-CAZAC-TD-MIMO system. The AM-CAZAC-TD-MIMO system outperformed the non-adaptive STBC-assisted MIMO-OFDM system using 8-PSK by about 2 dB at BER = ${10}^{-4}$. Moreover, the AM-CAZAC-TD-MIMO system demonstrated an SNR gain of about 4 dB when compared with an adaptive single-input single-output (SISO)-OFDM system with M-PSK. Therefore, it was shown that the spatial diversity of the MIMO-OFDM system is key for the AM-CAZAC-TD-MIMO system’s improved performance. Full article