This paper proposes a unified analytical framework for reconfigurable intelligent surface (RIS)-assisted two-way full-duplex (TW-FD) communication systems in 6G Internet of Things (IoT) scenarios. The proposed framework specifically addresses RISs with N reflective elements, facilitating efficient bidirectional communication. A novel unified moment-based analytical approach is developed, accommodating diverse fading models including Rayleigh, Nakagami-n, Weibull, Nakagami-m, and κ-μ, thereby significantly enhancing the versatility and practicality for complex 6G IoT environments. To comprehensively validate the applicability of our analysis, both independently identically distributed (i.i.d.) and independently non-identically distributed (i.n.i.d.) fading channel scenarios are investigated. By employing the Edgeworth expansion method, we derive analytical expressions for the probability density function (PDF) and cumulative distribution function (CDF) of the end-to-end signal-to-interference-plus-noise ratio (SINR). Additionally, closed-form expressions for probability, average symbol error rate (SER) for various modulation schemes, and end-to-end ergodic rate are provided. Monte Carlo simulations demonstrate the accuracy and robustness of the theoretical models proposed. The results presented in this work not only contribute substantially to the analytical methodologies for RIS-assisted communication but also offer practical guidance for the design and optimization of future 6G IoT systems.

Generating coherent and diverse human dances from music signals has gained tremendous progress in animating virtual avatars. While existing methods support direct dance synthesis, they fail to recognize that enabling users to edit dance movements is far more practical in real-world choreography scenarios. Moreover, the lack of high-quality dance datasets incorporating iterative editing also limits addressing this challenge. To achieve this goal, we first construct DanceRemix, a large-scale multiturn editable dance dataset comprising the prompt featuring over 25.3 M dance frames and 84.5 K pairs. In addition, we propose a novel framework for iterative and editable dance generation coherently aligned with given music signals, namely DanceEditor. Considering the dance motion should be both musical rhythmic and enable iterative editing by user descriptions, our framework is built upon a prediction-then-editing paradigm unifying multimodal conditions. At the initial prediction stage, our framework improves the authority of generated results by directly modeling dance movements from tailored, aligned music. Moreover, at the subsequent iterative editing stages, we incorporate text descriptions as conditioning information to draw the editable results through a specifically designed Cross-modality Editing Module (CEM). Specifically, CEM adaptively integrates the initial prediction with music and text prompts as temporal motion cues to guide the synthesized sequences. Thereby, the results display music harmonics while preserving fine-grained semantic alignment with text descriptions. Extensive experiments demonstrate that our method outperforms the state-of-the-art models on our newly collected DanceRemix dataset. Code is available at https://lzvsdy.github.io/DanceEditor/.

Indoor localization systems face critical tradeoffs between accuracy, coverage, robustness, and privacy. Current technologies either achieve high precision at the cost of privacy (camera-based) or preserve privacy while compromising accuracy (signal-based). This limitation is particularly problematic in high-security environments requiring precise tracking within confined spaces (typically 2m × 2m) while prohibiting camera surveillance. We propose WhereRU, a novel multimodal indoor localization system integrating Radio Frequency Identification (RFID) with ultrasonic technologies through a fusion framework. Unlike existing approaches treating modalities as separate components, our system implements deep algorithmic integration where RFID-based coarse-grained localization directly constrains ultrasonic measurements. The system employs WhereNet, a lightweight residual network for RFID processing, achieving 99.74% accuracy at the grid level. This coarse position estimate guides ultrasonic ranging optimization through Limited-memory Broyden-Fletcher-Goldfarb-Shanno Bound (L-BFGS-B) algorithm, effectively mitigating edge-region inaccuracies typical in ultrasonic-only systems. Experimental results demonstrate that WhereRU achieves an average localization error of only 0.107 meters–representing an 81.86% improvement over traditional Time of Arrival methods–while maintaining complete privacy by eliminating biometric data collection. This approach provides a practical solution for secure facilities requiring both centimeter-level localization accuracy and strict privacy preservation.

Semantic communication is a current research hotspot emphasizing the transmission of content-related semantics. However, this focus increases vulnerability to attacks that exploit high-dimensional features, underscoring the urgent need for robust defense mechanisms against evolving threats. Traditional defense strategies are often computationally intensive, require continuous updates, and perform inadequately against sophisticated adversarial tactics in noisy environments. In this paper, we propose a Dynamic Adversarial Defense Framework (DADF), a model-agnostic, plug-and-play solution for seamless integration with semantic communication systems, requiring no modifications for deployment. The Adversarial Pattern Learning (APL) module aligns adversarial and genuine audio samples within a unified feature space, facilitating noise pattern extraction and significantly reducing interference. Subsequently, a Dynamic Noise Filter (DNF) employs adaptive mechanisms to eliminate harmful noise based on noise patterns extracted from the APL. Testing across five public benchmarks demonstrates DADF's superior performance, with peak achievements including a 9.31-fold reduction in mean squared error (MSE) and a 269.78% increase in signal-to-distortion ratio (SDR), significantly outperforming existing solutions. This breakthrough establishes a new bench-mark for resilience and adaptability in semantic communications, enhancing security against evolving adversarial attacks. The code is available at https://github.com/sweet0516IDADF.

In this paper, we propose “SoundSpring”, a cutting-edge error-resilient audio transceiver that marries the robustness benefits of joint source-channel coding (JSCC) while also being compatible with current digital communication systems. Unlike recent deep JSCC transceivers, which learn to directly map audio signals to analog channel-input symbols via neural networks, our SoundSpring adopts the layered architecture that delineates audio compression from digital coded transmission, but it sufficiently exploits the impressive in-context predictive capabilities of large language (foundation) models. Integrated with the casual-order mask learning strategy, our single model operates on the latent feature domain and serve dual-functionalities: as efficient audio compressors at the transmitter and as effective mechanisms for packet loss concealment at the receiver. By jointly optimizing towards both audio compression efficiency and transmission error resiliency, we show that mask-learned language models are indeed powerful contextual predictors, and our dual-functional compression and concealment framework offers fresh perspectives on the application of foundation language models in audio communication. Through extensive experimental evaluations, we establish that SoundSpring apparently outperforms contemporary audio transmission systems in terms of signal fidelity metrics and perceptual quality scores. These new findings not only advocate for the practical deployment of SoundSpring in learning-based audio communication systems but also inspire the development of future audio semantic transceivers.

With the rapid development of the Internet of Things (IoT), indoor localization has become a critical component of numerous applications. Among them, non-contact indoor localization techniques based on Radio Frequency Identification (RFID) fingerprints have garnered significant attention. However, due to the complexity of indoor environments, existing methods achieve satisfactory localization performance on training data, but still face challenges in accurately recognizing the locations of individuals who were not part of the training data. To address these challenges, we propose the RP-Fusion. In our work, we construct a two-stream fusion network to extract fused fingerprint features from both Received Signal Strength Indication (RSSI) and phase. These fused fingerprint features can better map to location characteristics, reduce the impact of individual differences. Experimental results demonstrate the effectiveness of our method in achieving robust localization for untrained individuals, with the accuracy of 99.23%. This outperforms the majority of existing RF fingerprint-based indoor localization results.

Existing integrated sensing and communication (ISAC) systems face two critical challenges: a trade-off between resource allocation and interference management, and a lack of a unified performance metric that can be simultaneously applied to both sensing and communication functions. To address two aforementioned issues, a non-orthogonal multiple access-assisted ISAC (NOMA-ISAC) scheme is first proposed in this paper, which can simultaneously perform the sensing and communication tasks on shared radio resource, significantly improving spectral efficiency while mitigating mutual interference. Furthermore, in order to provide a unified metric to simplify the performance analysis in ISAC systems, we extend the definition of the outage probability from a mean square error (MSE) perspective. Then based on the built framework, the tractable expressions of the outage probability are derived for both communication and sensing tasks to evaluate the performance of our proposed NOMA-ISAC scheme and the conventional orthogonal radio resource-based ISAC (OR-ISAC) scheme. Next, the asymptotic outage probability comparisons are carried out to show that the NOMA-ISAC scheme can achieve better performance than the OR-ISAC scheme. Finally, simulation results are provided to verify the analytical derivations, and also demonstrate the robustness and performance gains of our proposed NOMA-ISAC scheme over OR-ISAC scheme.

RFID-based human activity recognition has attracted extensive attention due to its low cost, non-invasiveness, and privacy protection. However, existing methods may limit cross-domain sensing as the mapping between activities and signals is destroyed when the environment changes. Meanwhile, these methods lack generalization as their systems need to be fully retrained whenever new activities are added, which incurs data collection and retraining overheads. This paper proposes a few-shot learning-based RFID sensing system, TransRF, composed of a signal processing module, a feature extraction module, and a classification module. Specifically, to recognize novel classes in unknown domains with limited samples, the feature extraction module consists of multi-head self-attention and multi-scale hybrid dilated convolution and pre-train it with source domain data. Therefore, when the system is applied to the target domain, a few samples are required to fine-tune the classification module for domain adaptation. Experimental results on the genuine RFID dataset show that TransRF achieves an accuracy of 98.0% in unknown domains, a 31.4% improvement over the state-of-the-art. Additionally, we published our dataset containing three scenarios with 1.728 million pieces of data.

Indoor public places are facing more and more security risks, and need to be monitored to find potential anomalies. Benefiting from the advantages of low cost and high privacy, RFID is widely used in indoor monitoring. At present, it has become a common solution to construct the RFID raw data into time sequence trajectory, and then perform preprocessing and cluster analysis. However, there are redundant and uncertain factors in the RFID raw data, which affect the efficiency of anomaly detection. In this paper, we propose BPCluster, a probabilistic-based RFID trajectory anomaly detection algorithm for indoor RFID trajectories. The algorithm incorporates a probabilistic trajectory model, which reduces the redundancy and uncertainty through the context information of trajectories, and then clusters trajectories by the improved LCS algorithm to find abnormal trajectories. Experiments show that BPCluster has better performance in effectiveness and environmental adaptability, and the average accuracy in various environments reaches 91%.

Federated learning is a new artificial intelligence technology with which an edge server can orchestrate with multiple end users to train a global model collaboratively. Under this setting, users only upload the locally trained parameters instead of their local data, substantially reducing communication costs and boosting data privacy. Nonetheless, federated learning mainly relies on users’ local training, overlooking the abundant computing resources owned by the edge server. To exploit the edge server’s processing power, we propose a hybrid learning paradigm that consists of centralized and federated learning components. This scheme uploads a portion of users’ data for centralized learning when the local model is trained under federated learning. We derive a theoretical upper bound for the model accuracy, which can be used to assess the performance of the proposed new learning paradigm. To balance the computation and communication resources for a good model accuracy performance, we establish a joint optimization problem of model accuracy, latency, and energy consumption. We also devise the corresponding joint optimization algorithm to solve the problem. Experiment results show that compared with centralized and federated learning, the proposed hybrid learning algorithm can effectively improve the model accuracy and significantly reduce computation and communication resources.

In practical compressive sensing (CS) communication system, Analog to Digital Converter (ADC) is a necessary component to convert analog signals into digital ones. However, nonlinear distortion in ADC is unavoidable and definitely affects the reception accuracy. Though recent works have studied various methods to combat the negative effect of ADC nonlinear distortion, few of them discuss the solution in communication system with CS. This paper studies the CS recovery method when compressive measurements suffer from ADC nonlinear distortion. A double-sparsity model is first formulated, where the original signal and the ADC nonlinear distortion are both sparse. Then, for the type of clipping ADC, we propose a corresponding algorithm based on the Alternating Direction Method of Multipliers (ADMM) strategy to solve the double-sparsity (DS) problem, named as DS-ADMM. For the type of self-reset (SR) ADC, we explore its essence of rounding operation to design an integer constraint based feedback updating (ICFU) strategy, and accordingly propose the DS-ADMM-ICFU recovery algorithm. Experiment results show that the DS-ADMM algorithm for the double-sparsity problem improves the recovery performance compared with the existing counterpart, and DS-ADMM-ICFU for SR ADC exhibits preferable advantage in typical communication systems.

Over the past years, tremendous progresses have been achieved for wireless positioning system based on Received Signal Strength (RSS). However, most researches assume ideal RSS data, which ignore the anomalies that generally exist due to the interference in signal and instrument malfunction. To reduce the negative influence of anomalies on system performance, anomaly detection is regarded as an important preprocessing technique. To better distinguish anomalies from the RSS data that is distributed in multiple blobs, this paper proposes a two-step anomaly detection scheme called K-means aided Extended Isolation Forest (KEIF). The first step is to exploit K-means to cluster the received data according to the RSS features. Then, based on the positions of source node, Extended Isolation Forest (EIF) is employed for each cluster to obtain anomaly scores, which represent the isolated degree of data points. The data with scores higher than a threshold is considered as anomalies. We verify our proposed scheme in a RSS-based fingerprinting wireless positioning system, and the experiments demonstrate that the real dataset processed by KEIF can effectively improve the positioning accuracy, compared with the original dataset without anomaly detection and the datasets processed by other existing anomaly detection schemes.

Human activity recognition has a wide range of applications, especially for the care of elderly people living alone and the monitoring of abnormal behaviors of key personnel. Although conventional video surveillance technology has made many research advances in this field, this technology destroys people's privacy. Activity recognition technology based on RFID avoids damage to people's privacy, and is being widely studied and applied. This paper uses RFID Received Signal Strength Indicator (RSSI) to identify and classify human behaviors. Predecessors employed CNN and LSTM for human activity identification, but there were still some shortcomings: 1) The 2D convolution loses the temporal information of continuous actions and reduces the classification accuracy. 2) LSTM network has a series of training difficulties. 3) No available public dataset for the current mission. To solve these problems, this paper proposes a convolutional neural network called temporal spatial convolutional neural network (TSCNN). Taking the continuous frame sequence as input, the network is designed using 3D convolution to realize realtime activities recognition. The average classification accuracy of our network is 94.6%, 15.6% higher than the state-of-the- art - Tagfree. Our lowest accuracy is 81.8%, and Tagfree is 35.4%. Besides, the ablation experiment proves the necessity of the design in the TSCNN network. Furthermore, we collect more than 60000 RFID signal data and transform them into corresponding pixel maps to form a new dataset. We present and expose the dataset called RF-men.

MOTIVATION: The classification of high-throughput protein data based on mass spectrometry (MS) is of great practical significance in medical diagnosis. Generally, MS data are characterized by high dimension, which inevitably leads to prohibitive cost of computation. To solve this problem, one-bit compressed sensing (CS), which is an extreme case of quantized CS, has been employed on MS data to select important features with low dimension. Though enjoying remarkably reduction of computation complexity, the current one-bit CS method does not consider the unavoidable noise contained in MS dataset, and does not exploit the inherent structure of the underlying MS data. RESULTS: We propose two feature selection (FS) methods based on one-bit CS to deal with the noise and the underlying block-sparsity features, respectively. In the first method, the FS problem is modeled as a perturbed one-bit CS problem, where the perturbation represents the noise in MS data. By iterating between perturbation refinement and FS, this method selects the significant features from noisy data. The second method formulates the problem as a perturbed one-bit block CS problem and selects the features block by block. Such block extraction is due to the fact that the significant features in the first method usually cluster in groups. Experiments show that, the two proposed methods have better classification performance for real MS data when compared with the existing method, and the second one outperforms the first one. AVAILABILITY AND IMPLEMENTATION: The source code of our methods is available at: https://github.com/tianyan8023/OBCS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The use of radio-frequency identification (RFID) technology in supply chain has been a fairly mature application in recent years, which can be extended to the field of carrier management for the inventory and access control of sensitive files and mobile storage medium. To address the inherent defects of false readings of RFID, we present RF-AMOC, a tag movement identification system that leverages the signal variation patterns between the opposite antennas and the tag to accurately determine whether someone takes the sensitive carrier out of the room or just the normal carrier usage activity in the room. Particularly, we focus on two kinds of signal variation modes: Direct side models, where the RSSI is sensed by one antenna on the tag side, and obstruction side models, where the RSSI is sensed by the other antenna that was obstructed by the person. Then, Pearson Coefficient and crest comparison algorithms are adopted to match the theoretical and actual RF-signal curves on the two sides, respectively. Additionally, a starting point acquisition method is proposed to extract the meaningful time period. A prototype of RF-AMOC is realized in two different environments with various persons, and the results validate that it is superior in terms of sensitivity and specificity with strong robustness.

The Radio Frequency Identification (RFID) data acquisition rate used for monitoring is so high that the RFID data stream contains a large amount of redundant data, which increases the system overhead. To balance the accuracy and real-time performance of monitoring, it is necessary to filter out redundant RFID data. We propose an algorithm called Time-Distance Bloom Filter (TDBF) that takes into account the read time and read distance of RFID tags, which greatly reduces data redundancy. In addition, we have proposed a measurement of the filter performance evaluation indicators. In experiments, we found that the performance score of the TDBF algorithm was 5.2, while the Time Bloom Filter (TBF) score was only 0.03, which indicates that the TDBF algorithm can achieve a lower false negative rate, lower false positive rate, and higher data compression rate. Furthermore, in a dynamic scenario, the TDBF algorithm can filter out valid data according to the actual scenario requirements.

In the field of indoor monitoring, to analyze the track data collected by RFID readers effectively and intuitively is the critical issue. The indoor environment is usually crowded with a large number of flexible and mobile obstacles. The movements of obstacles always change the road network structure. To analyze the trajectories under the labile road network constrains is the key problem. To solve this problem, we propose a trajectory clustering method based on digital-twin technology called DTCluster. The proposed digital-twin model is a virtual digital model, which can extract and display the changing road network structure through real-time mapping and virtual simulation functions. In our paper, the original CFSFDP algorithm is improved to reduce the influence of the track missing points and to enhance the clustering effect in the face of unequal trajectories in length. We also take two parameters: the speed and the direction of moving targets into account. The trajectory clustering results in our DTCluster follow the changing road network for synchronous display on the proposed digital-twin model. It is convenient and effective for researchers to use DTCluster to find the problems which cannot be observed in the clustering results displayed in original planned road network. These problems can only be observed in the current changed road network environment. In our paper, we demonstrate the validity and intuition of DTCluster byexprimental results.

Anomaly analysis of trajectories is one of the means to maintain indoor safety. Effective track anomaly detection should be based on the current road network structure. However, the indoor environment usually contains many obstacles flexible to move. The changes in the positions of obstacles frequently cause the changes of the road network structure. When the road network changes, it takes time and effort to manually redraw the road network and simultaneously losses the guarantee of real-time performance. At the same time, manual drawing is forbidden in some confidential places. In our paper, the scheme of RFID track anomaly detection combined with digital-twin technology is proposed to provide a real-time actual road network for anomaly analysis. The accurate mapping and virtual simulation functions of digital-twin technology are used to achieve the dynamic real-time rendering and the maintenance of the indoor road network structure. We also improve a clustering algorithm to make it suitable for indoor RFID track clustering. In our paper, deviation thresholds and digital-twin model are used to detect anomalies. The trajectory is judged to be abnormal when it appears in the restricted areas or exceeds the deviation thresholds in any terms of position, velocity, or direction. We use an improved scan-line algorithm and F1-scores to determine the threshold values. Our proposed anomaly detection method provides more effective and intuitive results for researchers to analyze.

With the wide application of MIMO-OFDM technology, Channel State Information (CSI) as a fine-grained feature can be extracted from PHY layer with Wi-Fi. Although CSI has a better performance on expressing the spatial and temporal features of wireless signal, it is more sensitive to the multipath reflection. As a result, Line-of-Sight (LoS) identification and corresponding subcarrier parameter estimation play an important role in improving positioning accuracy. In this paper, we propose a complete parameter processing framework, which involves phase calibration, phase ambiguity elimination, subcarrier parameter (amplitude and phase) estimation of LoS, fingerprint feature extraction and relationship mapping from fingerprint feature to position estimate. The experimental results show that, compared with existing algorithm, our proposed algorithm improves the positioning accuracy by 2.3% in LoS and 10.7% in NLoS cases.

Various research works have been proposed for Wi-Fi-based indoor localization, including Received Signal Strength Indicator (RSSI)-based fingerprint algorithm, Angle of Arrival (AoA)-based algorithm and so on. However, since RSSI value cannot accurately express the spatial features of emitted wireless signal, and the interfering noise in indoor environment makes the wireless signal distortion, RSSI-based localization algorithm cannot achieve an ideal accuracy. In this paper, we utilize Channel State Information (CSI) extracted from MIMO-OFDM PHY layer as fingerprint image to express the spatial and temporal features of Wi-Fi signal. At the same time, an indoor localization algorithm is also proposed, which is based on convolutional neural network and dual-factor enhanced variational Bayes adaptive Kalman filter, to achieve accurate position estimate with time-varying measurement noise and process noise in complex indoor environment. According to the simulation results, compared with existing methods, our proposed algorithm improves the positioning accuracy up to 51.8%. In the real indoor environment, our proposed algorithm improves the positioning accuracy up to 22% in LoS scenario, and 9.8% in NLoS scenario, respectively.

In this paper, the L1-norm Linearly Constrained LMS (L1-LC-LMS) algorithm has been proposed. The L1-LC-LMS algorithm imposes the L1-norm restriction on the weights vector. By applying the L1-norm constraint, the weights vector convergence rate has been significantly improved. Furthermore, compared to traditional LC-LMS algorithm, the nulling depth of the L1-LC-LMS is greatly enhanced. The simulation is carried out in the arrays system with noise and other interferences. We have confirmed the effectiveness of the L1-LC-LMS algorithm from theoretical analysis and the experimental results.

In this paper, outage probability is investigated for full-duplex (FD) multi-relay networks with decode-and-forward (DF) protocol. Since FD operation allows node to transmit and receive signals on the same frequency band simultaneously, loop interference (LI) caused by power leakage between transmit and receive antennas is inevitable and deteriorates system performance. We propose five relay selection strategies based on different channel situation information (CSI) and obtain closed-form expressions of outage probability for them. Furthermore, two power allocation algorithms are proposed to optimize outage performance and their closed-form optimal allocation solutions are derived. Numerical simulation is made to facilitate comparison and verify our analysis. By observing simulations, we find that outage performance can be significantly enhanced by power allocation and both proposed allocation algorithms have their pros and cons.

Since radio frequency identification (RFID) technology has been used in various scenarios such as supply chain, access control system and credit card, tremendous efforts have been made to improve the authentication between tags and readers to prevent potential attacks. Though effective in certain circumstances, these existing methods usually require a server to maintain a database of identity related information for every tag, which makes the system vulnerable to the SQL injection attack and not suitable for distributed environment. To address these problems, we now propose a novel blockchain-based mutual authentication security protocol. In this new scheme, there is no need for the trusted third parties to provide security and privacy for the system. Authentication is executed as an unmodifiable transaction based on blockchain rather than database, which applies to distributed RFID systems with high security demand and relatively low real-time requirement. Analysis shows that our protocol is logically correct and can prevent multiple attacks.

At present, there has been a booming interest in utilizing Channel State Information (CSI) extracted from MIMO-OFDM PHY layer to achieve precise indoor localization. Compared with Received Signal Strength Indicator (RSSI), CSI as a fine-grained feature has a better performance on expressing the spatial and temporal features of wireless signal. As a result, CSI is more sensitive to the noise interference and multi-path. In this paper, we present a direct-path based fingerprint extraction algorithm for indoor localization in noisy and multi-path indoor environment. Our proposed algorithm firstly extracts the amplitude and phase measurements of direct-path from the raw CSI, and then calculates the unique fingerprint feature according to the filtered CSI. The experimental results show that our proposed algorithm improves the positioning accuracy up to 23.5% in complex indoor multipath environment.

Radio Frequency Identification (RFID)-based fingerprint indoor positioning and tracking technology is one of the key technologies in the study of wireless sensor network, and has been widely used in noisy environment. However, due to the time and space fluctuation in Received Signal Strength Indicator (RSSI) of RFID, indoor positioning accuracy is not satisfactory. In this work, we present a Multi-Direction Weight Position Kalman Filter (MDWPKF) according to the spacial feature of RSSI. This algorithm combines the Multi-Direction data collection method, with Standard Kalman Filter and fingerprint matching algorithm to achieve the signal fluctuation reduction, noise removal and 2D fingerprint mapping. At the same time, the Improved Position Kalman Filter (IPKF) in our proposed MDWPKF takes the advantages of Gaussian weight computation and velocity estimator to refine the position and velocity estimates. Compared with traditional PKF, the MDWPKF improves the positioning accuracy by 17.7%, and the velocity accuracy by 10.2%. Compared with Fingerprint Kalman Filter (FKF), the MDWPKF can be used for the tracking of both moving target (including position and velocity estimates) and stationary object.