Search Results (310)

With the intensifying competition among countries in lunar and deep-space exploration, the forward communication bands between Earth and the Moon are becoming increasingly congested. Spread-spectrum communication is poised to replace conventional unified-carrier wave systems as the dominant telemetry and tracking technology. Compared with Phase-Shift Keying (PSK) modulation, Gaussian-Filtered Minimum-Shift Keying (GMSK) offers lower transmit sidelobes and higher spectral efficiency, making it a promising alternative to Quadrature Phase-Shift Keying (QPSK) as a communication scheme integrated with spread-spectrum systems. This paper compares the direct-sequence spread-spectrum Gaussian-Filtered Minimum-Shift Keying (DSSS–GMSK) modulation scheme with the widely adopted DSSS–BPSK scheme and demonstrates the advantages of DSSS–GMSK, including higher spectral efficiency and improved signal acquisition performance over a wide Doppler dynamic range. This paper also proposes a deep-space DSSS–GMSK signal acquisition algorithm capable of achieving signal acquisition at a carrier-to-noise density ratio $(C/N_0)$ of 34 dB-Hz over a Doppler range from −350 kHz to +350 kHz, demonstrating the feasibility and potential of DSSS–GMSK. The algorithm requires minimal computational hardware resources, and its feasibility has been verified through simulations. Furthermore, its resistance to false locking is analyzed and discussed. Full article

Reliable ambiguity subset selection is essential for partial ambiguity resolution (PAR) in multi-GNSS and multi-frequency precise point positioning (PPP), as the increasing number of satellite–frequency ambiguities expands the ambiguity search space and reduces ambiguity-fixing reliability in high-dimensional scenarios. To address this issue, this study proposes a multi-factor ranking and screening partial ambiguity resolution (MPAR) algorithm, an entropy-weighted multi-factor ambiguity subset selection algorithm designed for multi-GNSS and multi-frequency undifferenced and uncombined precise point positioning (UDUC PPP). The proposed MPAR algorithm evaluates candidate ambiguities using three quality indicators: signal-to-noise ratio, ambiguity variance, and carrier-phase residual. Min–max normalization is used to eliminate scale differences among the indicators, while entropy-based adaptive weighting is introduced to dynamically determine their relative contributions. Based on the integrated ranking results, ambiguities are divided into easy-to-fix and hard-to-fix subsets, with the hard-to-fix subset further refined through iterative screening before integer fixing. The proposed algorithm was validated using 24 h BDS-3/GPS/Galileo observations collected from 11 globally distributed MGEX stations on day 350 of 2025 under five-, four-, and three-frequency configurations. Its performance was compared with the baseline full ambiguity resolution strategy (FAR), which fixes all candidate ambiguities without subsequent iterative exclusion after an initial fixing failure, as well as elevation-angle-factor-based partial ambiguity resolution (ELE) and variance-factor-based partial ambiguity resolution (VAR). The MPAR algorithm achieved ambiguity-fixing rates of 98.9%, 98.7%, and 99.2% under the three configurations, respectively, exhibiting performance comparable to ELE while outperforming VAR and FAR. Compared with VAR, MPAR increased the average proportions of wide-lane and narrow-lane ambiguity residuals within ±0.1 cycle by 13.6% and 46.4%, respectively. Under the five-frequency configuration, MPAR achieved the best overall performance, with horizontal and vertical convergence times of 9.1 and 8.1 min, respectively. These results demonstrate that the proposed entropy-weighted multi-factor subset selection algorithm improves ambiguity estimation quality and enhances the reliability and convergence performance of high-dimensional multi-GNSS and multi-frequency PPP. Full article

This paper proposes a high-sensitivity and sensitivity-tunable vibration sensing system based on a switchable loop length optoelectronic oscillator (OEO). Carrier-sideband separation is realized by using an acousto-optic modulator (AOM), and the resonant cavity length is designed to be independent of the sensing fiber arm. Compared with a conventional 10 GHz OEO under the same total loop delay condition, the proposed architecture provides a theoretical sensitivity enhancement of approximately $1.93\times {10}^4$, without requiring a high RF oscillation frequency. Meanwhile, the system oscillates at only 80 MHz, which greatly reduces the implementation difficulty of the frequency detection circuit. The proposed scheme further introduces a mechanical optical switch (MOS) to select intra-loop fibers of different lengths, thereby reconfiguring the equivalent loop delay and the free spectral range of the OEO. Experimental results show that stable single-mode oscillation is achieved at 80.42 MHz with a side-mode suppression ratio of 51 dB. By selecting loop fiber lengths of 1200 m, 500 m and 0 m, frequency-to-displacement sensitivities of 0.892 GHz/cm, 1.93 GHz/cm and 9.27 GHz/cm are obtained respectively, with excellent linearity. A 600 Hz vibration signal is successfully demodulated with a signal-to-noise ratio of 72.1 dB. The proposed method provides a simple and reconfigurable solution for high-precision vibration measurement under different operating conditions. Full article

Automatic modulation classification (AMC) is a critical enabler of cognitive radio (CR) systems, allowing secondary users to identify primary user modulation schemes and adapt transmission parameters in real time. Traditional AMC approaches, based on likelihood functions or hand-crafted features, suffer from degraded performance under low signal-to-noise ratio (SNR) conditions and realistic channel impairments. In this paper, we propose MS-SENet (Multi-Scale Squeeze–Excitation Network), a novel deep-learning architecture that integrates multi-scale convolutional feature extraction, squeeze-and-excitation channel attention, residual learning, bidirectional long short-term memory (BiLSTM) temporal modelling, and global attention pooling into a unified framework for robust AMC. The multi-scale convolution module employs parallel branches with kernel sizes of 3, 5, and 7 to capture both fine-grained phase transitions and coarse envelope patterns from raw in-phase/quadrature (I/Q) signal samples. Squeeze–excitation residual blocks perform channel-wise feature recalibration, enabling the network to emphasize informative feature maps while suppressing less relevant ones. A bidirectional LSTM layer models temporal dependencies across the signal sequence, and a global attention pooling mechanism performs weighted temporal aggregation prior to classification. We present a comprehensive taxonomy of deep-learning architectures for AMC organised along five axes—input representation, feature extraction, temporal modelling, regularization strategy, and architectural complexity—and conduct a rigorous comparative evaluation against ten baseline architectures on a RadioML-style synthetic dataset (110,000 samples, 11 modulation classes, and 20 SNR levels from −20 to +18 dB). The experimental results demonstrate that MS-SENet achieves a mean classification accuracy of 87.9% at SNR ≥ 0 dB (the average of the medium and high SNR regime averages: 86.06% for 0 ≤ SNR < 10 dB and 89.68% for SNR ≥ 10 dB) while maintaining a compact footprint of approximately 406 K parameters, making it suitable for deployment on resource-constrained edge devices. We further analyze the robustness of the proposed architecture to multipath fading, carrier frequency offset, and sample rate offset, confirming its resilience under practical operating conditions. MS-SENet is an architecture designed for automatic modulation classification of I/Q signals and is not related to the homonymous architecture for speech emotion recognition. Full article

In this work, we investigate deterministic chaos in external-cavity semiconductor lasers with delayed optical self-feedback. A noise-free quadrature-based delay differential model is used to isolate the intrinsic nonlinear dynamics produced by phase-sensitive delayed reinjection and carrier–photon interactions. Sensitivity to initial conditions is quantified by computing the leading Lyapunov exponents through a variational approach that integrates the base delay differential equations together with their delayed variational equations using a fourth-order Runge–Kutta method of steps and periodic QR orthonormalization. High-resolution Lyapunov maps are constructed in the $({\log }_{10}C,{\varphi }_f)$ parameter space for different pump ratios and selected short-feedback delays. The delay values are interpreted through the reference-normalized ratio ${\tau }_f/T_{R,\mathrm{ref}}$, where $T_{R,\mathrm{ref}}\approx 131.9\mathrm{ps}$ is a fixed reference timescale derived from a reference solitary-laser operating point. The results show that both the spatial organization of positive-${\lambda }_1$ regions and the mean positive Lyapunov exponent are strongly affected by feedback delay, feedback phase, feedback strength, and pump ratio. Within the selected short-delay set, delayed self-feedback produces broader, more connected, and more strongly unstable chaotic regions as the external-cavity memory time increases toward the fixed reference timescale, particularly at larger pump ratios. These findings show that short external-cavity self-feedback can support robust deterministic chaotic regimes relevant to compact and integrated photonic implementations. The proposed framework provides a reproducible deterministic reference for identifying and interpreting feedback-induced chaos in short-delay external-cavity semiconductor lasers, while stochastic effects such as spontaneous-emission and Langevin noise are left for future robustness studies. Full article

Long and narrow shielded confined spaces, represented by traffic tunnels and underground utility tunnels, constitute critical application scenarios for indoor and underground positioning services. Despite their relatively simple geometric configurations, such environments suffer from severe spatial distortion of geometric dilution of precision (GDOP). Coupled with pervasive low-elevation signal propagation and intensive multipath reflection effects, conventional BeiDou Navigation Satellite System (BDS) positioning services are unable to provide continuous and reliable coverage in these scenarios. To date, existing research on high-precision pseudolite positioning for narrow confined spaces remains largely confined to theoretical analysis and laboratory experimental verification, while systematic studies on application-oriented signal atlas feature network design are significantly insufficient, forming a prominent gap that restricts the practical engineering deployment of relevant technologies. To address the aforementioned technical bottlenecks, this paper proposes a novel BDS pseudolite signal atlas network design method to improve the continuity, stability and comprehensive positioning performance in spatially distorted narrow shielded environments. Field vehicular tests were carried out in actual engineering tunnels and underground utility tunnels to systematically analyze the variation characteristics of raw BDS pseudolite observation data, including pseudorange, carrier phase, carrier-to-noise ratio (C/N₀) and Doppler shift. The test results verified that kinematic Doppler parameters exhibited outstanding stability in complex shielded environments with strong multipath interference. On this basis, a spatial feature model based on kinematic Doppler measurements was constructed, and wavelet denoising technology was adopted to extract effective typical spatial feature parameters. Combined with the deterministic one-to-one mapping relationship between Doppler peak characteristics and spatial positions, a multi-peak kinematic Doppler atlas was established, which eliminates the dependence on pre-deployment data collection, dedicated database construction and offline model training. Furthermore, comprehensively considering multi-dimensional constraints such as spatial environment scale, carrier dynamic characteristics and terminal output rate, the atlas network scheme was optimized to achieve a balanced trade-off among positioning detection accuracy, absolute positioning precision and suppression of the pseudolite near-far effect. Comparative experimental results demonstrate that the proposed BDS pseudolite atlas network effectively resolves the inherent GNSS positioning difficulty in long and narrow shielded spaces. Benefiting from the rational spectral peak configuration strategy, the system can satisfy the continuous and stable positioning requirements of multiple carrier types including motor vehicles and railway locomotives under variable motion speeds and terminal output rates. This study provides a robust and feasible technical solution for high-precision BDS positioning services in long and narrow shielded confined spaces, and holds favorable engineering application prospects for underground navigation scenarios. Full article

It is well-known that activation functions are crucial in determining spectral expressiveness, training dynamics, and reconstruction accuracy in implicit neural representations (INRs), which employ coordinate-based multilayer perceptrons to represent continuous signals. Despite showing excellent performance, sinusoidal activations, for example SIREN, are limited in their adaptability to diverse signal types due to their fixed harmonic structure. In this paper, we propose two novel periodic activation functions for INRs. (1) Harmonic generalizes sinusoidal activations by combining the fundamental frequency with learned second and third harmonics through per-neuron trainable amplitude coefficients, resulting in a richer spectral basis within the SIREN initialization framework. (2) PM-FINER (Phase-Modulated FINER) extends the variable-periodic FINER activation by embedding frequency modulation synthesis directly into the instantaneous phase, enabling data-driven phase distortion via a learnable modulation index and carrier ratio. We conducted comprehensive experiments spanning nine architectural configurations (including SIREN, WIRE, FINER, Gaussian, Harmonic, PM-FINER, and an additional direct comparison against the Subtractive Modulative Network (SMN)), using six natural images, three learning rate schedulers, and three random seeds, totaling 486 main training runs (534 runs total including an ${\omega }_0$ sensitivity sweep). Our evaluation combined peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and rigorous statistical analysis, such as paired t-tests, Wilcoxon signed-rank tests, Cohen’s d effect sizes, and Friedman rank tests. Under cosine annealing, Harmonic achieves a mean PSNR gain of 6.08 dB over SIREN and 2.57 dB over FINER (both $p<0.001$, Cohen’s $d>3.7$), while PM-FINER ranks statistically on par with Harmonic (mean difference $0.17$ dB, $p=0.36$), outperforming all of the other baselines. Compared with SMN, Harmonic outperforms it by $+7.94$ dB under cosine annealing (Bonferroni-adjusted $p<{10}^{-5}$, Cohen’s $d=12.3$), winning on all six images. Additionally, the Friedman ranking across the six images confirmed Harmonic (with mean rank $=1.33$) and PM-FINER (with mean rank $=1.67$), being the top two methods under cosine annealing. Our results establish interpretable multi-harmonic and phase-modulated activations as real alternatives to the existing INR activation functions. Full article

Reliable spatial georeferencing of mobile mapping platforms is a fundamental prerequisite for high-fidelity urban remote sensing products such as 3D point clouds and digital twins. However, in deep urban canyons, severe signal occlusion and multipath effects reduce visible GNSS satellites, causing ambiguity resolution (AR) failure and degraded observation geometry for UGV-borne systems. Conventional Vehicle-to-Vehicle (V2V) cooperation offers limited improvement due to symmetric ground-level occlusion. To overcome this, we propose an asymmetric GNSS/UWB fusion method that introduces Unmanned Aerial Vehicles (UAVs) as high-altitude dynamic spatial anchors to reconstruct the 3D observation geometry. Two contributions are presented: (i) an asymmetric heterogeneous stochastic model coupling carrier-to-noise ratio (C/N0) and elevation angle to handle the quality disparity between air and ground sensor links, preventing multipath contamination of high-fidelity UAV observations; and (ii) a dynamic baseline constrained least-squares algorithm integrating Ultra-Wideband (UWB) ranging to stabilize GNSS positioning under high-dynamic relative motion. Validated through high-fidelity simulations and field experiments, the method achieves a 98.2% AR success rate and sub-decimeter 3D accuracy under extreme occlusion (≤3 visible satellites), while urban-canyon tests demonstrate 100% positioning availability across all evaluated epochs and reduce the 95th-percentile 3D error from 7.25 m to 0.19 m under the tested single-UAV/single-UGV configuration. The framework supports smart city modeling, 3D reconstruction, and infrastructure monitoring. Full article

To address carrier-phase loss of lock and long-term drift in frequency-offset estimation that may arise from time-of-arrival (TOA) measurements in enhanced Loran (eLoran) timing receivers under low signal-to-noise ratio (SNR) and moderate-to-high dynamics, this paper proposes a joint TOA and carrier-phase measurement and tracking method. First, transmitter identification and group repetition interval (GRI) lock are achieved by exploiting the periodic repetition of pulse groups, and epoch folding is applied to enhance effective SNR. Then, a sub-sample TOA observation is constructed via a three-stage progressive refinement procedure: energy-matching coarse estimation, coherent cross-correlation, and parabolic peak interpolation. In parallel, baseband phase observations are obtained through coherent downconversion and accumulation. A unified state-space model incorporating TOA bias, TOA drift rate, baseband phase, and frequency offset is further established to enable joint Kalman filtering of TOA and phase. Moreover, an innovation-likelihood-weighted parallel multiple-model filter combined with measurement-noise covariance inflation is introduced to suppress outlier observations. Simulations show that the TOA estimate converges within about 1 s while maintaining phase continuity and stable frequency-offset estimation, and that the proposed method achieves superior overall robustness and long-term stability compared with a conventional Costas loop. Full article

The integration of satellite and terrestrial networks within the same spectrum is a key enabler for extending mobile connectivity in future communication systems. In this context, the Direct Connectivity between Mobile Satellite Service and International Mobile Telecommunications user equipment (DC-MSS-IMT) paradigm, currently under study within the International Telecommunication Union foresees the use of terrestrial IMT frequency bands by satellite systems to directly serve conventional mobile devices. This paper presents an experimental study to assess the coexistence between a terrestrial 5G-NR receiver and a co-channel interfering signal representative of a Low Earth Orbit (LEO) satellite downlink. A controlled laboratory setup in a conducted configuration was implemented to ensure repeatability and accurate control of interference conditions. Measurements were performed over four carrier frequencies representative of IMT bands (763 MHz, 1482 MHz, 2150 MHz, and 2635 MHz), considering different traffic load conditions (100% and 50%) and Doppler shifts associated with satellite motion. The interference impact was evaluated in terms of receiver desensitization, defined as the increase in the total received power relative to the baseline noise level. The results show that a 1 dB desensitization threshold is consistently reached when the interfering signal power is approximately 5–6 dB below the receiver noise floor, corresponding to an interference-to-noise ratio (I/N) of about −6 dB. This behavior is observed across all tested frequency bands, traffic conditions, and Doppler scenarios, indicating limited sensitivity to frequency offsets within the considered range. The findings confirm the validity of commonly adopted coexistence criteria and provide experimentally derived reference values to support ongoing regulatory and technical studies on spectrum sharing between satellite and terrestrial IMT systems. Full article

To address limitations in the modulation-quality analysis of high-order Quadrature Amplitude Modulation (QAM) signals, including insufficient timing synchronization accuracy, challenges in carrier recovery, and coupling between synchronization errors and parameter estimation, a cascaded digital baseband processing framework tailored for measurement scenarios is proposed. The proposed framework is designed to integrate synchronization recovery and parameter measurement. In the timing synchronization stage, a feedforward open-loop structure based on the Oerder–Meyr (OM) algorithm is employed to estimate the optimal sampling instants rapidly. In the carrier synchronization stage, a two-stage recovery structure is constructed, comprising coarse frequency offset estimation based on polarity decision and fine synchronization using an improved frequency–phase detector (FPD), thereby achieving both robust acquisition of large frequency offsets and high-precision compensation of residual errors. On this basis, a unified modulation quality evaluation model is established, enabling the joint estimation of the Error Vector Magnitude (EVM) and the Modulation Error Ratio (MER), as well as amplitude, phase, and frequency errors, within a consistent analytical framework. System-level validation of 256 QAM and 1024 QAM signals is conducted using a MATLAB R2021b-based simulation platform. The results demonstrate that stable synchronization recovery can be achieved under timing, frequency, and phase perturbations, yielding well-defined constellation diagrams. In terms of parameter estimation, the relative errors of all evaluated metrics are maintained within 2%, which is significantly below the conventional 5% measurement criterion. Further analysis indicates that the proposed method maintains strong robustness across varying signal-to-noise ratios (SNRs) and sampling rates. The results confirm that the proposed cascaded processing framework effectively unifies synchronization recovery and modulation quality analysis, significantly improving parameter estimation accuracy while maintaining high synchronization precision. This approach provides a practical and efficient solution for high-order QAM signal testing and measurement systems. Full article

In densely built urban environments, GNSS signals frequently undergo diffraction at building edges, and the resulting errors can severely degrade positioning accuracy and reliability. Previous studies have shown a strong correlation between diffraction error and the carrier-power-to-noise-density ratio (C/N0). Building on this observation, this study proposes a GNSS diffraction-mitigation method based on segmented down-weighting and exclusion of affected observations. First, an open-sky reference model of the elevation–C/N0 relationship is established for each satellite class. A robust strategy is then introduced to adaptively down-weight moderately contaminated observations and remove severely affected ones during stochastic modeling. The proposed method is evaluated using both static and kinematic datasets collected in dense urban environments. In the static experiment under severe building obstruction, the ambiguity-fixing rate (AFR) reaches 95.5%, with horizontal and vertical accuracies of 4 mm and 8 mm, respectively, substantially outperforming conventional weighting strategies. In the vehicle-based kinematic experiment, the fixed-solution rate exceeds 80%, and the float solution is also noticeably improved relative to traditional weighting and exclusion methods. Overall, the proposed method effectively mitigates diffraction-induced errors and improves positioning performance in dense urban environments, with potential applications in automated inspection, intelligent construction, and high-precision deformation monitoring. Full article

Real-time binaural beat synthesis in dynamic acoustic environments is challenged by carrier non-stationarity, interaural phase discontinuities, and processing delay in conventional digital signal processing pipelines. This study proposes a predictive dual-stage neural framework for phase-coherent auditory synthesis under non-stationary acoustic conditions. The framework decouples real-time carrier estimation from phase-coherent signal generation through two specialized modules. An intelligent acoustic sensing module (AI-1) estimates time-varying carrier information across harmonic, fluctuating, and broadband acoustic profiles using a causal neural front-end with an adaptive confidence-driven strategy. A predictive phase-coherent generator (AI-2) then forecasts short-horizon carrier trajectories and drives a discrete-time phase accumulator to maintain continuous phase evolution during binaural beat embedding. Objective evaluation under multiple acoustic profiles and noise conditions shows that the proposed framework maintains strong phase continuity, with a Phase Coherence Factor greater than 0.91, and low artifact levels, with a Signal-to-Artifact Ratio greater than 39.8 dB, under the evaluated conditions. Additional comparisons with conventional DSP baselines, stronger classical F0 estimators, a lightweight neural F0 tracker, and component-wise ablation variants further demonstrate that the performance improvement arises from the combination of adaptive carrier estimation and predictive phase-coherent actuation, rather than from carrier estimation alone. Hardware profiling shows a combined INT8 inference time of 2.4 ms per frame on a resource-constrained Raspberry Pi Zero 2W-class edge device. Importantly, this inference time and the sub-millisecond phase-accumulator resolution should not be interpreted as sub-millisecond end-to-end physical audio latency. The complete system still includes buffering, framing, neural inference, and output processing delay; the proposed method instead reduces effective phase-boundary misalignment through short-horizon predictive compensation. These results support the proposed framework as a lightweight engineering solution for real-time phase-continuous auditory synthesis in dynamic listening environments. The reported PCF and SAR values should be interpreted as signal-level indicators of phase continuity and artifact suppression, rather than as evidence of listener comfort, perceptual preference, or neurophysiological efficacy. Full article

Orbit determination in Earth–Moon space is challenged by dynamic-model mismatch and unstable GNSS observation conditions, especially under weak and intermittent signals. To address this issue, this paper proposes a GNSS single-point positioning (SPP)-aided orbit determination method based on adaptive sliding-window filtering. A tightly coupled framework is constructed by integrating orbital dynamics propagation with SPP pseudo-range observations, allowing propagation errors to be corrected in real time through measurement updates. To enhance adaptability under time-varying observation conditions, a dynamic sliding-window strategy is introduced, in which the observation-noise covariance is adjusted according to carrier-to-noise ratio ($C/N_0$) variations. Simulations for three representative Earth–Moon trajectories, including a near-rectilinear halo orbit (NRHO), a distant retrograde orbit (DRO), and a Halo orbit, show that the proposed method significantly outperforms the conventional tightly coupled solution. The three-dimensional RMS position error is reduced from 6.65 m to 1.27 m for NRHO, from 6.57 m to 1.27 m for DRO, and from 5.91 m to 1.44 m for Halo, corresponding to improvements of 80.9%, 80.4%, and 75.4%, respectively. Under a simulated 200-epoch GNSS interruption in the Halo case, the method also improves outage robustness and post-recovery performance, reducing the three-dimensional RMS error by 23.2% in the interruption-centered interval and by 26.1% over the full arc. Full article

Co-channel interference (CCI) remains a critical factor affecting link reliability in narrowband wireless systems, especially in scenarios with intensive frequency reuse, overlapping coverage, and dense terminal access. Existing interference detection methods are either computationally simple but insufficiently sensitive to short-term spectral variations, or highly accurate but dependent on labeled data and nontrivial inference resources. To address this issue, this paper proposes a lightweight CCI detection method in the FFT domain based on spectrum-jump analysis. The proposed method does not rely on absolute power growth as the primary interference indicator. Instead, it tracks the temporal inconsistency of dominant spectral-bin indices across consecutive FFT frames and converts recurrent peak-bin migration into an interference decision through a short-window counting mechanism. The method is computationally efficient, interpretable, and suitable for real-time deployment without offline model training. SDR-based measurements are combined with controlled repeated experiments to assess detector performance under varying signal-to-noise ratio (SNR), interference-to-signal ratio (ISR), carrier-frequency offset (CFO), multi-peak ambiguity, and two-path Rayleigh fading conditions. On the measured SDR record, the proposed method captures all interference-positive windows after the marked onset, while the controlled SNR/ISR experiments yield an overall detection probability of 96.0% over 250 CCI trials with no false alarms over 250 normal trials. ROC and precision–recall analyses further show that the selected threshold lies within a broad validation plateau. The results also reveal clear applicability boundaries: when the CFO approaches zero, when the interference is very weak, or when multiple stationary peaks have nearly equal power, dominant-bin migration may be weak or ambiguous. Therefore, the proposed approach is a low-complexity online detector for CCI cases that induce observable FFT-bin instability, and it can also serve as a front-end trigger for more advanced interference analysis modules. Full article