This paper investigates the maximization of energy efficiency in an active simultaneously transmitting and reflecting reconfigurable intelligent surface (ASTARS) empowered non-orthogonal multiple access (NOMA) satellite Internet of Things (IoT) with finite blocklength (FBL) transmission. The activated IoT devices (AIoTDs) in the coverage area can be divided into activated reflection-region IoTDs (ARDs) and activated transmission-region IoTDs (ATDs). Drawing on the capability of ASTARS to efficiently establish cascaded links across full-space, thereby enabling more effective adjustment of uplink signal transmission. The ASTARS can properly amplify signal to mitigate its multiplicative fading and long-distance uplink transmission attenuation, which introduce new degrees of freedom (DoF) into the optimization process. Considering the massive-access requirements of the constrained battery-life IoTDs, two device pairing strategies, namely strong-ARD poor-ATD pairing (SPP) and strong-ARD strong-ATD pairing (SSP), are proposed to enable short-packet data transmission. The penalty alternating iterative algorithm (PAIA) is proposed by jointly optimizing the binary matching coefficient, ASTARS coefficient matrix and AIoTDs transmission power. Simulation results illustrate that 1) the ASTARS can achieve the highest energy efficiency than the passive simultaneously transmitting and reflecting surface (PSTARS) and without reconfigurable intelligent surface (RIS); 2) SPP demonstrates superior performance compared to SSP; 3) an optimal number of ASTARS elements exists for each distinct ASTARS maximum amplification coefficient.

With the increasing carrier frequency in next-generation wireless networks, conventional discrete aperture arrays (DAPA) are unable to fully meet the growing demands of sixth-generation wireless networks. To provide a higher degree of freedom, the continuous-aperture array (CAPA) technique emerges as a promising solution for next-generation networks. In this article, a CAPA-aided network is proposed to deliver access services to multiple users. The transmission power of a two-user pair in the downlink is optimized by using the Fourier transformation to derive tractable results. With the aid of Karush-Kuhn-Tucker (KKT) conditions, closed-form expressions for both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) are derived. Time division multiple access (TDMA) and spatial division multiple access (SDMA) are also considered as OMA schemes. Furthermore, DAPA is included as a benchmark for comparison. Our analytical and numerical results demonstrate the following: i) The proposed KKT-based approach achieves optimal performance in transmission power; ii) The minimal required transmission power for NOMA is lower than that for the OMA benchmark schemes; iii) A performance gap is observed between CAPA and DAPA in both NOMA and OMA, emphasizing the advantages of CAPA-aided networks.

Radar-based gesture recognition and person identification offer a natural, convenient, and privacy-preserving approach to human-computer interaction. However, most existing research focuses predominantly on learning for a single task, which requires separate models for each task. This separation increases the complexity of the deployment and the computational overhead. To address these challenges, this study introduces a multi-task learning framework that simultaneously performs gesture recognition and person identification using a miniature radar sensor. By leveraging radar's capacity to capture finegrained spectral and spatial motion patterns, the framework incorporates micro-Doppler and range-Doppler processing, alongside a multi-branch architecture to enhance modality-specific feature representation. It enables unified learning of shared and task-specific features within a single network architecture. The proposed model, MTxLSTM, integrates CNN and the recent xLSTM to mitigate task interference, improve generalization, and improve gesture recognition through person-specific nuances while enhancing person identification by leveraging contextual gesture information. Experimental results reveal that MTxLSTM outperforms existing multi-task learning frameworks and stateof- the-art models, achieving 99.21% in gesture recognition and 98.59% in person identification with moderate model complexity and inference speed. This study concurrently executes gesture recognition and person identification using a miniature radar sensor, and marking the first application of xLSTM in radar sensing technology.

Radar-based human activity recognition (HAR) has emerged as a critical component in various applications, ranging from smart homes to healthcare monitoring. Radar has several advantages: a wide detection range, a certain penetration ability, non-contact and non-perception detection ability, not being affected by light, and privacy-preserving. However, achieving high accuracy with efficiency remains a significant challenge due to the complexity of radar signals and the variability in human activities. Currently, the majority of research efforts are centered on enhancing performance, often at the expense of computational efficiency. In this paper, we propose RadarAttn, a novel approach that integrates visual attention mechanisms with self-attention to enhance the performance and efficiency of HAR systems. The architecture of RadarAttn can reduce floating-point operations (FLOPs) and parameter counts while improving accuracy. Our method leverages the visual attention mechanism to focus on the most relevant regions of radar spectrograms. Simultaneously, the self-attention mechanism is used to capture long-range dependencies within the radar signal, enabling the model to learn complex patterns associated with different activities. Experimental results on benchmark radar-based HAR datasets demonstrate that RadarAttn significantly outperforms state-of-the-art methods in both accuracy and computational efficiency. Our approach offers a promising direction for developing robust and scalable radar-based HAR systems for real-world applications.

Fluid antenna systems (FAS) are among the most promising technologies for the sixth generation (6G) mobile communication networks. Unlike traditional fixed-position multiple-input multiple-output (MIMO) systems, a FAS possesses position reconfigurability to switch on-demand amongNpredefined ports over a prescribed space. This paper explores the performance of a single-input single-output (SISO) model with a fixed-position antenna transmitter and a single-antenna FAS receiver, referred to as the Rx-SISO-FAS model, under spatially-correlated Rician fading channels. Our contributions include exact expressions and closed-form bounds for the outage probability of the Rx-SISO-FAS model, as well as exact and closed-form lower bounds for the ergodic rate. Importantly, we also analyze the performance considering both uniform linear array (ULA) and uniform planar array (UPA) configurations for the ports of the FAS. To gain insights, we evaluate the diversity order of the proposed model and our analytical results indicate that with a fixed overall system size, increasing the number of ports,N, significantly decreases the outage performance of FAS under different Rician fading factors. Our numerical results further demonstrate that:i) the Rx-SISO-FAS model can enhance performance under spatially-correlated Rician fading channels over the fixed-position antenna counterpart;ii) the Rician factor negatively impacts performance in the low signal-to-noise ratio (SNR) regime;iii) FAS can outperform anLbranches maximum ratio combining (MRC) system under Rician fading channels; andiv) when the number of ports is identical, UPA outperforms ULA.

Fluid antenna systems (FAS) are among the most promising technologies for the sixth generation (6G) mobile communication networks. A FAS possesses position reconfigurability to switch on-demand among$N$predefined ports over a prescribed space. This paper explores the performance of a singleinput single-output (SISO) model with a fixed-position antenna transmitter and a single-antenna FAS receiver, referred to as the Rx-SISO-FAS model, under spatially-correlated Rician fading channels. Our contributions include exact expressions and closedform bounds for the outage probability of the Rx-SISO-FAS model with uniform linear array port configuration. To gain insights, we evaluate the diversity order of the proposed model and our analytical results indicate that with a fixed overall system size, increasing the number of ports,$N$, significantly decreases the outage performance of FAS under different Rician fading factors. Our numerical results further demonstrate that:$i$) the Rx-SISO-FAS model can enhance performance under spatiallycorrelated Rician fading channels over the fixed-position antenna counterpart;$i i)$the Rician factor negatively impacts performance in the low signal-to-noise ratio (SNR) regime.

As the global aging population grows, the need for non-invasive, reliable monitoring solutions for elderly individuals living alone becomes urgent. Kitchen activities, a highrisk area in homes, pose unique safety challenges. However, existing human activity recognition methods still struggle with accuracy, and few studies specifically address these challenges in kitchen environments. This paper introduces OD-GCResNet, a novel hybrid deep learning model for kitchen activity recognition based on micro-Doppler signatures. The proposed model combines Omni-Dimensional Dynamic Convolution with Recursive Gated Convolution to enhance global feature interactions and adaptive attention, allowing for accurate detection of subtle micro-Doppler variations in complex, real-world environments. We validate OD-GCResNet on our collected kitchen activity dataset and achieve a classification accuracy of 99.61 %, outperforming baseline models. This work represents a significant step forward in non-contact, privacy-preserving safety monitoring solutions for elderly care. Our dataset and codes are available at https://github.com/Canberra1111/Kitchen-Micro-Doppler-HAR-with-OD-GCResNet.

This study investigates the application of a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided medium-Earth-orbit (MEO) satellite network for providing both global positioning services and communication services in the urban canyons, where the direct satellite-user links are obstructed. Superposition coding (SC) and successive interference cancellation (SIC) techniques are utilized for the integrated navigation and communication (INAC) networks, and the composed navigation and communication signals are reflected or transmitted to ground users or indoor users located in urban canyons. To meet diverse application needs, navigation-oriented (NO)-INAC and communicationoriented (CO)-INAC have been developed, each tailored according to distinct power allocation factors. We then proposed two algorithms, namely navigation-prioritized-algorithm (NPA) and communication-prioritized-algorithm (CPA), to improve the navigation or communication performance by selecting the satellite with the optimized position dilution of precision (PDoP) or with the best channel gain.

As the landscape of sixth-generation (6G) wireless networks advances, low-Earth-orbit (LEO) satellite communication emerges as a promising solution for offering comprehensive global communication services, though its potential is challenged by severe large-scale path loss that significantly impairs the received channel capacity available to terrestrial users. To address this limitation, this paper investigates a federated learning aided distributed beamforming network for LEO satellite communications, namely the FederSat network. In order to enhance the average achievable rate of the LEO satellite networks, we propose a novel code-book-based distributed beamforming strategy in the FederSat networks. Through numerical analysis, we demonstrate that the proposed FederSat networks substantially outperform in average achievable rate. Additionally, more LEO satellites are encouraged for further enhancing the received signal power, which indicates that a mega-constellation LEO satellite network is preferable.

Simultaneous transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) technique has recently received considerable attention due to its omni-directional radiation capability. In this paper, motivated by the interference-mitigation-based (IMB) and signal-enhancement-based (SEB) designs, we introduce an innovative STAR-RIS assisted simultaneous-signal-enhancement-and-interference-mitigation (SSEIM) design in non-orthogonal multiple access (NOMA) multiple-input single-output cellular communication networks. Our objective is to maximize the system spectral efficiency (SE) by jointly optimizing the reflection and transmission phase shifts at the STAR-RIS, the precoding matrix of BSs, and the power allocation factors of NOMA users. We propose a low-complexity simultaneous enhancement and mitigation algorithm. Furthermore, by exploiting the manifold optimization technique, we introduce the Riemannian conjugate gradient algorithm to solve the non-convex subproblems with unit modulus constraint. Our analysis reveals that the proposed SSEIM design exceeds the traditional RIS-aided SEB and IMB designs.

The increasing quality of service requirements and demand for satellite services necessitate higher data rates, spectral efficiency, and stability in satellite communication networks. Therefore, this paper investigates the non-orthogonal multiple access (NOMA) assisted satellite communication networks, where multiple users are uniformly distributed over a spherical hat according to the homogeneous Poisson point process (HPPP). Based on the characteristics of HPPP, we analyze the distance distributions of users. To evaluate the performance of the proposed networks, we first derive the closed-form expressions and approximated expressions of the outage probability (OP) for paired NOMA users. To obtain more insights into the proposed networks, the ergodic rate and diversity orders for paired NOMA users are also derived. Spectral efficiency is derived for NOMA and orthogonal multiple access (OMA) assisted satellite communication networks. Our analytical results demonstrate that the diversity order of the proposed networks is all one. Numerical results confirm that: 1) compared to OMA, the proposed NOMA-assisted satellite network demonstrates superior outage performance and spectral efficiency, especially in the higher power regimes; 2) fading factors have negligible effects on OP and spectral efficiency; and 3) under certain target rates for both near and far users, the outage performance of far users in NOMA-assisted satellite communication networks is superior to that of near users.

Satellite communication is a crucial component for achieving global communications in sixth generation (6G). Due to the large distance between the satellite and the terrestrial users, the multiplicative fading effects of traditional passive simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) are more severe in satellite communication. Recently, active simultaneous transmitting and reflecting surfaces (ASTARSs) have been proposed to mitigate multiplicative fading by amplifying the incident signal. Motivated by the above, a novel ASTARS-aided non-orthogonal multiple access (NOMA) satellite communication network is proposed in this paper, which includes one low earth orbit (LEO) satellite, one ASTARS and several far-field users (FFUs) and near-field users (NFUs). We propose an adaptive NOMA pairing strategy that allows the satellite to flexibly select pairing schemes. Depending on the users’ quality of service requirements, either the NFU-FFU (NF) pairing scheme or the NFU-NFU (NN) pairing scheme can be used. Then, we formulate an optimization problem to maximize the channel capacity. A two-layer optimization algorithm is proposed, where the outer layer selects the NOMA user pairing schemes, while the inner layer alternately optimizes the NOMA power allocation factors, the satellite precoding matrix and the ASTARS phase shift matrices. To solve the non-convex optimization problem, successive convex approximation and a penalty-based method are employed. The numerical results reveal: 1) The channel capacity of the NF pairing scheme is always higher than that of the NN pairing scheme; 2) Compared to the “Without RIS" and “Passive STARS" schemes, ASTARS can significantly improve the channel capacity of satellite communication.

Radar sensing is gaining increasing attention due to its unique advantages, including being device-free, privacy-preserving, and capable of penetrating obstacles. It has been extensively studied in various applications such as human activity recognition, vital sign monitoring, and person identification. However, most existing research focuses on a single specific application, and there remains a lack of studies or datasets dedicated to multi-task radar sensing. In this paper, we collected a dataset for two sensing tasks, including gesture recognition and person identification, via a miniature mm-wave radar. The raw radar signals were processed using micro-Doppler and range-Doppler techniques to extract spectral and spatial representations. We propose an improved multi-task radar sensing framework (MT-DualFormer) that incorporates attention-based cross-task feature distillation and contrastive learning to maximize task performance. MT-DualFormer consists of dual branches with CNN and Transformer modules, capturing both spatial and temporal dependencies in radar data. Attention-based cross-task feature distillation enables knowledge transfer between gesture recognition and person identification tasks. Meanwhile, contrastive learning ensures embedding space separability, facilitating robust task-specific classification. In the evaluation, MT-DualFormer achieves accuracy rates of 98.87% for gesture recognition and 97.96% for person identification, surpassing five representative multi-task approaches and ten state-of-the-art models. This study underscores the importance of leveraging task correlations to enhance the performance of radar-based sensing systems.

Current research in human activity recognition primarily emphasizes enhancing accuracy, with limited exploration into computational efficiency and hardware compatibility. Recently, Mamba has sparked substantial interest within the realm of deep learning. Mamba is a hardware-aware algorithm enabling very efficient training and inference. Researchers are applying Mamba to various tasks, demonstrating significant promise in both language and vision tasks. It is worthwhile to investigate the use of Mamba for efficient human activity recognition. In this paper, we proposed a hybrid neural network that integrates CNN and visual Mamba, called ActivityMamba. The SE-Mamba block in ActivityMamba utilizes both CNN’s local and Mamba’s global context modeling while keeping computation and memory efficiency. We evaluated the ActivityMamba on five public benchmark datasets collected by using three different sensing techniques. ActivityMamba achieved higher performance than vision transformers, vision Mamba, and CNNs with fewer FLOPs and parameters. It sets a new SOTA on all five datasets, which are 91.78% OA and 89.13% F1 on the USC-HAD dataset, 99.19% OA and 98.64% F1 on the UT-HAR dataset, 99.82% OA and F1 on the DIAT dataset, 98.59% OA and 98.65% F1 on the UCI-HAR dataset, and 95.41% OA and 93.14% F1 on the UniMib dataset. Our work is the first to investigate the CNN-Mamba hybrid network for efficient human activity recognition.

Transformer architectures are popularized in both vision and natural language processing tasks, and they have achieved new performance benchmarks because of their long-term dependencies modeling, efficient parallel processing, and increased model capacity. While transformers offer powerful capabilities, their demanding computational requirements clash with the real-time and energy-efficient needs of edge-oriented human activity recognition. It is necessary to compress the transformer to reduce its memory consumption and accelerate the inference. In this paper, we investigated the binarization of a transformer-DeepViT for efficient human activity recognition. For feeding sensor signals into DeepViT, we first processed sensor signals to spectrograms by using wavelet transform. Then we applied three methods to binarize DeepViT and evaluated it on three public benchmark datasets for sensor-based human activity recognition. Compared to the full-precision DeepViT, the fully binarized one (Bi-DeepViT) reduced about 96.7% model size and 99% BOPs (Bit Operations) with only a little accuracy compromised. Furthermore, we explored the effects of binarizing various components and latent binarization of DeepViT to understand their impact on the model. We also validated the performance of Bi-DeepViTs on two wireless sensing datasets. The result shows that a certain partial binarization can improve the performance of DeepViT. Our work is the first to apply a binarized transformer in HAR.

An animated short film about departure.

Integrated sensing and communication technologies provide essential sensing capabilities that address pressing challenges in remote health monitoring systems. However, most of today’s systems remain obtrusive, requiring users to wear devices, interfering with people’s daily activities, and often raising privacy concerns. Herein, we present HealthDAR, a low-cost, contactless, and easy-to-deploy health monitoring system. Specifically, HealthDAR encompasses three interventions: i) Symptom Early Detection (monitoring of vital signs and cough detection), ii) Tracking & Social Distancing, and iii) Preventive Measures (monitoring of daily activities such as face-touching and hand-washing). HealthDAR has three key components: (1) A low-cost, low-energy, and compact integrated radar system, (2) A simultaneous signal processing combined deep learning (SSPDL) network for cough detection, and (3) A deep learning method for the classification of daily activities. Through performance tests involving multiple subjects across uncontrolled environments, we demonstrate HealthDAR’s practical utility for health monitoring.

Fall detection and recognition play a crucial role in enabling timely medical interventions for people who are at risk of falls, especially among vulnerable populations like older adults and those with mobility limitations. In this article, a cost-effective integrated sensing and communication system, namely, FallDR, is presented for fall detection and recognition using ultrawideband communication. First, we collected the time of flight information of falls (four types) and nonfall events by 10 participants using FallDR. We then proposed a convolutional neural network incorporated with squeeze-and-excitation blocks to detect and recognize falls based on fall trajectories. It proves that the proposed model is accurate, energy-efficient, and lightweight to achieve 100% accuracy in fall detection and recognition. Our proposed solution is proven to be highly robust against environmental changes, such as interference, distance, and direction changes. Further tests in an office showed that FallDR could achieve nearly 100% accuracy, even when the environment was changed. FallDR efficiently employs the characteristics of fall trajectory and the advanced modeling ability of the neural network. We have published our archived data sets and code for comparisons and improvements.

Human activity recognition (HAR) has become a research hotspot because of its wide range of application prospects. It has higher requirements for real-time and powerefficient processing. However, a large amount of data transfer between sensors and servers, and computation-intensive recognition models hinder the implementation of real-time HAR systems. Recently, edge computing has been proposed to address this challenge by moving computational and data storage resources to the sensors, rather than depending on a centralized server/cloud. In this paper, we investigated binary neural networks for edge intelligence-enabled HAR using radar point cloud. Point cloud can provide 3-dimensional spatial information, which is helpful to improve recognition accuracy. Time-series point cloud also brings challenges, such as larger data volume, 4-dimensional data processing, and more intensive computation. To tackle these challenges, we adopt the 2-dimensional histograms for point cloud multi-view processing and propose the EdgeActNet, a binary neural network for point cloud-based human activity classification on edge devices. In the evaluation, the EdgeActNet achieved the best results with average accuracies of 97.63% on the MMActivity dataset and 95.03% on the point cloud samples of the DGUHA dataset respectively; and saved 16.9× memory consumption and 11.5× inference time compared to its full-precision version. Our work also is the first to apply 2D histogram-based multi-view representation and BNNs for timeseries point cloud classification.

General matrix multiplication (GEMM) is a ubiqui-tous computing kernel/algorithm for data processing in diverse applications, including artificial intelligence (AI) and deep learning (DL). Recent shift towards edge computing has inspired GEMM architectures based on unary computing, which are predominantly stochastic and rate-coded systems. This paper proposes a novel GEMM architecture based on temporal-coding, called tuGEMM, that performs exact computation. We introduce two variants of tuGEMM, serial and parallel, with distinct area/power-latency trade-offs. Post-synthesis Power-Performance-Area (PPA) in 45 nm CMOS are reported for 2-bit, 4-bit, and 8-bit computations. The designs illustrate significant advantages in area-power efficiency over state-of-the-art stochastic unary systems especially at low precisions, e.g. incurring just 0.03 mm2and 9 mW for 4 bits, and 0.01 mm2and 4 mW for 2 bits. This makes tuGEMM ideal for power constrained mobile and edge devices performing always-on real-time sensory processing.

In order to provide communication services in a more efficient manner, and instead of using low-orbit communication satellites, we investigate an integrated navigation and communication (INAC) network by medium-orbit navigation satellites. In this article, non-orthogonal multiple access (NOMA) is investigated for facilitating the INAC information. In order to improve the received signal power level, we then introduce a reconfigurable intelligent surface (RIS)-aided INAC network. The navigation and communication signals can be reflected to the users located in the city center. Based on the different power allocation factors, navigation-oriented-INAC (NO-INAC) and communication-oriented-INAC (CO-INAC) are proposed to meet different application scenarios. The bit error ratio (BER) is calculated to illustrate the performance of both NO-INAC and CO-INAC. We analyze the results of single-point positions and multi-point positions. The numerical results demonstrate that: 1) The RIS-aided INAC network is able to successfully reflect the navigation and communication signals from the occluding satellite. 2) The proposed RIS-aided INAC network provides a new solution for satellite communications in a more efficient manner.

Automatic fall detection and recognition are challenging problems. In this paper, a novel solution is proposed based on the trajectories of human falls by using the ultra-wideband (UWB) communication system and machine learning methods for fall detection and recognition. Most previous studies of fall detection based on active UWB sensing used electromagnetic signals directly, which may bring problems like radar clutter, signal coupling, multi-path, fading, and interference. Our proposed method only uses human falls trajectories by passive UWB sensing, which achieved fall recognition performance of 93.26% by using the support vector machine with RBF kernel function (SVM-RBF). Compared with previous research, the superiority of this study is that our solution is robust against interference and environmental changes, which means it is reliable for real-world applications. The archived UWB datasets and code have been already published, which may provide the basis for the comparison of techniques and improvements.

In this article, a cost-effective ultrawideband (UWB) communication system for gesture recognition in a smart home environment is proposed, which uses gesture trajectories and a deep learning model. Most previous studies of gesture recognition using the UWB technology used electromagnetic signals directly, which may bring problems, such as radar clutter, signal coupling, multipath, fading, and interference. However, instead of using UWB’s high-frequency pulse signals, the proposed method only uses gesture trajectories by data positioning. To this end, first, a data set of four gesture activities was created. Then, this data set was trained using a convolutional neural network (CNN) integrated with a squeeze-and-excitation (SE) block, namely, the SE-Conv1D model. Finally, the system was prototyped to interact with appliances in practical smart homes. The experimental data was used to demonstrate the superiority of the SE-Conv1D model in comparison with four baselines: 1) support vector machines; 2)$K$-nearest neighbor; 3) random forest; and 4) binarized neural networks. Experimental results show that all collected gesture activities are correctly recognized with an overall accuracy of over 95%, among which the proposed SE-Conv1D model achieves the best accuracy of 99.48%. The proposed system is a complete end-to-end sensing system specifically designed for tracking and recognizing human gestures, which is robust against interference and changes in distance or direction. In addition, the proposed system can tackle the device selection problems for smart homes, which means it is reliable for real-world applications.