Search Results (768)

Automatic modulation classification of unmanned aerial vehicle wireless signals presents difficulties due to noise, fading, frequency offset and other time-varying channel impairments, and the existing deep learning methods usually exhibit a trade-off between recognition performance and computation complexity. To address this problem, we propose SupeNetUAV, a lightweight network motivated by UAV Unmanned aerial vehicles (UAVs) wireless sensing. A dual-branch stem combines raw in-phase/quadrature samples with signal-derived amplitude and differential-phase descriptors. Parallel depthwise dilated convolutions extract temporal patterns at several scales, while squeeze-and-excitation and residual paths refine the features. A compact head controls the number of channels and temporal bins used for classification. On RML2016.10A and HisarMod2019.1, SupeNetUAV achieves overall accuracies of 61.54% and 76.77% with 39,239 and 145,046 parameters, respectively. An accuracy-first comparison gives both datasets equal weight and considers resource efficiency after joint recognition accuracy. SupeNetUAV ranks first among nine evaluated implementations, with a geometric-mean accuracy of 68.73%, compared with 68.18% for MobileAmcT. Its parameter counts are 93.30% and 75.45% lower than those of MobileAmcT. Full article

Direct vehicle-to-vulnerable road user (V2VRU) communication can prevent accidents by enabling mutual awareness between vehicles and vulnerable road users (VRUs) such as pedestrians and cyclists. A prerequisite for developing reliable V2VRU communication systems is having a channel model that captures the main propagation characteristics of this specific link type. In this work, a measurement-based parameterization and validation of a WINNER-type V2VRU geometry-based stochastic channel model (GSCM) is presented at $5.2$ GHz, based on single-input single-output (SISO) wideband channel measurements in urban environments. The three most critical accident-prone scenarios involving pedestrians and cyclists are considered. The large-scale parameters (LSPs), path loss, shadow fading, delay spread, K-factor, and angular spreads, are estimated from the measurement data and fitted to log-normal distributions. The results reveal propagation characteristics that differ substantially from those reported for vehicle-to-vehicle (V2V) channels, including significantly higher path loss exponents under obstructed conditions and distinct LSP correlation structures. The proposed model is validated statistically and further assessed through link-level simulations. The simulations show that commonly used V2V channel models fail to reproduce the error floor observed in the measured V2VRU channels, which highlights the importance of using dedicated channel models when evaluating V2VRU safety applications. Full article

By adjusting antenna parameters, flexible antenna systems can dynamically reconfigure wireless channel characteristics to improve system performance. Non-orthogonal multiple access (NOMA) is a promising multiple access technique for enhancing the spectral efficiency and connectivity of next-generation wireless systems. A key research focus is how to leverage the synergy between NOMA and flexible antenna systems. A novel type of flexible antenna system called pinching-antenna system can provide line-of-sight (LoS) links to users by adjusting the position of the pinching antenna, mitigating large-scale fading. In this paper, we investigate how to implement reliable communication in a downlink NOMA-assisted pinching-antenna system by minimizing the symbol error rate (SER). We derive the SER expressions for NOMA-assisted pinching-antenna system users with QAM modulation, and minimize it with respect to the power allocation factor and the position of the pinching antenna. At higher-order modulation, it is revealed that the minimum SER strategy is for the pinching antenna to track the strong NOMA user. Finally, the analysis is verified by Monte Carlo simulation. Full article

In lunar and deep-space optical communication uplinks, atmospheric turbulence can induce temporally correlated fades in photon-limited free-space optical (FSO) channels. These fades can lead to burst errors and consequently degrade the reliability of coded transmission. In this work, we conducted a 12 km urban horizontal free-space PPM communication experiment using a room-temperature single-photon detector. The acquired photon-counting data were used to characterize the scintillation and fade-duration statistics of the atmospheric channel. We further compared the communication performance of different channel interleaving schemes under the same link conditions. At a data rate of 25.8 Mbps, the Matrix-128 scheme achieved a receiver sensitivity of 0.86 detected photons per bit at the lowest operating point with zero observed bit errors, corresponding to a 2.64 dB sensitivity improvement over the non-interleaved scheme. The experimental results show that channel interleaving effectively disperses turbulence-induced burst errors and improves the receiver sensitivity of the high-speed photon-counting PPM system. This experiment provides field experimental evidence for evaluating the performance of channel interleaving and selecting interleaving parameters for uplink photon-counting communication under realistic atmospheric turbulence conditions. Full article

Hybrid automatic repeat request (HARQ) remains the backbone of link-layer reliability, yet it was designed for a setting in which every bit matters equally and the channel of the next slot is unknown. Neither assumption holds in semantic communication over time-varying channels: some parts of a message are decisive for the receiver’s task while others are not, and the deep fade that destroyed a first transmission is usually still present when an immediate retransmission arrives. This article proposes predictive semantic-aware HARQ (PSA-HARQ), a retransmission framework that makes its decisions at the level of individual semantic units and bases them jointly on how much each unit matters and on how the channel is expected to evolve. Semantic importance is extracted from a Transformer semantic encoder, a lightweight recurrent predictor forecasts the channel a few slots ahead, and a finite-horizon optimal-stopping controller decides, for every unit and every slot, whether to transmit, wait for a better slot, or abandon the unit. The forecast enters the controller through a confidence-weighted estimator whose weight is tracked at run time from the observed prediction errors, so that the policy falls back to the statistics-only optimum whenever the predictor becomes uninformative. Simulations over a vehicular situational-awareness task and over a real public record-classification dataset, with finite-blocklength coding and time-correlated Rayleigh fading, show that PSA-HARQ matches or exceeds the task accuracy of conventional HARQ in the low-SNR regime where retransmissions are most frequent, while transmitting far fewer blocks and consuming far less energy at the same channel condition. A cost-matched ablation shows that semantic importance and channel prediction contribute complementary gains. The results indicate that anticipating the channel, rather than merely reacting to it, is a practical and largely untapped lever for reliable and efficient 6G semantic communication. Full article

Reliable semantic interaction between physical entities and their virtual counterparts is fundamental to digital-twin operation. In challenging wireless environments, however, channel noise and fading corrupt continuous semantic representations, causing semantic drift, token substitutions, repetitive generation, and premature termination at the receiver. This article proposes LM-DeepSC, a language model-aided text semantic communication framework for digital twins. The framework combines end-to-end joint source–channel semantic transmission with a trainable continuous semantic feature adapter at the receiver. The adapter projects channel-corrupted features produced by the semantic decoder into continuous conditioning representations that a frozen pretrained language model can directly exploit. Consequently, the receiver jointly exploits residual communication evidence, contextual dependencies, and pretrained linguistic knowledge to reconstruct the source text without first committing to an error-prone intermediate token sequence. Experiments over additive white Gaussian noise and Rayleigh fading channels show that LM-DeepSC consistently outperforms DeepSC in multi-order BLEU scores and sentence similarity under the same transmitted channel-symbol budget. Evaluations over ten independent AWGN channel realizations further demonstrate a relative reduction of 46.3–71.0% in token substitution rate. On MASSIVE control instructions unseen during communication-model training, LM-DeepSC improves downstream intent-classification accuracy by 6.3–15.5 percentage points over DeepSC. These results demonstrate the effectiveness of the proposed LM-assisted receiver for robust text interaction under noisy wireless channels, with potential application to digital-twin systems. Full article

Low-altitude unmanned aerial vehicles (UAVs) serving as aerial base stations for ground vehicles create air-to-vehicle (A2V) links in which both endpoints move, compressing the channel coherence time so that the reported channel quality indicator (CQI) is already stale when applied. Link adaptation calibrated for terrestrial deployments does not account for this dual-mobility aging, and the resulting overestimation of link quality inflates first-transmission errors. This paper proposes a Doppler-aware CQI correction for three-dimensional (3D) A2V Open RAN networks: an offline-calibrated back-off, indexed by the maximum Doppler frequency and the Rician K-factor, is subtracted from the measured signal-to-interference-plus-noise ratio (SINR) before CQI quantization. Because the back-off depends only on parameters the network already derives from geometry and mobility, the correction acts from the first transmission and requires no feedback convergence, unlike outer-loop link adaptation (OLLA). The scheme is implemented in Simu5G within a 3D network model providing aerial cells, dual-mobility fading decorrelation, and an Open Radio Access Network (O-RAN)-based measurement plane, and evaluated across a factorial campaign spanning three schedulers, two deployment topologies, and paired random seeds. Applying the correction to A2V links alone reduces the first-transmission error rate of aerial-served vehicles by 46–54% in an urban grid with no penalty to terrestrial links; applied network-wide, it reduces total network error by 57–62% (urban) and 54–58% (highway) and mean latency by up to 0.38 ms, at a cost of 7.3–8.8 percentage points in resource-block utilization and negligible throughput loss. Against OLLA under identical conditions, it matches or improves the error rate in the urban grid with 2.7–3.8 percentage points less overhead and reduces highway error by a further 1.2–1.3 percentage points. Full article

In the age of limited spectrum, cognitive radios (CRs) are becoming a key tool for effective spectrum use. Accurate spectrum sensing (SS) in the presence of channel impairments such as fading, noise, and interference is a crucial problem for CRs, especially during periods of low Signal-to-Noise Ratio (SNR). Here, we suggest a radial basis function neural network (RBFNN)-based adaptive equalization framework integrated with spectrum sensing techniques, specifically cyclostationary feature detection (CFD) and energy detection (ED), in order to attain a high level of detection accuracy and low false alarm rates. Moreover, the equalizer improves signal quality under nonlinear noisy channel conditions using supervised learning-based optimization. Results from the simulation show improved bit error rate (BER) and mean square error (MSE) performance for SNR ranging from 0 to 25 dB on nonlinear and noisy channels. Compared with the conventional energy detector operating without equalization, the proposed RBFNN-assisted framework achieves an 18.6% increase in detection probability and a 14.3% reduction in false alarm probability at an SNR of 5 dB. Moreover, the proposed scheme also demonstrates superior performance in terms of signal reconstruction and robust spectrum sensing in fading channels. Full article

This paper addresses the performance degradation caused by delayed channel state information (CSI) feedback in vehicular networks by introducing full-duplex (FD) technology into vehicle-to-vehicle (V2V) communications. We jointly optimize power control and spectrum allocation, designing an FD-V2V interference management algorithm aimed at maximizing the ergodic sum rate and the minimum ergodic rate of vehicle-to-infrastructure (V2I) links, respectively. Furthermore, an adaptive full-duplex V2V (AFD-V2V) interference management algorithm is proposed to dynamically select the optimal duplex mode. The proposed algorithm maximizes system performance while guaranteeing V2V link reliability by integrating large-scale fading information, statistical properties of small-scale fading, and delayed CSI of small-scale fading from non-directly connected base station links. The simulation results demonstrate that the AFD-V2V scheme significantly outperforms traditional half-duplex V2V (HD-V2V) in both the ergodic sum rate and the minimum ergodic rate. Full article

Physical-layer jamming directed at the LoRa preamble may become detectable before substantial degradation of the acquisition structure is evident at the receiver. The practically relevant question is therefore not only whether an attack can eventually be detected, but how much jammer-power headroom a monitor provides before the selected preamble-degradation onset is reached. This paper introduces a detection-theoretic evaluation framework that expresses this pre-degradation warning capability in decibels. Three quantities are defined: the detection sensitivity floor, i.e., the smallest relative jammer level yielding a detection probability of 0.9 at a false-alarm probability of ${10}^{-2}$; the preamble-based link-degradation onset, i.e., the level at which the preamble symbol error rate increases by five percentage points relative to its jamming-free baseline; and the resulting early-warning margin separating the two thresholds. The resulting early-warning margin is a power-domain measure of jammer-power headroom and should not be interpreted as elapsed warning time. The framework is evaluated over 13 isolated impairment configurations, corresponding to 221 jammer-level operating points, and complemented by a three-step severity-graded composite-channel ladder that jointly combines noise, multipath, co-channel interference, and synchronization impairments, for more than $5\times {10}^5$ packet-level evaluations. Alongside a convolutional autoencoder and a one-class support vector machine using the same three-channel in-phase, quadrature, and magnitude preamble representation, we evaluate a training-light dechirp-domain peak-to-mean ratio statistic and a simple score-level fusion rule; under independent threshold calibration, the fusion rule preserves the separability of its constituents but, under fading, interference, and synchronization impairments, does not recover the fixed-operating-point sensitivity of the dechirp statistic. Under strong co-channel interference, the proposed scalar statistic reaches the target detection probability at $-8.5$ dB, whereas the autoencoder does not reach the target detection probability anywhere within the evaluated sweep, corresponding to a sensitivity advantage of more than 14 dB and showing that the observed loss of autoencoder sensitivity is detector-dependent rather than caused by an absence of discriminative information in the received preamble. No detector dominates across all isolated impairments: the dechirp statistic is particularly effective under co-channel interference and synchronization offsets, whereas the autoencoder remains advantageous under severe multipath fading. Some learned-detector margins become negative under moderate additive noise and under severe multipath fading, and the composite-channel ladder shows a progressive degradation of detection sensitivity as joint impairment severity increases. These results provide a detector-agnostic, physically interpretable yardstick for evaluating the pre-degradation sensitivity of LoRa physical-layer jamming monitors. Full article

Adaptive semantic communication can save radio resources by matching the representation rate to channel quality, yet its decisions become unreliable when channel state information reaches the transmitter with delay. This study examines that overlooked failure mode and develops a prediction-aided framework for rate-adaptive transmission of Internet of Things sensor streams. A compact long short-term memory network forecasts a distribution of the receive signal-to-noise ratio (SNR), and a risk-aware controller selects the smallest rate whose expected semantic distortion under that forecast meets a fidelity target. The controller is independent of the semantic codec and accounts for forecast uncertainty rather than relying on a single predicted value. The framework is evaluated on six real sensor streams, over time-correlated fading channels and on SNR logs measured in a commercial 5G/LTE network, with all schemes held to the same average bandwidth and with confidence intervals over independent channel realizations. Adaptation based on stale channel information loses its advantage over fixed-rate transmission as mobility or feedback delay increases, both in simulation and on measured logs. Channel prediction recovers a substantial part of this loss, a single predictor serves the whole mobility range without retraining, and the learned forecast remains reliable when the channel statistics assumed by a model-based predictor are wrong. Prediction also keeps its advantage over stale channel information under drifting mobility, varying channel-estimation quality and frequency-selective multipath, and a predictor trained for one mobility regime can be adapted to another within seconds. A second-order analysis, whose assumptions are verified on the trained codec, explains how forecast uncertainty and the curvature of the codec’s distortion response affect the rate decision, including how often uncertainty changes the choice among discrete rates. The resulting framework is lightweight and suitable for practical semantic links operating with delayed feedback. Full article

Analog radio-over-free-space optical (RoFSO) fronthaul can transport radio waveforms over rapidly deployable optical beams, but atmospheric fading, receiver noise, orthogonal frequency-division multiplexing (OFDM) peaks, analog bandwidth, and channel-state uncertainty degrade modulation quality. This paper develops a 1 km intensity-modulation/direct-detection quadrature amplitude modulation (QAM)-OFDM RoFSO link together with a simulation-calibrated semi-analytical error vector magnitude (EVM)/outage approximation. The fixed-calibration approximation is assessed on previously untested numerical cases without refitting, with a maximum threshold-power difference of 0.29 dB. To address practical waveform and receiver effects, a separate root-mean-square (RMS)-referenced track preserves natural OFDM peak-to-average power ratio (PAPR), uses explicit pilot OFDM symbols and pilot-only least-squares gain estimation, accounts for pilot overhead in effective spectral efficiency, applies a partial RoFSO EVM budget, and derives a protection margin from empirical gain-conditioned channel-state information (CSI) errors. Under a common 5-dBm maximum active-frame mean-power ceiling, the margin-aware joint QAM/power controller achieves 1.608 b/s/Hz with 5.98% outage, compared with 1.130 b/s/Hz and 10.76% for static 16-QAM. A marginal-preserving turbulence–pointing dependence stress test produces only a modest additional outage penalty. These results are simulation-based sensitivity and consistency assessments rather than experimental validation. Full article

Non-cooperative orthogonal frequency division multiplexing (OFDM) systems over time-varying channels suffer from inter-carrier interference (ICI), deep fading, carrier frequency offset (CFO), and phase offset (PO), which severely degrade conventional automatic modulation classification (AMC) performance. To tackle this issue, we propose a bidirectional chamfered distance-based AMC method (RCD-AMC). First, a regularized subband-smoothed recursive difference division (RAM-SCDD) preprocessing is introduced. It employs an SNR-dependent regularization factor and a local subband smoothing mechanism to cancel CFO/PO effects and mitigate noise spikes induced by channel variations, yielding a stable non-negative spectral quotient sequence. Second, an RCD feature extractor is developed, which leverages bidirectional matching errors and median aggregation to suppress down-order misclassification that plagues conventional error vector magnitude (EVM) at low SNR. Third, a fuzzy support vector machine (FSVM) driven by feature confidence is constructed, where matching residuals are mapped to sample memberships, and a differential penalty scheme adaptively curbs the influence of low-quality samples on decision boundaries. Simulation results demonstrate that RCD-AMC achieves superior classification accuracy and cross-channel generalization in both homogeneous and heterogeneous time-varying channel scenarios, while maintaining low computational complexity—effectively overcoming the performance degradation of traditional feature-based AMC under dynamically varying channel conditions. Full article

This paper investigates the performance of a cascaded dual-hop relay system comprising a radio-frequency (RF) hop followed by a free-space optical (FSO) hop. The RF channel was subject to Rayleigh fading, whereas the FSO channel experienced beta-distributed dust-induced irradiance fluctuations based on experimentally obtained channel parameters. A fixed-gain amplify-and-forward (AF) relay was employed, and the FSO link operated using intensity modulation/direct detection (IM/DD) with on–off keying (OOK). Unlike conventional mixed RF/FSO studies that primarily model the optical hop through atmospheric turbulence and pointing errors, this work examined the end-to-end effect of dust-induced fading in a cascaded RF/FSO architecture. An exact integral representation of the end-to-end signal-to-noise ratio (SNR) cumulative distribution function was formulated. A finite-series closed-form approximation of the end-to-end CDF was subsequently obtained, from which corresponding closed-form approximations for the outage probability, the average bit error rate (BER), and the ergodic capacity metric were derived. The accuracy of the proposed approximations was validated through numerical integration and Monte Carlo simulations. The results show that the finite-series expressions provided highly accurate and computationally efficient performance estimates in the moderate- and high-SNR regions, while a measurable deviation may occur under very low-SNR conditions. The developed framework provides useful analytical tools for evaluating cascaded RF/FSO systems operating over beta-distributed dust-fading channels. Full article

In practical communication systems, signals suffer from attenuation during transmission over channels, particularly in multipath environments where inter-symbol interference (ISI), fading, and noise further degrade signal quality. To mitigate channel-induced distortion, receivers typically employ equalization techniques. Common channel equalization methods include linear equalizers, adaptive equalizers, and decision feedback equalizers (DFEs). Single-carrier frequency-domain equalization (SC-FDE) systems can effectively combat frequency-selective interference. Compared with time-domain equalization, frequency-domain equalization not only improves compensation performance but also significantly reduces computational complexity. This paper proposes a correlation-aware multi-observation SC-FDE receiver for frequency-selective multipath channels. The proposed receiver employs sliding windows to obtain multiple equalized observations of the same target symbol, thereby exploiting the observation diversity associated with different symbol positions and channel conditions. These observations are subsequently combined using an MVDR-based combiner, which suppresses position-dependent residual ISI while preserving the desired signal under a distortionless constraint. Through simulation tests, the proposed MVDR-based sliding-window scheme achieves favorable equalization performance under various channel models. Full article