Search Results (360)

WiFi channel state information (CSI) has emerged as a promising modality for human action recognition due to its non-invasive nature and robustness in diverse environments. However, most existing methods process CSI channels collectively, potentially overlooking valuable channel-specific information. In this study, we propose a novel architecture, DF-CNN, which treats CSI channels separately and integrates their outputs using a decision fusion (DF) strategy. Extensive experiments demonstrate that DF-CNN significantly outperforms traditional approaches, achieving state-of-the-art performance. We also provide a comprehensive analysis of individual and combined CSI channel evaluations, showcasing the effectiveness of our method. This work establishes the importance of separate channel processing in CSI-based human action recognition and sets a new benchmark for the field. Full article

Wi-Fi-based human activity recognition (HAR) is a non-intrusive and privacy-preserving method that leverages Channel State Information (CSI) for identifying human activities. However, existing approaches often struggle with robust feature extraction, especially in dynamic and multi-environment scenarios, and fail to effectively integrate amplitude and phase features of CSI. This study proposes a novel model, the Phase–Amplitude Channel State Information Network (PA-CSI), to address these challenges. The model introduces two key innovations: (1) a dual-feature approach combining amplitude and phase features for enhanced robustness, and (2) an attention-enhanced feature fusion mechanism incorporating multi-scale convolutional layers and Gated Residual Networks (GRN) to optimize feature extraction. Experimental results demonstrate that the proposed model achieves state-of-the-art performance on three datasets, including StanWiFi (99.9%), MultiEnv (98.0%), and the MINE lab dataset (99.9%). These findings underscore the potential of the PA-CSI model to advance Wi-Fi-based HAR in real-world applications. Full article

With the rapid advancement of communication technologies, wireless networks have not only transformed people’s lifestyles but also spurred the development of numerous emerging applications and services. Against this backdrop, research on Wi-Fi-based human activity recognition (HAR) has become a hot topic in both academia and industry. Channel State Information (CSI) contains rich spatiotemporal information. However, existing deep learning methods for human activity recognition (HAR) typically focus on either temporal or spatial features. While some approaches do combine both types of features, they often emphasize temporal sequences and underutilize spatial information. In contrast, this paper proposes an enhanced approach by modifying residual networks (ResNet) instead of using simple CNN. This modification allows for effective spatial feature extraction while preserving temporal information. The extracted spatial features are then fed into a modifying GRU model for temporal sequence learning. Our model achieves an accuracy of 99.4% on the UT_HAR dataset and 99.24% on the NTU-FI HAR dataset. Compared to other existing models, RGANet shows improvements of 1.21% on the UT_HAR dataset and 0.38% on the NTU-FI HAR dataset. Full article

Channel prediction is an effective technology to support adaptive transmission in wireless communication. To solve the difficulty of accurately predicting channel state information (CSI) due to fast time-varying characteristics, a next-generation reservoir calculation network (NGRCN) is combined with CSI, and a channel prediction method for OFDM wireless communication systems based on an adaptive reinforced reservoir learning network (adaptive RRLN) is proposed. An adaptive elastic network (adaptive EN) is used to estimate the output weight matrix to avoid ill-conditioned solutions. Therefore, the adaptive RRLN has echo and oracle properties. In addition, an adaptive singular spectral analysis (adaptive SSA) method is proposed to improve the local predictability of CSI by decomposing and reconstructing CSI to improve the fitting accuracy of the channel prediction model. In the simulation section, the OFDM wireless communication systems are constructed using IEEE802.11ah and the one-step prediction, the multi-step prediction, and the robustness test are implemented and analyzed. The simulation results show that the prediction accuracy of the adaptive RRLN can reach 3 × 10⁻⁵ and 8.36 × 10⁻⁶, which offers satisfactory prediction performance and robustness. Full article

In modern (5G) and future Multi-User (MU) wireless communication systems Beyond 5G (B5G) using Multiple-Input Multiple-Output (MIMO) technology, base stations with a large number of antennas communicate with many mobile stations. This technology is becoming especially relevant in modern multi-user wireless sensor networks in various application scenarios. The problem of organizing an MU mode on the downlink has arisen, which can be solved by precoding at the Base Station (BS) without using additional channel frequency–time resources. In order to utilize an efficient precoding algorithm at the base station, full Channel State Information (CSI) is needed for each mobile station. Transmitting this information for massive MIMO systems normally requires the allocation of high-speed channel resources for the feedback. With limited feedback, reduced information (partial CSI) is used, for example, the codeword from the codebook that is closest to the estimated channel vector (or matrix). Incomplete (or inaccurate) CSI causes interference from the signals, transmitted to neighboring mobile stations, that ultimately results in a decrease in the number of active users served. In this paper, we propose a new downlink precoding approach for MU-MIMO systems that also uses codebooks to reduce the information transmitted over a feedback channel. A key aspect of the proposed approach, in contrast to the existing ones, is the transmission of new, uncorrelated information in each cycle, which allows for accumulating CSI with higher accuracy without increasing the feedback overhead. The proposed approach is most effective in systems with dynamic user selection. In such systems, increasing the accuracy of CSI leads to an increase in the number of active users served, which after a few cycles, can reach a maximum value determined by the number of transmit antennas at the BS side. This approach appears to be promising for addressing the challenges associated with current and future massive MIMO systems, as evidenced by our statistical simulation results. Various methods for extracting and transmitting such uncorrelated information over a feedback channel are considered. In many known publications, the precoder, codebooks, CSI estimation methods and other aspects of CSI transmission over a feedback channel are separately optimized, but a comprehensive approach to jointly solving these problems has not yet been developed. In our paper, we propose to fill this gap by combining a new approach of precoding and CSI estimation with CSI accumulation and transmission over a feedback channel. Full article

The Enhanced 911 (E911) mandate of the Federal Communications Commission (FCC) drives the evolution of indoor three-dimensional (3D) location/positioning services for emergency calls. Many indoor localization systems exploit location-dependent wireless signaling signatures, often called fingerprints, and machine learning techniques for position estimation. In particular, received signal strength indicators (RSSIs) and Channel State Information (CSI) in Wireless Local Area Networks (WLANs or Wi-Fi) have gained popularity and have been addressed in the literature. While RSSI signatures are easy to collect, the fluctuation of wireless signals resulting from environmental uncertainties leads to considerable variations in RSSIs, which poses a challenge to accurate localization on a single floor, not to mention multi-floor or even three-dimensional (3D) indoor localization. Considering recent E911 mandate attention to vertical location accuracy, this study aimed to investigate CSI from Wi-Fi signals to produce baseline Z-axis location data, which has not been thoroughly addressed. To that end, we utilized CSI measurements and two representative machine learning methods, an artificial neural network (ANN) and convolutional neural network (CNN), to estimate both 3D and vertical-axis positioning feasibility to achieve E911 accuracy compliance. Full article

The increasing dependence on Wi-Fi for device-to-device communication in industrial environments has introduced significant security and privacy challenges. In such wireless networks, rogue access point (RAP) attacks have become more common, exploiting the openness of wireless communication to intercept sensitive operational data, compromise privacy, and disrupt industrial processes. Existing mitigation schemes often rely on dedicated hardware and cryptographic methods for authentication, which are computationally expensive and impractical for the diverse and resource-limited devices commonly found in industrial networks. To address these challenges, this paper introduces a robust and lightweight Wi-Fi device identification scheme, named the P-A scheme, specifically designed for industrial settings. By extracting hardware fingerprints from the phase and amplitude characteristics of channel state information (CSI), the P-A scheme offers an efficient and scalable solution for identifying devices and detecting rogue access points. A lightweight neural network ensures fast and accurate identification, making the scheme suitable for real-time industrial applications. Extensive experiments in real-world scenarios demonstrate the effectiveness of the scheme, achieving 95% identification accuracy within 0.5 s. The P-A scheme offers a practical pathway to safeguard data integrity and privacy in complex industrial networks against evolving cyber threats. Full article

With the growing prevalence of large-scale intelligent surveillance camera systems, the burden on real-time video analytics pipelines has significantly increased due to continuous video transmission from numerous cameras. To mitigate this strain, recent approaches focus on filtering irrelevant video frames early in the pipeline, at the camera or edge device level. In this paper, we propose Wi-Filter, an innovative filtering method that leverages Wi-Fi signals from wireless edge devices, such as Wi-Fi-enabled cameras, to optimize filtering decisions dynamically. Wi-Filter utilizes channel state information (CSI) readily available from these wireless cameras to detect human motion within the field of view, adjusting the filtering threshold accordingly. The motion-sensing models in Wi-Filter (Wi-Fi assisted Filter) are trained using a self-supervised approach, where CSI data are automatically annotated via synchronized camera feeds. We demonstrate the effectiveness of Wi-Filter through real-world experiments and prototype implementation. Wi-Filter achieves motion detection accuracy exceeding 97.2% and reduces false positive rates by up to 60% while maintaining a high detection rate, even in challenging environments, showing its potential to enhance the efficiency of video analytics pipelines. Full article

The complexity and uncertainty of the marine environment pose significant challenges to the stability and coverage of communication links. Due to the limited coverage range of traditional onshore base stations (BSs) in marine environments, relay technology has become an essential approach to extending communication coverage. However, the rapid variations in marine wireless channels and the complexity of hydrological conditions make it extremely difficult to obtain accurate channel state information (CSI). In particular, dynamic environmental factors such as waves, tides and wind speed cause channel parameters to fluctuate significantly over time. To address these challenges, this paper proposes a cooperative communication strategy based on ships and designs a novel channel modeling method to accurately capture the characteristics of marine wireless channels. Furthermore, a deep learning-based optimization scheme is proposed, which formulates the relay selection problem as a spatiotemporal classification task. By integrating the spatial positions of candidate relays and their communication conditions, the proposed scheme enables real-time selection of the optimal relay while evaluating link connectivity probabilities under hydrological influences. Simulation results confirm that the proposed method achieves high accuracy even in rapidly changing marine environments. Full article

This paper presents an integrated approach to developing lightweight, high-performance deep learning models for human activity recognition (HAR) using WiFi Channel State Information (CSI). Motivated by the need for accuracy and efficiency in resource-constrained environments, we combine Bayesian Optimization-based Neural Architecture Search (NAS) with a structured pruning algorithm. NAS identifies optimal network configurations, while pruning systematically removes redundant parameters, preserving accuracy. This approach allows for robust activity recognition from diverse WiFi datasets under varying conditions. Experimental results across multiple benchmark datasets demonstrate that our method not only maintains but often improves accuracy after pruning, resulting in models that are both smaller and more accurate. This offers a scalable and adaptable solution for real-world deployments in IoT and mobile platforms, achieving an optimal balance of efficiency and accuracy in HAR using WiFi CSI. Full article

The Low Earth Orbit (LEO) small satellites are extensively used for global connectivity to enable services in underpopulated, remote or underdeveloped areas. Their inherent broadcast nature exposes LEO–terrestrial communication links to severe security threats, which always reveal new challenges. The secrecy performance of the satellite-to-ground user link in the presence of a ground eavesdropper is studied in this paper. We observe both scenarios of the eavesdropper’s channel state information (CSI) being known or unknown to the satellite. Throughout the analysis, we consider that locations of the intended and unauthorized user are both arbitrary in the satellite’s footprint. On the other hand, we analyze the case when the user is in the center of the satellite’s central beam. In order to achieve realistic physical layer security features of the system, the satellite channels are assumed to undergo Gamma-shadowed Ricean fading, where both line-of-site and scattering components are influenced by shadowing effect. In addition, some practical effects, such as satellite multi-beam pattern and free space loss, are considered in the analysis. Capitalizing on the aforementioned scenarios, we derive the novel analytical expressions for the average secrecy capacity, secrecy outage probability, probability of non-zero secrecy capacity, and probability of intercept events in the form of Meijer’s G functions. In addition, novel asymptotic expressions are derived from previously mentioned metrics. Numerical results are presented to illustrate the effects of beam radius, satellite altitude, receivers’ position, as well as the interplay of the fading or/and shadowing impacts over main and wiretap channels on the system security. Analytical results are confirmed by Monte Carlo simulations. Full article

A communication network integrating multiple modes can effectively support the sustainable development of next-generation wireless communications. Integrated sensing, communication, and power transfer (ISCPT) represents an emerging technological paradigm that not only facilitates information transmission but also enables environmental sensing and wireless power transfer. To achieve optimal beamforming in transmission, it is crucial to satisfy multiple constraints, including quality of service (QoS), radar sensing accuracy, and power transfer efficiency, while ensuring fundamental system performance. The presence of multiple parametric constraints makes the problem a non-convex optimization challenge, underscoring the need for a solution that balances low computational complexity with high precision. Additionally, the accuracy of channel state information (CSI) is pivotal in determining the achievable rate, as imperfect or incomplete CSI can significantly degrade system performance and beamforming efficiency. Deep reinforcement learning (DRL), a machine learning technique where an agent learns by interacting with its environment, offers a promising approach that can dynamically optimize system performance through adaptive decision-making strategies. In this paper, we propose a DRL-based ISCPT framework, which effectively manages complex environmental states and continuously adjusts variables related to sensing, communication, and energy harvesting to enhance overall system efficiency and reliability. The achievable rate upper bound can be inferred through robust, learnable beamforming in the ISCPT system. Our results demonstrate that DRL-based algorithms significantly improve resource allocation, power management, and information transmission, particularly in dynamic and uncertain environments with imperfect CSI. Full article

With the impending integration of sensing capabilities into new wireless standards such as 6G and 802.11 bf, there is a growing threat to public privacy. Recent studies have revealed that even small-scale activities, like keyboard typing, can be sensed by attackers using Wi-Fi Channel State Information (CSI) as these devices become more common in commercial spaces. This paper aims to model the minimum CSI data rate required to sense activities in the channel and quantifies the detection accuracy of WiFi-based keystroke recognition in relation to the CSI sensing data rate. Our experimental findings using commercial-off-the-shelf hardware suggest that interference can be used as a defence strategy to degrade the CSI data rate and prevent undesirable Wi-Fi sensing attacks. To achieve a reduced data rate, we propose an extension to Bianchi’s model of CSMA/CA systems and establish a new mathematical relationship between channel contention and the available CSI. This proposed relationship was empirically verified, and our contention-based defence strategy was experimentally validated. Experiments show that our contention-based defence strategy increases the chances of evading undesired WiFi-based keystroke recognition by around 70%. By leveraging prior work that shows a degradation in CSI quality with lower transmission rates, we show that conservative interference injection can sufficiently reduce sensing accuracy whilst maintaining channel bandwidth. Full article

WiFi-based human sensing has emerged as a transformative technology for advancing sustainable living environments and promoting well-being by enabling non-intrusive and device-free monitoring of human behaviors. This offers significant potential in applications such as smart homes and sustainable urban spaces and healthcare systems that enhance well-being and patient monitoring. However, current research predominantly addresses single-user scenarios, limiting its applicability in multi-user environments. In this work, we introduce “MultiSenseX”, a cutting-edge system leveraging a multi-label, multi-view Transformer-based architecture to achieve simultaneous localization and activity recognition in multi-occupant settings. By employing advanced preprocessing techniques and utilizing the Transformer’s self-attention mechanism, MultiSenseX effectively learns complex patterns of human activity and location from Channel State Information (CSI) data. This approach transcends traditional sequential methods, enabling accurate and real-time analysis in dynamic, multi-user contexts. Our empirical evaluation demonstrates MultiSenseX’s superior performance in both localization and activity recognition tasks, achieving remarkable accuracy and scalability. By enhancing multi-user sensing technologies, MultiSenseX supports the development of intelligent, efficient, and sustainable communities, contributing to SDG 11 (Sustainable Cities and Communities) and SDG 3 (Good Health and Well-being) through safer, smarter, and more inclusive urban living solutions. Full article

Artificial noise (AN)-aided techniques have been considered to be promising and practical candidates for enhancing physical layer security. However, there has been a lack of analysis regarding the AN effect on the eavesdropper (EV) from the perspective of the signal-to-interference plus noise ratio (SINR) regarding the existence of the EV’s channel state information (CSI) at the legitimate transmitter. In this paper, we analyze the performance of AN-aided secure transmission from the SINR perspective when a legitimate transmitter has and does not have the EV’s CSI. Based on the analyzed EV’s SINRs for the above two cases, the secrecy gap, which is the difference between the two secrecy capacities, is defined and analyzed. Based on the derived secrecy gap, we have analyzed the asymptotic performances of the secrecy capacity and gap when the number of antennas of the legitimate transmitter and the number of antennas of the EV have large values. Through asymptotic analysis, it is demonstrated that the AN-aided secure transmission under the practical environment (i.e., the case that the EV’s CSI is not available at the legitimate transmitter) can nearly achieve an ideal performance (i.e., the performance when the EV’s CSI is available at the legitimate transmitter) in a massive antenna system. Full article