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.

Low earth orbit (LEO) satellite-assisted edge computing is a promising paradigm for providing seamless connectivity to remote areas and enabling reliable services in dense urban scenarios. By establishing virtual line-of-sight (LoS) links and coherently combining reflected signals, the reconfigurable intelligent surface (RIS) can significantly enhance the channel conditions of satellite-terrestrial communication links. This paper integrates the novel simultaneously transmitting and reflecting beyond-diagonal RIS (STAR-BD-RIS) technology into the LEO satellite collaborative edge computing (LSCEC) system, where each ground user equipment (GUE) can simultaneously offload task bits to multiple satellites via a hybrid non-orthogonal multiple access (NOMA) scheme with the aid of STAR-BD-RIS. To minimize the weighted sum energy consumption of GUEs and LEO satellites, the transmit power, CPU frequency, offloading strategy, bandwidth allocation, and STAR-BD-RIS beamforming are jointly optimized by an iterative algorithm based on the successive convex approximation (SCA) technique and the penalty dual decomposition (PDD) method. Simulation results illustrate that: 1) the STAR-BD-RIS scheme achieves lower energy consumption than traditional RIS (T-RIS), simultaneously transmitting and reflecting RIS (STAR-RIS) and beyond-diagonal RIS (BD-RIS) schemes in LSCEC systems; 2) the proposed hybrid NOMA demonstrates superior scalability and performance over both NOMA and orthogonal frequency division multiple access (OFDMA); 3) the proposed satellite collaboration scheme significantly reduces the energy consumption compared to the case without collaboration, while exhibiting diminishing energy-saving gains as the number of satellites increases.

Pinching antenna (PA) is a flexible antenna composed of a waveguide and multiple dielectric particles, which is capable of reconfiguring wireless channels intelligently in line-of-sight links. By leveraging the unique features of PAs, we exploit the uplink (UL) transmission in pinching antenna systems (PASS). To comprehensively evaluate the performance gains of PASS in UL transmissions, three scenarios, multiple PAs for a single user (MPSU), a single PA for a single user (SPSU), and a single PA for multiple users (SPMU) are considered. The positions of PAs are optimized to obtain the maximal channel gains in the considered scenarios. For the MPSU and SPSU scenarios, by applying the optimized position of PAs, closed-form expressions for analytical, asymptotic and approximated ergodic rate are derived. As the further advance, closed-form expressions of approximated ergodic rate is derived when a single PA is fixed in the SPMU scenario. Our results demonstrate the following key insights: i) The proposed PASS significantly outperforms conventional Multiple-input Single-output networks by exploiting the flexibility of PAs; ii) The PA distribution follows an asymmetric non-uniform distribution in the MPSU scenario; iii) Optimizing PA positions significantly enhances the ergodic sum rate performance.

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.

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.

Pinching antenna (PA) is a flexible antenna composed of a waveguide and multiple dielectric particles, which is capable of reconfiguring wireless channels intelligently in line-of-sight links. By leveraging the unique features of PAs, we exploit the uplink (UL) transmission in pinching antenna systems (PASS). To comprehensively evaluate the performance gains of PASS in UL transmissions, multiple PAs are deployed for a single user, namely MPSU. The positions of PAs are optimized to obtain the maximal channel gains in the considered scenarios. For the MPSU and SPSU scenarios, by applying the optimized position of PAs, closed-form expressions for analytical, asymptotic and approximated ergodic rate are derived. Our results demonstrate the following key insights: i) The PA distribution follows an asymmetric non-uniform distribution in the MPSU scenario; ii) Optimizing PA positions significantly enhances the ergodic sum rate performance.

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.

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.

Nonorthogonal multiple access (NOMA) has been recognized as a key enabling techniques for future communication systems since it can take advantage of differences in users’ channel conditions to achieve higher spectral efficiency compared with orthogonal multiple access. However, employing such a capacity-centric framework can result in intragroup co-channel interference for those users whose symbols are intended to be first decoded, potentially causing a decrease in reliability. Therefore, the Alamouti encoding method has been widely used in the multiuser MISO-NOMA broadcast channels to improve reliability. Although the Alamouti-based NOMA paradigm can improve reliability by providing full spatial diversity, it comes at the cost of a significant loss in spectral efficiency compared to the capacity-centric NOMA framework. To overcome these limitations, this article presents a new approach for improving the performance of multiuser MISO-NOMA broadcast channels, in which the diversity-multiplexing tradeoff is leveraged more effectively by providing multiplexing for near users and diversity for far users at the transceiver. The closed-form expressions of ergodic sum-rate and outage probability for the proposed scheme are derived. Simulation results demonstrate that the proposed scheme is capable of performing well in a wide range of scenarios and it surpasses traditional methods in terms of reliability and effectiveness.

With the development of 6G wireless networks, non-fixed monitoring methods utilizing drone networks as monitoring carriers have been receiving increasing attention. However, due to significant environmental interference affecting the hovering of drones in the air, the video signals often contain substantial random interference, which impacts the performance of current multi-object and multi-camera tracking (MOMCT) algorithms. To address this issue, this paper proposes a digital-twin-driven multi-vehicle multi-drone monitoring system, in which a simulation scenario consistent with the actual environment is created. Furthermore, a large amount of vehicle monitoring video data from multiple drone perspectives with random interference is generated, thus compensating for the shortage of multi-camera vehicle monitoring data, especially data affected by random interference. Additionally, an online tracking framework for MOMCT based on the digital twin system is set up, forming a pipeline that fully utilizes the simulated data generated by the digital-twin system for training and testing. Using this pipeline, we find that a feature detection algorithm combining AttentionNet-CBAM and ResNext101-IBN-A can effectively enhance the ability to identify target features, thereby achieving better multi-vehicle tracking results. Experimental results on the CityFlow dataset verify that the proposed method has improved the IDF1 by 1.52% and IDR by 3.58% in comparison with the state-of-the-art online MOMCT methods.

To address the issues of limited computational capability and constrained battery life faced by users in the Internet of Things, wireless-powered mobile edge computing (MEC) has been proposed as a promising solution. However, the efficiency of its key functions, namely task offloading and energy transfer, can be significantly impaired if the direct links between the access point (AP) and users are obstructed. Inspired by the potentials of active simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) for achieving full-space coverage and mitigating multiplicative fading effects, this paper investigates the incorporation of active STAR-RIS in wireless-powered MEC. To meet the high data rate requirements in future smart environments, we aim to maximize the total number of completed task bits. To address the formulated challenging non-convex problem, a resource allocation and active beamforming algorithm (RAABA) is first proposed for a basic two-user non-orthogonal multiple access (NOMA) scenario, jointly optimizing the energy transfer time, decoding order, transmit power, CPU frequency of users, and beamforming of STAR-RIS. We then extend the RAABA to general multi-user scenarios (RAABAM) by leveraging a matching-theory-based user pairing algorithm. Furthermore, a low-complexity RAABAM (L-RAABAM) is proposed by simplifying the matching process and deriving a closed-form expression for the optimal transmit power of users. Simulation results show that: i) by jointly optimizing multiple highly-coupled variables, our proposed RAABAM and L-RAABAM schemes achieve a higher total number of completed task bits; ii) the active STAR-RIS significantly outperforms passive/active traditional RIS and passive STAR-RIS; iii) the deployment rules for active STAR-RIS differ from those for passive STAR-RIS in wireless-powered MEC, where the optimal deployment location of active STAR-RIS depends on the number of its elements.

In this paper, we present the strengths of WiFi-enhanced SLAM over traditional SLAM systems. We minimize the Root Mean Square Error (RMSE) in localization and mapping by incorporating WiFi signal information. The work elegantly embeds WiFi signals in the well-studied SLAM optimization architecture without degrading the efficiency on the computational side, providing improved accuracy and reliability. We utilize a deep neural network (DNN) to learn the optimal fusion of WiFi and traditional sensors, which enhances system performance significantly. The presented LoC-WiFi-SLAM system is of moderate computational as well as spatial complexity. Simulation results show that the Loc-WiFi-SLAM system is able to use available WiFi infrastructure to complement traditional sensors, thus achieving an improvement in mapping and location accuracy. It not only increases localization accuracy but also makes it suitable for robotic systems, thus providing a robust and scalable solution for environments.

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.

The rapid development of next-generation Internet of Things (IoT) applications, including integrated-sensing-and-communication (ISAC), smart grids, smart cities, intelligent transport networks, etc., enables at least tens of billions of bandwidth-thirsty IoT devices, which consume a deluge of data in the sixth-generation (6G) communication systems. In addition, future challenging heterogeneous services and applications, such as Industry 4.0, require the provisioning of unprecedented massive device access, heterogeneous data traffic, high spectral efficiency, and low latency. As a result, there is an urgent demand to pay more attention to IoT networks for high-reliable and low-delay massive access.

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.

The convergence of Blockchain and Internet of Things (BIoT) is fully considered as a paradigm for mitigating threats related to the trust, security, and privacy of Internet of Things (IoT) data. However, because the bandwidth across nodes and time varies in practical IoT networks, it is difficult for existing BIoT mechanisms guarantee blockchain consensus performances. The consensus time could become long owing to low-bandwidth nodes taking longer to download blocks than high-bandwidth nodes. Conventional wisdom holds that removing low-bandwidth nodes can decrease the consensus time, but the nodes could have high-bandwidth at another time owing to bandwidth variability; thus, kicking which nodes out of the consensus is a great challenge. In this article, a novel lightweight BIoT convergence (namely, E-Chain) is proposed to overcome bandwidth variability. The E-Chain first decouples the blockchain into on-chain validating and off-chain voting components. In the off-chain voting part, each node incurs a one-bit communication overhead for voting on a block based on a reputation index. This voting component does not need to download the full content of the block, and is therefore not affected by bandwidth variability. The reputation index was formulated using a rating algorithm with multidimensional IoT network metrics. In addition, the voting mechanism is secure and can still reach the correct consensus when suffering from byzantine attacks. By contrast, a block is validated and stored in a dispersed manner in the on-chain validating part. The E-Chain performances were then evaluated and compared with state-of-the-art mechanisms. Experimental results show that the E-Chain mechanism can significantly decrease both the consensus time and memory resources, and incur an acceptable memory overhead for resource-constrained IoT nodes.

This paper investigates the performance of active reconfigurable intelligent surface (ARIS) assisted non-orthogonal multiple access (NOMA) networks over cascaded Nakagami-m fading channels. The effects of hardware impairments (HIS) and the number of reflection elements on ARIS-NOMA networks with imperfect successive interference cancellation (ipSIC) and perfect successive interference cancellation (pSIC) are considered. More specifically, we derive the expressions of outage probability with ipSIC/pSIC for ARIS-NOMA-HIS networks. According to the approximated analyses, the diversity orders and high signal-tonoise ratio (SNR) slopes for a pair of non-orthogonal users are attained in detail. The simulation results are presented to verify that the outage behaviors of ARIS-NOMA-HIS networks precede that of ARIS-aided orthogonal multiple access (OMA), passive reconfigurable intelligent surface (PRIS) aided OMA, and other conventional cooperative communication.

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.

In this paper, a novel concept called simultaneously transmitting and reflecting RIS (STAR-RIS) is introduced into the wireless-powered mobile edge computing (MEC) systems to improve the efficiency of energy transfer and task offloading. Compared with traditional reflecting-only RIS, STAR-RIS extends the half-space coverage to full-space coverage by simultaneously transmitting and reflecting incident signals, and also provides new degrees-of-freedom (DoFs) for manipulating signal propagation. We aim to maximize the total computation rate of all users, where the energy transfer time, transmit power and CPU frequencies of users, and the configuration design of STAR-RIS are jointly optimized. Considering the characteristics of STAR-RIS, three operating protocols, namely energy splitting (ES), mode switching (MS), and time splitting (TS) are studied, respectively. For the ES protocol, based on the penalty method, successive convex approximation (SCA), and the linear search method, an iterative algorithm is proposed to solve the formulated non-convex problem. Then, the proposed algorithm for ES protocol is extended to solve the MS and TS problems. Simulation results illustrate that the STAR-RIS outperforms traditional reflecting/transmitting-only RIS. More importantly, the TS protocol can achieve the largest computation rate among the three operating protocols of STAR-RIS.

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.

The wireless-powered mobile edge computing (MEC) has been considered as a promising approach to enhance the computation capability and prolong the lifetime of user equipments (DEs). To further improve the efficiency of energy transfer and task offloading, in this paper, a novel concept called simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is introduced into the wireless-powered MEC. The total computation rate maximization problem is investigated by jointly optimizing the energy transfer time, UEs' resource allocation, and configuration design of STAR-RIS via a linear search algorithm based on the block coordinate decent (BCD) and successive convex approximation (SCA) techniques. Simulation results illustrate that our proposed algorithm can achieve a higher computation rate than the scheme with conventional RIS and that without local computing.

Global Navigation Satellite System (GNSS) is widely applied in the next generation wireless networks, where high-precision positioning is required. However, due to the impact of blockages, the high-precision positioning signals are blocked in the ultra-dense networks. In this article, we introduce a RIS-aided GNSS network, where more navigation links can be reflected to the users located at the city center. In order to evaluate the performance of the network, we analyze the results of multi-point location respectively. We also analyze the skyplot and dilution of precision (DoP) of the RIS-aided GNSS network. The numerical results show that: 1) The RIS assisted GNSS network is able to successfully reflect the navigation signals from the occluding satellite and realize the location calculation in the city center. 2) The location of RIS affects the positioning accuracy, and the appropriate RIS location is useful to improve the positioning accuracy.

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.

This paper conceive a large intelligent surface (LIS)aided multiple-input multiple-output network for providing wireless services to randomly roaming users. The network performance is analyzed by utilizing stochastic geometry tools. We aim for serving multiple users by jointly designing the passive beamforming weight at LISs and detection weight vectors at users. As a benefit, the interference imposed by the LISs can be suppressed. In an effort to evaluate the performance of the proposed network, we first derive approximated channel statistics for characterizing the effective channel gains. Then, we derive closed-form expressions for the ergodic rate of users. For gleaning further insights, we investigate the high-signal-to-noise-ratio (SNR) of ergodic rate. Our analytical results demonstrate that the specific fading environments encountered between the LISs and users have almost no impact on the ergodic rate attained.

This paper investigates a massive non-orthogonal multiple access (NOMA) enhanced Internet of Things (IoT) network. In order to provide massive connectivity, a novel cluster strategy is proposed, where massive devices can be served simultaneously. New channel statistics are derived. The exact and the asymptotic expressions in terms of coverage probability are derived. In order to obtain further engineering insights, short-packet communication scenarios are investigated. From our analysis, we show that the performance of NOMA enhanced IoT networks is capable of outperforming orthogonal multiple access (OMA) enhanced IoT networks.

This article investigates a nonorthogonal multiple access (NOMA)-enhanced Internet of Things (IoT) network. In order to provide connectivity, a novel cluster strategy is proposed, where multiple devices can be served simultaneously. Two potential scenarios are investigated: 1) NOMA-enhanced terrestrial IoT networks and 2) NOMA-enhanced aerial IoT networks. We utilize stochastic geometry tools to model the spatial randomness of both terrestrial and aerial devices. New channel statistics are derived for both terrestrial and aerial devices. The exact and the asymptotic expressions in terms of coverage probability are derived. In order to obtain further engineering insights, short-packet communication scenarios are investigated. From our analysis, we show that the performance of NOMA-enhanced IoT networks is capable of outperforming OMA-enhanced IoT networks. Moreover, based on simulation results, there exists an optimal value of the transmit power that maximizes the coverage probability.

Reconfigurable intelligent surfaces (RISs) constitute a promising performance enhancement for next-generation (NG) wireless networks in terms of enhancing both their spectral efficiency (SE) and energy efficiency (EE). We conceive a system for serving paired power-domain non-orthogonal multiple access (NOMA) users by designing the passive beamforming weights at the RISs. In an effort to evaluate the network performance, we first derive the best-case and worst-case of new channel statistics for characterizing the effective channel gains. Then, we derive the best-case and worst-case of our closed-form expressions derived both for the outage probability and for the ergodic rate of the prioritized user. For gleaning further insights, we investigate both the diversity orders of the outage probability and the high-signal-to-noise (SNR) slopes of the ergodic rate. We also derive both the SE and EE of the proposed network. Our analytical results demonstrate that the base station (BS)-user links have almost no impact on the diversity orders attained when the number of RISs is high enough. Numerical results are provided for confirming that: i) the high-SNR slope of the RIS-aided network is one; ii) the proposed RIS-aided NOMA network has superior network performance compared to its orthogonal counterpart.

Reconfigurable intelligent surface (RIS) technique stands as a promising signal enhancement or signal cancellation technique for next generation networks. We design a novel passive beamforming weight at RISs in a multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) network for simultaneously serving paired users, where a signal cancellation based (SCB) design is employed. In order to implement the proposed SCB design, we first evaluate the minimal required number of RISs in both the diffuse scattering and anomalous reflector scenarios. Then, new channel statistics are derived for characterizing the effective channel gains. In order to evaluate the network's performance, we derive the closed-form expressions both for the outage probability (OP) and for the ergodic rate (ER). The diversity orders as well as the high-signal-to-noise-ratio (SNR) slopes are derived for engineering insights. Moreover, the network's performance of a finite resolution design has been evaluated. Our analytical results demonstrate that: i) the inter-cluster interference can be eliminated with the aid of large number of RIS elements; ii) the line-of-sight of the BS-RIS and RIS-user links are required for the diffuse scattering scenario, whereas the LoS links are not required for the anomalous reflector scenario.

This paper investigates a novel design for joint radar-communication (JRC) waveform, which is capable of improving the network’s performance by solving the phase shift mutation of the Chirp-BPSK waveform. We aim for jointly designing chirp waveform and improving Binary Phase Shift Keying (BPSK), as well as the hybrid chaotic spread spectrum code (CSSC). As a benefit, the JRC waveform can be successfully decoded, and the accumulation of phase shift can be suppressed by hybrid CSSC. The detailed deduction on the short-time Fourier transform (STFT) of the JRC waveform is adopted to analyse the complicated AF characters. Our analytical results demonstrate that the proposed JRC signal has superior spectrum performance, ambiguity function (AF), and transmission rate than conventional Chirp and Chirp-BPSK waveform. Numerical results are provided to confirm that (i) the proposed waveform is capable of providing better communication and radar performance; (ii) the performance of AF is not affected by the transmission rate for the proposed waveform.

This paper investigates the non-orthogonal multiple access (NOMA) enhanced Air-to-Everything (A2X) frameworks. A novel 3-Dimension unmanned aerial vehicles (UAVs) framework for providing wireless services to randomly roaming NOMA receivers (Rxs) in the sphere space is proposed by utilizing stochastic geometry tools. In an effort to evaluate the performance of the proposed framework, we first derive closed-form expressions for the outage probability of paired NOMA Rxs. For obtaining more insights, we investigate the diversity order of NOMA enhanced A2X frameworks. Our analytical results demonstrate that the diversity order and the high SNR slope of the proposed framework are m. Numerical results are provided to confirm that for the case of fixed LoS probability, the outage performance of paired NOMA Rxs mainly depends on users with poor channel conditions.

In this paper, the application of non-orthogonal multiple access (NOMA) aided multiple unmanned aerial vehicles (UAVs) wireless networks is investigated in the downlink scenario. A new multiple UAVs framework, user-centric strategy for providing emergency services in the rural area, is proposed by utilizing a stochastic geometry model. In order to provide practical insights for the proposed NOMA assisted UAV framework, an imperfect successive interference cancelation (ipSIC) scenario is taken into account. For the user-centric strategy, we derive new exact expressions for the coverage probability. The derived analytical results explicitly indicate that the ipSIC coefficient is a dominant component in terms of coverage probability.

This paper advocates a pair of strategies in non-orthogonal multiple access (NOMA) in unmanned aerial vehicles (UAVs) communications, where multiple UAVs play as new aerial communications platforms for serving terrestrial NOMA users. A new multiple UAVs framework with invoking stochastic geometry technique is proposed, in which a pair of practical strategies are considered: 1) the UAV-centric strategy for offloading actions and 2) the user-centric strategy for providing emergency communications. In order to provide practical insights for the proposed NOMA assisted UAV framework, an imperfect successive interference cancelation (ipSIC) scenario is taken into account. For both UAV-centric strategy and user-centric strategy, we derive new exact expressions for the coverage probability. We also derive new analytical results for orthogonal multiple access (OMA) for providing a benchmark scheme. The derived analytical results in both user-centric strategy and UAV-centric strategy explicitly indicate that the ipSIC coefficient is a dominant component in terms of coverage probability. Numerical results are provided to confirm that: 1) for both user-centric strategy and UAV-centric strategy, NOMA assisted UAV cellular networks is capable of outperforming OMA by setting power allocation factors and targeted rate properly and 2) the coverage probability of NOMA assisted UAV cellular framework is affected to a large extent by ipSIC coefficient, target rates, and power allocations factors of paired NOMA users.

This paper investigates the multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) assisted unmanned aerial vehicles (UAVs) networks. By utilizing a stochastic geometry model, a new 3-D UAV framework for providing wireless service to randomly roaming NOMA users has been proposed. In an effort to evaluate the performance of the proposed framework, we derive analytical expressions for the outage probability and the ergodic rate of MIMO-NOMA enhanced UAV networks. We examine tractable upper bounds for the whole proposed framework, with deriving asymptotic results for scenarios that transmit power of interference sources being proportional or being fixed to the UAV. For obtaining more insights for the proposed framework, we investigate the diversity order and high signal-to-noise slope of MIMO-NOMA assisted UAV networks. Our results confirm that: 1) Outage probability of NOMA enhanced UAV networks is affected to a large extent by the targeted transmission rates and power allocation factors of NOMA users and 2) For the case that the interference power is proportional to the UAV power, there are error floors for the outage probabilities.