Due to the flourishing of Internet of Things (IoT) technology, radar-based human activity recognition (HAR) technology has made significant progress and has become an indispensable research area. Many radar systems use multiple feature maps and handle them directly in image format. However, the generation of multiple forms of feature maps requires heavy computational resources, which makes it impractical for real-world applications. Additionally, many networks fail to fully extract temporal information from the time-Doppler (TD) map during the feature extraction. Therefore, a time-weighted network based on strip pooling (TWN-SP) using the TD map is proposed in this article. The TWN-SP consists of two time-weighted modules based on fire (TWMs-F) and a feature fusion module based on temporal and channel attention (FFM-TCA). Due to the application of depthwise separable convolution (DSC) and placing the feature fusion step at the forefront, the proposed network has fewer parameters. Moreover, a radar dataset named RadSet is constructed, containing TD maps of six daily activities. To validate the performance of the TWN-SP, a ten-fold cross-validation (CV) on the public dataset named Radar848 and a leave-one-subject-out (LOSO) CV on the RadSet dataset are carried out, respectively. To further validate the generalization performance of TWN-SP, a comparative analysis was conducted on another public dataset, Ci4R. The TWN-SP achieves an accuracy of 99.28% on the RadSet dataset. Experimental results indicate that the TWN-SP surpasses the current leading networks in both performance and complexity.

As the Internet of Things (IoT) technology advances, human activity recognition (HAR) using IoT devices, including wearable sensors has become prevalent in various applications. Nevertheless, many sensor-based HAR methods still struggle to balance recognition accuracy with network complexity. Meanwhile, most existing sensor-based HAR networks fail to achieve an effective fusion of multidimensional features. To address the above issues, a hierarchical multiscale time-frequency and channel feature adaptive fusion (HMTF-CFAF) network is put forward. The HMTF-CFAF efficiently extracts unique multiscale time-frequency and channel features in sensor data using hierarchical connectivity. Furthermore, it incorporates a feature fusion mechanism to integrate and exchange multiscale and multidimensional features, providing more comprehensive and richer features. To evaluate the HMTF-CFAF network, we utilize three datasets: 1) the University of California Irvine HAR (UCI-HAR); 2) physical activity monitoring for aging people (PAMAP2); and 3) self-collected household behavior (HB) dataset. The HMTF-CFAF network achieves the accuracies of 97.66%, 98.75%, and 98.80% on the above three datasets, respectively, demonstrating its excellent performance.

Abstract The challenge of ensuring reliability for high‐efficiency technology, multiple‐input‐multiple‐output orthogonal‐frequency‐division‐multiplexing (MIMO‐OFDM), in wireless frequency‐selective fading environments persists. In this article, the concept of spreading a symbol's energy is proposed as a viable solution to enhance transmission reliability for MIMO‐OFDM systems. And an energy‐spreading‐transform (EST)‐based MIMO‐OFDM transceiver is developed. Following the Inverse Fast Fourier Transform (IFFT) performed by the conventional MIMO‐OFDM transmitter, an orthogonal transformation called the EST is introduced. This transform spreads the energy of a symbol across the entire frequency domain and all time slots. The EST is coupled with the improved iterative detection algorithm named EST‐partial decision (PD)‐iterative‐interference‐cancellation (EST‐PD‐IIC) to maximize and leverage the potential diversity gain. Numerical simulation results demonstrate that the proposed scheme approaches the performance bound when signal‐to‐noise ratio (SNR) is about 21dB for 16‐ary quadrature amplitude modulation (16‐QAM). Complexity analysis illustrates that the computational complexity of the evolved EST‐PD‐IIC algorithm is lower than that of the famous vertically Bell laboratory layered space‐time detector (V‐BLAST) when the antenna array size is greater than . In summary, the proposed scheme is practical for providing high‐quality communication in multi‐path fading environments and can even enable a reliable communication without channel encoding when Eb/N0 exceeds a threshold in 5G‐Advanced.

Considering the inefficient resource allocation (RA) and high quality of service (QoS) requirement in vehicular communications, this paper proposes two dynamic RA algorithms, spectral clustering based greedy (SCGR) algorithm and multi-agent deep reinforcement learning (DRL) algorithm, to maximize both the sum capacity of the vehicle-to-infrastructure (V2I) uplinks and the total energy efficiency (EE) of the vehicle-to-vehicle (V2V) links by assigning the proper power and resource block (RB) to each V2V link in spatial modulation (SM) multiple-input multiple-output (SM-MIMO) vehicular system. For the SCGR algorithm, the spectral clustering (SC) scheme is first utilized to group the V2V links for the suitable RBs. Then, the optimal power is distributed to each V2V link by the greedy (GR) algorithm. For the DRL algorithm, a decentralized model-free network, improved multi-agent deep Q fully connected neural network (IDQFN), is developed to simultaneously find the best power allocation (PA) and RB assignment (RBA). Moreover, the SM technology is exploited to convey the information through the V2I and V2V links and improve the system capacity. Numerical results reveal that the proposed SCGR and IDQFN RA schemes outperform the typical RA algorithms, and the IDQFN scheme achieves better EE than the SCGR scheme, while the SCGR algorithm obtains the optimal average bit error rate (ABER) performance.

Abstract The demand for high‐resolution videos has been consistently rising across various domains, propelled by continuous advancements in societal. Nonetheless, limitations in imaging and economic factors often result in obtaining low‐resolution images. The currently available space‐time video super‐resolution methods often fail to fully exploit the information existing within the spatio‐temporal domain. To address this problem, the issue is tackled by conceptualizing the input low‐resolution video as a cuboid structure. An innovative methodology called “Cuboid‐Net”, which incorporates a multi‐branch convolutional neural network, is introduced. Cuboid‐Net is designed to collectively enhance the spatial and temporal resolutions of videos, enabling the extraction of rich and meaningful information across both spatial and temporal dimensions. Specifically, the input video is taken as a cuboid to generate different directional slices as input for different branches of the network. The proposed network contains four modules, that is, a multi‐branch‐based hybrid feature extraction module, a multi‐branch‐based reconstruction module, a first‐stage quality enhancement module, and a second‐stage cross frame quality enhancement module for interpolated frames only. Experimental results demonstrate that the proposed method is not only effective for spatial and temporal super‐resolution of video but also for spatial and angular super‐resolution of light field.

In recent years, sensor-based human activity recognition (HAR) technology has been the focus of extensive research and has been successfully applied to many aspects of people’s lives, but there are still some deficiencies. Most studies only distinguish daily activities and have low accuracy for easy confusing activities. In addition, many deep learning models only consider closed set HAR, but the real world contains unknown class (UC) activities that cannot be foreseen, which makes it challenging to apply these models to practice. In view of the above problems, this article proposes a multiresolution fusion convolution network (MRFC-Net) to cover the shortcoming that confusing activities are difficult to correctly identify, thus improving the accuracy of recognition. Furthermore, a multiresolution fusion convolution variational auto-encoder network (MRFC-VAE-Net) for open set HAR is proposed. According to the reconstruction loss of the network, the corresponding threshold is set to effectively classify the known and UC activities in the open set. At the same time, a rich data set named daily-abnormal activity of special group (DAASG) is constructed, which can be applied to the daily monitoring of special groups, such as prisoners and the elderly. Experiments and analyses are carried out on the wireless sensor data mining (WISDM), physical activity monitoring for aging people (PAMAP2) and DAASG data sets, to prove the effectiveness and superiority of the proposed networks.

Human activity recognition (HAR) technology based on wearables has received increasing attention in recent years. The traditional methods have used hand-crafted features to recognize human activities, resulting in shallow feature extraction. With the development of deep learning, an increasing number of researchers have focused on studying deep learning methods. To achieve higher recognition accuracy, the majority of the current HAR research involves multisource and multimodal sensors (MMSs) data. However, due to the limitations in the receptive fields of single-dimensional convolutional kernels, these networks are still infeasible for extracting spatiotemporal features. In this study, a multidimensional parallel convolutional connected (MPCC) deep learning network based on MMS data for HAR is proposed that fully utilizes the advantages of multidimensional convolutional kernels. Moreover, multiscale residual convolutional squeeze-and-excitation (MRCSE) modules are proposed to enrich the diversity of feature information by combining squeeze-and-excitation (SE) blocks. A daily home activity (DHA) data set is constructed based on the requirements for HAR in certain scenarios, such as smart home, and we conduct experiments on the optimal combination of sensor locations on the DHA data set according to a weighted$\text{F}1~({\mathrm{ F}}_{\mathrm{ W}})$-score. Both tenfold and leave-one-subject-out (LOSO) cross-validations (CVs) are used to evaluate the performance of the proposed network. The MPCC-MRCSE network achieves${\mathrm{ F}}_{\mathrm{ W}}$-scores of 98.33% and 95.42% on the physical activity monitoring for aging people (PAMAP2) and OPPORTUNITY data sets using tenfold CVs, respectively, and achieves${\mathrm{ F}}_{\mathrm{ W}}$-scores of 81.47% on the PAMAP2 when applying an LOSO CV.

Integrating reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) provides a promising solution to support large-scale secure communications. However, reducing the impact of transceiver hardware impairments (HWI) on the performance of RIS-aided NOMA secure networks remains a challenge. In this paper, we propose a robust transmission scheme for RIS-aided NOMA secure networks with transceiver HWI under two proposed eavesdropping scenarios and two artificial noise methods. A closed-form expression for the distortion noise power caused by transceiver HWI in NOMA networks is derived to quantify transceiver HWI. The sum secrecy rate of NOMA users with imperfect successive interference cancellation (SIC) is maximized under distortion noise for more robust security. Further, to tackle the resulting non-convex problem, we decompose it into two sub-problems. Both of them are converted into convex problems using the multi-dimensional quadratic transform (MDQT) method, semi-definite relaxation (SDR) technique, and the successive convex approximation (SCA) algorithm, and then employing the alternate optimization (AO) algorithm solve them. Numerical results demonstrate that the proposed scheme has superior security performance over orthogonal multiple access (OMA) networks, time division multiple access (TDMA) networks, and traditional NOMA networks, achieving more robust performance than the conventional scheme that ignores HWI and imperfect SIC.

In this paper, we consider the channel estimation problem in an IRS-assisted wireless communication system where both the direct and reflected users can be served simultaneously. It is assumed that all the channels experience block fading where the reflecting channel variation follows a classical autoregressive (AR) model. In order to obtain accurate channel state information (CSI) of all the users, the channel estimation process is performed in two stages. In the first stage, we employ a classical least square (LS) method to estimate the direct channels. In the second stage, we can remove the signal interference of the direct users to the reflected users by performing the channel estimation in the null space of the direct channels. Then, we perform reflecting channel estimation by using the Kalman filter, which is able to track the channel variations by taking advantage of prior estimations. Numerical results demonstrate that the proposed approach outperforms the existing method in terms of channel estimation errors for both the direct and reflected users.

Abstract This paper focuses on high efficiency secret key generation mechanism of physical‐layer communication over fading channels in ubiquitous wireless networks. The secret key rate via traditional physical‐layer approach could be limited when the wireless propagation channels connecting two sensors change slowly. To generate a high‐rate secret key and improve the communication efficiency over quasi‐static block fading channels, a novel multi‐randomness device‐to‐device secret key generation strategy and a cooperative communication mechanism aided by relay nodes are proposed. In the proposed schemes, the legitimate members to send random signals rotationally in every coherent time are set; thus, two legitimate ubiquitous wireless network members, Alice and Bob, can obtain the potential correlated information by exploiting the randomness and the reciprocity of the wireless propagation channels. Considering the reciprocity of wireless channels is variable while the forward channel gain and backward channel gain are correlated in coherent time, a modified secret key generation scheme is proposed via layered coding with theoretical secret key rates derived. The simulation results show that the proposed scheme outperforms traditional approaches with favourable application prospects in ubiquitous wireless communications networks and internet of things.

In this paper, we consider a three-hop multi-relay network with one transmitter, one receiver and two clusters of relay nodes. With the aid of perfect channel state information (CSI), two different design approaches are devised. In the first approach, we aim to maximize the received signal to noise ratio (SNR) under different transmit power constraint at the relay nodes by performing optimization on the beamforming vectors of the two clusters jointly, whilst in the second design, the total transmit power of the relay nodes is minimized under the QoS constraint at the receiver. It is shown that, in both approaches, upon the coefficients of either one cluster being determined, the other one could be optimized, and therefore the overall optimization problem can be resorted to an efficient iteration process. Simulation results show that our proposed approaches outperform the existing solutions for improved energy efficiency and increased receiving SNR.

In this study, the authors investigate the outage probability and bit‐error rate (BER) of distributed antenna system in downlink multicell environment with blanket transmission. Different from the most existing works, the variance of interference plus noise is treated as a random variable other than constant, and it is influenced by the short term fading when propagation pathloss and transmit power are given. From the perspective of information theory, the closed‐form and approximate analytical expressions of downlink outage probability and average BER in the cellular system are derived for no shadowing and shadowing scenarios, respectively. Extensive simulation results validate the theoretical analysis and demonstrate that the system performances can be significantly improved for cell‐edge users. Moreover, the proposed analytical method can obtain more accurate system performances.

In this paper, we design an outage probability constrained robust collaborative beamforming approach for the distributed multi-relay network in the downlink, where the channel state information (CSI) available is imperfect. We aim to minimize the total transmit power of the relay nodes whilst keeping the outage probability at the destination node below the predefined threshold. Assuming that the CSI mismatches follow Gaussian distribution, the equivalent counterpart for the outage probability constraint on the required signal-to-noise-ratio (SNR) is given explicitly. We show that though the original optimization problem is non-convex thus very intractable, it could be optimally solved by using the well-known interior-point method together with an efficient one-dimension search. Simulation results reveal that our proposed approaches can guarantee the quality-of-service (QoS) in term of outage probability in statistical sense while the non-robust scheme fails to do so.

Lifetime extension is a key design issue for wireless sensor networks (WSN) with battery-operated nodes. Compared with direct transmission, transmission power can be significantly reduced by cooperative communication, because it can effectively mitigate multi-path fading by introducing space diversity. In this paper, BER analysis for M-PSK and M-QAM modulation in noncooperative and cooperative situations is firstly presented. Then power is optimally allocated to source and relay nodes with the objective of minimizing the total transmission power under the average BER constraint. Based on these works, a new distributed cooperative MAC protocol is proposed to improve the lifetime of WSN. In this protocol, both channel state information (CSI) and residual energy information (REI) of sensor nodes are considered to choose the cooperative nodes. Simulation results show that when the access point (AP) is above a certain height, the proposed cooperative protocol can significantly prolong network lifetime compared with non-cooperative MAC protocols.

Along with the multiple-input multiple-output (MIMO) system gains, comes a price in hardware complexity and cost due to multiple RF chains. It is possible to alleviate this cost and at the same time maintain many advantages of MIMO systems by a technique known as antenna selection. In this paper, we propose a new receive antenna selection algorithm which is based on the orthogonal components of the rows of channel matrix. It can achieve very nearly outage capacity with the optimal selection method while keep low computational complexity. Besides, the bit error rate (BER) performance under space-time block coding (STBC) transmit scheme demonstrates the significant performance of the proposed selection algorithm

Multiple-antenna wireless communication systems have recently attracted significant attention due to their higher capacity and better immunity to fading as compared to systems that employ single-sensor transceiver. Increasing the number of transmit and receive antennas enables to improve system performance but at the price of higher hardware cost and computational burden due to the multiple RF chains. Antenna selection is a low-cost low-complexity alternative to capture many of the advantages of MIMO systems. In this paper, we propose a new antenna selection algorithm with low complexity for wireless multiple-input multiple-output (MIMO) systems. The new algorithm is based on the orthogonal components of the rows of channel matrix. It achieves almost the same outage capacity as the optimal selection method while keeping lower computational complexity. The simulation results for quasi-static Rayleigh flat fading channel demonstrate the significant performance of the proposed selection algorithm

Theoretical investigation have shown that multiple antenna schemes can improve link reliability and provide high spectral efficiency. Space-time block coding (STBC) and vertical Bell Labs layered space time (V-BLAST) systems are capable of meeting these requirements by using multiple antenna strategies,respectively. However, in order to achieve largely improved performance they need precise channel information at the receiver, which is difficult to be guaranteed in some specific scenarios. In this paper, independent component analysis (ICA) technique is exploited to detect the transmitted signals blindly, which needs not the channel estimation. By analyzing the essential structures of STBC and V-BLAST systems, the specific models suitable to ICA are established. The robustness against channel estimation error and flexibility of system design can be acquired by using the ICA based scheme. Simulation results demonstrate the validity of proposed approach and the feasibility of ICA based scheme is also discussed.