For co-channel FM reception with two simultaneously active sources, two-pass constant modulus algorithm (CMA) can provide a coarse decomposition of the overlapped signals, but the weak branch often remains severely distorted after demodulation. We propose TACE-Net, a two-stage asymmetric conditional enhancement framework for weak-source recovery. Stage I refines the dominant CMA branch, and Stage II enhances the weak branch using the pre-CMA mixture, the weak branch, and the dominant branch refined in Stage I. To benchmark weakbranch recovery, we construct VCTK-Radio, a dataset simulating FM modulation, co-channel mixing, CMA-based separation, and demodulation using the VCTK corpus. On VCTK-Radio, TACENet improves DNSMOS-OVRL from 1.164 to 2.901 and PESQ from 1.169 to 1.901, while reducing WER from 71.25% to 28.72% on the weak branch, outperforming competitive baselines.

Network traffic classification is crucial for both network security and management. Despite advances in deep learning-based multi-task traffic classification, existing models often struggle to jointly handle multiple tasks while providing interpretable insights. In multi-task scenarios, different tasks rely on distinct regions of the traffic sequence, motivating the use of dynamic and interpretable attention mechanisms. To this end, we propose Dynamic Quantized Self-Attention (DQSA), a unified framework specifically designed for multi-task network traffic classification. At its core, the Task Gated Attention Router (TGAR) dynamically associates attention heads with different tasks, enabling adaptive focus on task-specific patterns. This mechanism provides interpretable attention scores, which help analyze misclassifications and guide further model refinement. To improve efficiency and handle diverse network traffic features, we introduce the Soft Quantized Self-Attention Head (SQ-SAH) to reduce computational complexity and extend the Rotary Position Embedding (RoPE) to accommodate these features. Extensive experiments on ISCX VPN-NonVPN and DCI-LTE datasets demonstrate that DQSA consistently outperforms state-of-the-art baselines, achieving 92.85% accuracy on the encapsulation-level task of ISCX VPN-NonVPN and 93.17% accuracy on the application-level task of DCI-LTE, surpassing the strongest existing methods by up to 2.65%, while providing interpretable task-specific attention for efficient multi-task network traffic classification.

Accurate indoor pathloss prediction is crucial for optimizing wireless communication in indoor settings, where diverse materials and complex electromagnetic interactions pose significant modeling challenges. This paper introduces TransPathNet, a novel two-stage deep learning framework that leverages transformer-based feature extraction and multiscale convolutional attention decoding to generate high-precision indoor radio pathloss maps. TransPathNet demonstrates state-of-the-art performance in the ICASSP 2025 Indoor Pathloss Radio Map Prediction Challenge, achieving an overall Root Mean Squared Error (RMSE) of 10.397 dB on the challenge full test set and 9.73 dB on the challenge Kaggle test set, showing excellent generalization capabilities across different indoor geometries, frequencies, and antenna patterns. Our project page, including the associated code, is available at https://lixin.ai/TransPathNet/.

The site-specific non-line-of-sight (NLoS) conditions and unpredictable transmission signals in urban areas complicate localization efforts. Radio-frequency fingerprinting (RFF) addresses this challenge by building a database of signal characteristics at various locations. However, transition from indoor to outdoor environments is difficult due to the vast physical area and inaccessible sites. In this paper, we propose an RFF-based passive localization approach enhanced by ray-tracing simulation. This simulation utilizes real geographic data, including all buildings and terrains, without making assumptions about NLoS error statistics. To improve localization accuracy, we investigate the optimal placement of a moving sensor by minimizing the average mean squared error (MSE) within a confidence interval. Simulation results indicate that RFF-assisted passive localization is effective in real-world scenarios, and the optimal placement of the moving sensor significantly enhances localization accuracy.

The widespread use of encrypted communication in mobile networks poses significant challenges in accurately classifying traffic. Detecting out-of-distribution (OOD) samples, which significantly deviate from known classes, adds complexity to the task. This paper proposes a feature analysis-based OOD detection scheme for traffic classification in Long-Term Evolution (LTE) systems. Our method utilizes Long Short-Term Memory (LSTM) networks for feature extraction, capturing the feature vectors of the traffic series. Principal Component Analysis (PCA) is then applied to obtain principal and residual principal components. Leveraging the residual feature vector, we construct an OOD score to quantify deviation from the ID dataset. Extensive experiments on a large-scale encrypted mobile traffic dataset demonstrate the superiority of our approach, achieving high accuracy in OOD detection compared to existing techniques. Our method contributes to enhanced security and reliable traffic classification in LTE systems, addressing challenges posed by OOD samples.

This study explores the potential of using power lines to sense human activities. A model based on the Hertzian dipole approximation method is proposed to simulate the sensing mechanism, involving the calculation of the radiated field and the estimation of the Doppler frequency of the echoes. Simulations conducted with the proposed model demonstrate its effectiveness. Additionally, measurements performed with a universal software radio peripheral (USRP) follow the simulation results closely. These preliminary findings illustrate the feasibility of utilizing power lines for sensing human activities.

High-resolution imaging is an ever-popular research topic in the radar community. However, achieving higher resolution generally necessitates larger bandwidths, posing significant engineering challenges. To overcome these challenges, synthesizing a large bandwidth using shifting sub-bands across different center frequencies has emerged as a promising technique. This study explores the advantages and challenges of frequency modulated continuous wave (FMCW) radar imaging with shifting sub-bands. Methods to compensate for phase errors have been investigated to synthesize baseband signal. The imaging performance has been evaluated through both simulations and outdoor experiments using universal software radio peripheral (USRP) X410.

In urban areas with tall buildings and narrow streets, signal distortions from multipath and non-line-of-sight (NLOS) conditions significantly affect the localization accuracy using Long-Term Evolution (LTE) signals. To address these limitations and improve localization accuracy, we propose a teacher-student transfer learning framework based on graph neural network (GNN), utilizing LTE networks and receiver arrays. For the challenges of limited real data, our proposed model can effectively improve performance through fine-tuning with a generated synthetic dataset. Experimental findings validate the efficacy of our method, showcasing significant accuracy improvements of 41.3% and 53.3% for synthetic and real data, respectively, compared to existing techniques. Our approach outperforms conventional localization methods and alternative machine learning models, emphasizing its superior performance.

Classification of traffic service types is a valuable function for wireless communication networks. Even though some progress has been made, the recognition of the type of the traffic services cannot be done in real time. In this paper, we propose a novel method for classifying traffic series in real time based on transfer learning techniques. We pre-train a deep learning model with long traffic series and fine-tune the model with short traffic series. In this way, the developed model achieves the capability of recognising traffic services in real time. In other words, the model can recognize traffic services by using short traffic series. We collect Downlink Control Information (DCI) from commercial LTE networks when using five common types of traffic services. Then we use the dataset to validate our method. Our experimental results show that, by using proposed method, LSTM accuracy rates will increase to 80% and 88.5% when the length of the traffic series is 5 seconds and 10 seconds respectively, which is higher than the baseline. The strategy is also suitable for one dimension convolution neural network (1D-CNN).

In this paper, we address the important problem of direction of arrival (DOA) estimation using a single directional antenna under the multipath environment. We address this practically important problem by following a three step approach; i) Restoration, ii) Separation and iii) Estimation. By assuming constant modulus signal similar to earlier works, we employ the constant modulus algorithm (CMA) to restore the constant modularity which gets destroyed due to multipath, in the restoration step. In the subsequent separation step, using the restored signal as the reference, we separate the power profile corresponding to each of the direct and reflected paths. Further, to estimate DOA from these separated power profiles, a compressed sensing based algorithm is also proposed. Both the simulation and experimental results show significantly better multipath mitigation with the proposed approach than those by the existing approaches.

We consider the problem of tracking a receiver using signals-of-opportunity (SOOPs) from beacons and a reference anchor with known positions and velocities, and where all devices have asynchronous local clocks or oscillators. We model the clock drift at individual devices by a two-state model with unknown clock offset and clock skew and analyze the biases introduced by clock asynchronism in the received signals. Based on an extended Kalman filter, we propose a sequential estimator to jointly track the receiver location, velocity, and its clock parameters using altitude information together with time-difference-of-arrival and frequency-difference-of-arrival measurements obtained from the SOOP samples collected by the receiver and a reference anchor. The receiver was implemented on a software-defined radio testbed, and field experiments are carried out using Iridium satellites as the SOOP beacons. The experiment and simulation results demonstrate that our measurement model has a good fit, and our proposed estimator can successfully track both the receiver location, velocity, and the relative clock offset and skew with respect to the reference anchor with good accuracy.

We propose a novel real-time system design for multiband signal blind reconstruction using multi-coset sampling theory. Multi-channel signals are acquired under sub-Nyquist sampling frequency to perfectly reconstruct the original signal spectrum. A novel system design with Field-Programmable Gate Array (FPGA) implementation is presented in this paper. There are two main contributions in this paper. Firstly, the FPGA system uses 32-bit single precision floating point dataflow rather than conventional 16-bit fixed point to recover signals with much lower Signal-Noise Ratio (SNR). Secondly, we introduce a novel Jacobi CORDIC eigenvalue decomposition (EVD) core using parallel pivot-seeking circuit and parallel 3-CORDIC design to improve speed significantly. Hermitian matrices of dimensions from 2 to 10 are tested to compare conventional 2-CORDIC EVD and proposed EVD. The proposed EVD effectively reduces on average 36% of processing time for mesh connection system and over 50% for parallel system.

We propose a novel distributed expectation maximization (EM) method for non-cooperative RF target localization using a wireless sensor network. We consider the scenario where few or no sensors receive line-of-sight signals from the target. In the case of non-line-of-sight signals, the signal path consists of a single reflection between the transmitter and receiver. Each sensor is able to measure the time difference of arrival of the target's signal with respect to a reference sensor, as well as the angle of arrival of the target's signal. We derive a distributed EM algorithm where each node makes use of its local information to compute summary statistics, and then shares these statistics with its neighbors to improve its estimate of the target localization. We show that our distributed algorithm converges, and simulation results suggest that our method achieves an accuracy close to the centralized EM algorithm. We apply the distributed EM algorithm to a set of experimental measurements with a network of four nodes, which confirm that the algorithm is able to localize a RF target in a realistic non-line-of-sight scenario.

Spectrum blind reconstruction and direction-of-arrival (DOA) estimation of multiple narrow-band signals spread over wide spectrum sampled at sub-Nyquist sampling rates are considered in this paper. A new sub-Nyquist sampling receiver architecture which requires minimal hardware along with an efficient algorithm for estimation of the parameters and spectrum reconstruction is presented. We further show with the proposed approach, that a minimum average sampling rate of 2(N + 1)B would be sufficient in order to reconstruct the spectrum as well as estimate their corresponding DOA of N narrow-band signals of maximum bandwidth B. Simulation results are also provided which shows the performance very close to the Cramer Rao bound.

Motivated by the existing time-frequency peak filtering (TFPF) algorithm, herein a robust time-varying filtering (RTVF) algorithm is proposed for filtering and separating multicomponent frequency modulation (FM) signals. The performance of the TFPF based on windowed Wigner-Ville distribution is limited by the linear constraint on the waveform of the received signal. The proposed RTVF significantly improves the filtering performance with low complexity by applying a sinusoidal time-frequency distribution, which allows a sinusoidal constraint on the signal's waveform. The RTVF can successfully decompose a multicomponent signal into individual components based on an initial instantaneous frequency (IF) estimate of each component. Unlike existing time-varying filters, the RTVF is much less sensitive to the accuracy of the IF estimate, which can be gradually refined by performing an iterative RTVF procedure.

We consider the problem of localizing two devices using signals of opportunity from beacons with known positions. Beacons and devices have asynchronous local clocks or oscillators with unknown clock skews and offsets. We model clock skews as random, and analyze the biases introduced by clock asynchronism in the received signals. By deriving the equivalent Fisher information matrix for the modified Bayesian Cramér-Rao lower bound (CRLB) of device position and velocity estimation, we quantify the errors caused by clock asynchronism. We propose an algorithm based on differential time-difference-of-arrival (DTDOA) and frequency-difference-of-arrival (FDOA) that mitigates the effects of clock asynchronism to estimate the device positions and velocities. Simulation results suggest that our proposed algorithm is robust and approaches the CRLB when clock skews have small standard deviations.

In this paper, a new method based on estimation and line-of-sight (LOS) detection of individual time-of-arrival (TOA) measurement and Kalman filter (KF) is proposed to track a moving target in mixed line-of-sight and non-line-of-sight (LOS/NLOS) environments. In the proposed tracking algorithm, TOA measurements collected by multiple stationary sensors in a wireless sensor network are used. First, a pseudo-measured position is calculated by choosing the point along the circle defined by a given TOA measurement which has the shortest distance to the predicted position of a moving target. The pseudo-measured position is shown to be an approximately unbiased estimate of the true position of the target. Second, each pseudo-measured position calculated is passed to a detector to be identified as either LOS or NLOS. The average of all the selected LOS pseudo-measured positions is then used as a new pseudo-measurement for the KF to track the moving target. Unlike all the existing target tracking algorithms in mixed LOS/NLOS environments, the proposed tracking algorithm is able to perform target tracking even with just one LOS TOA measurement at a given time instance without prior information of the NLOS noise which may be difficult to obtain in practice. Another advantage of the proposed tracking algorithm is its computational efficiency. Simulation results show that the proposed tracking algorithm performs better than some recent tracking algorithms, particularly in severe mixed LOS/NLOS environments.

This paper considers spectrum-blind reconstruction (SBR) of multi-band signals (MBS) which are sampled with multi-coset sampling (MCS) architecture. A new SBR algorithm which we refer to as Khatri-Rao SBR (KR-SBR) is presented. With this new KR-SBR algorithm, the average MCS rate can be reduced by 50% whilst attaining the same performance as that of the existing state-of-art SBR algorithms. Under certain conditions we will also show that the proposed KR-SBR algorithm has the capacity to achieve SBR when the average MCS rate approaches the Landau sampling rate. Simulation results are also presented to demonstrate the advantages of the proposed KR-SBR algorithm.

In this paper, a novel individual angle-of-arrival (AOA) measurement detection method and extended Kalman filter (EKF) based tracking algorithm is proposed. The detection method is used to detect whether an individual AOA measurement is line-of-sight (LOS) or non-line-of-sight (NLOS). After the measurement detection, the selected LOS AOA measurements are then used into an dynamic EKF to track a moving target in mixed LOS/NLOS environments. Different from some traditional NLOS error detection methods, which determine the estimation result of a set of AOA measurements collected at every time step is LOS or not, the proposed method detects each AOA measurement one by one at one time step. This algorithm makes good use of LOS AOA measurements and greatly improves the tracking accuracy of the EKF in mixed LOS/NLOS environments. Simulations implemented under different NLOS percentage scenarios demonstrates the improvement of the classical EKF with the assistance of the proposed measurement detection method for AOA measurement.

The estimation of the mixing matrix as well as the number of sources in blind source separation are two challenging problems. This paper proposes an effective estimation method to solve these two problems for underdetermined blind separation of overlapped sources in short-time Fourier transform (STFT) domain. Our study considers the blind estimation of the mixing matrix based on subspace projection as well as clustering methods, and the number of sources can be therefore estimated by counting the columns of the estimated mixing matrix. The proposed estimation method is noise-robust and suitable for the sources whose spectral contents are highly overlapped in STFT domain. Numerical results on speech sources are presented to illustrate the effectiveness and robustness of the proposed method.

A simple but effective approach based on gating and robust extended Kalman filtering (EKF) is proposed for target tracking in mixed line-of-sight and non-line-of-sight (LOS/NLOS) environments, using time-of-arrival (TOA) measurements. Utilizing a gating based detection method determined by the statistical properties of the TOA measurements, a confidence region is derived. Most of the LOS TOA measurements can be effectively selected from all mixture measurements using the properly defined confidence region. These selected LOS TOA measurements are then passed on to the dynamic robust EKF to do the target tracking. Simulations have shown the proposed tracking approach has outperformed both the classic rEKF as well as a recent target tracking approach. Moreover, the proposed approach does not require any statistical knowledge of the NLOS error and only assumes that the standard deviation of the LOS noise is known.

We consider the problem of localizing two sensors using signals-of-opportunity from beacons with known positions. Beacons and sensors have asynchronous local clocks or oscillators with unknown clock skews and offsets. We analyze the biases introduced by clock asynchronism in the received signals, and derive the Cramér-Rao lower bound (CRLB) for sensor position and velocity estimation errors. We quantify the error caused by beacon clock asynchronism. We also propose an algorithm that mitigates the effects of clock asynchronism to estimate the sensor positions and velocities. Simulation results suggest that our proposed algorithm is robust and approaches the CRLB when sensor clock skews have small standard deviations.

This paper proposes an approach to improving the target tracking accuracy in mixed line-of-sight/non-line-of-sight (LOS/NLOS) environments based on individual measurement detection (IMD) and road constraints. Utilizing the IMD algorithm, most LOS time-difference-of-arrival (TDOA) measurements can be correctly selected from mixed LOS/NLOS TDOA measurements. Incorporating the prior knowledge of the road constraint as a pseudo-measurement, the augmented dynamic extended Kalman filter (EKF) is then implemented to track a moving target. Simulation results demonstrate that the road constraint assisted algorithm, in conjunction with the IMD method, performs better than the road constraint assisted algorithm without the IMD method, or tracking algorithm with the IMD method but without the road constraint. The proposed method is especially useful when the number of selected LOS TDOA measurements is less than three.

We consider the problem of localizing two sensors using their TDOA measurements of signals from beacons with known positions. Beacons and sensors have asynchronous local clocks or oscillators with unknown clock skews and offsets. We analyze the effect of the clock skews and offsets on the TDOA estimations, and introduce a novel approach using the difference in TDOA (DTDOA) measurements to mitigate the effects of clock skews and offsets on the location estimates. We propose an algorithm for location and velocity estimation based on DTDOA, and perform simulations to verify the robustness of our algorithm to asynchronous local clocks in the beacons and sensors.

Non-line-of-sight (NLOS) error is one of the most important factors affecting the accuracy of positioning or tracking especially in urban or indoor environments. This paper concentrates on alleviating the influence of the NLOS error. A position detection approach utilizing the circle error probability (CEP) in conjunction with the least square (LS) method and Kaiman Filter (KF) tracking algorithms is proposed. The LS is used to estimate the positions of a moving target from time-of-arrival (TOA) measurements and the KF is used to smooth the tracking trajectory. Simulation results show that this approach can effectively identify line-of-sight (LOS) estimated positions, leading to higher tracking accuracy than that by other tracking methods without using position detection.

This paper addresses the problem of heart sounds (HS) localization from single channel respiratory sounds (RS) recordings by applying wavelet-based localization scheme. After a wavelet-based multiscale decomposition of the noisy signal, HS contaminated segments are localized in the noisy RS signal based on the cumulative sums of likelihood ratios capturing the dynamic behaviour of the signal. Quantitative evaluation of the localized HS segments for various types of simulated data has been performed. The comparisons between the estimated boundaries of the localized HS segments and the actual segment boundaries of the synchronized pure HS signals show the proposed method is able to localize the HS segments accurately in an automatic way. Also, the test results on real RS recordings in terms of the detection accuracy show the promising performance by the proposed method.

For copyright protection, the robustness of a watermarking scheme against various attacks is an essential requirement. Many proposed robust watermarking schemes may achieve good robustness but sacrifice the good quality of the watermarked image. This paper, therefore, proposes a transparent robust watermarking scheme, which embeds the watermark (or the secret information) adaptively in the discrete-cosine transform (DCT) domain. The proposed scheme is blind as the original image is not needed, and only a key containing the watermark locations and the scaling factors is required for watermark extraction. This adaptive replacement embedding technique can guarantee the preserving of the good visual quality of a watermarked image. Comparing with other existing DCT-domain watermarking methods, the proposed watermarking method can achieve higher robustness performance, while retaining better quality of the watermarked image in terms of PSNR.

This paper addresses the general problem of estimating parameters in nuclear spectroscopy. We present a unified Bayesian formulation to tackle the various aspects of this problem. This includes deconvolution and modelling of both the peaks and background. The peaks are modelled with Gaussian or Lorentzian type functions and the background with cubic B-splines. The number of peaks and spline knots are treated as unknowns and as such are also estimated together with the model parameters. The Bayesian model allows us to define a posterior probability on the parameter space upon which all subsequent Bayesian inference is based. Direct evaluation of this distribution or its derived features such as the conditional expectation is, unfortunately, not possible on account of the need to evaluate high-dimension integrals. As such we resort to a stochastic numerical Bayesian technique, the reversible-jump Markov-chain Monte Carlo(RJMCMC) method.