The frequency-modulated continuous wave (FMCW) radar has received much attention in the field of noncontact vital signs monitoring. However, since vital signs are usually very weak, it can be easily buried by interference and noise, especially for the heartbeat signal. To tackle this challenge, this article proposes a novel systematic vital signs detection framework using the multiple-input multiple-output FMCW radar. First, the signal noise ratio of the vital signs signal is enhanced by combining the phase signals of multiple channels using the maximum ratio combining method. Then, to suppress noise and interference, we construct the vital signs signal with singular spectral analysis and propose a correlation-based selection criterion to select potential intrinsic mode functions of the respiration and heartbeat signals. Finally, a fast independent component analysis is applied to extract the respiration signal, and the second-order derivative based fast independent component analysis in conjunction with an infinite impulse response notch filter is further developed to extract the heartbeat signal. Simulations and experimental results validate the effectiveness of the proposed framework.

Precise positioning is a key factor and enabler technology for many use cases on intelligent transportation systems (ITS) and connected and automated vehicles (CAVs). Recently, the quantum positioning system (QPS) based on quantum ranging has emerged as a novel way to improve security and precision. As a key process of QPS, the entangled photons based ranging technology has picosecond-level clock synchronization, and the ranging accuracy can reach the Heisenberg limit. If promising QPS is deployed in the ITS and CAVs, it will cause a profound change. However, the existing QPS still lacks accuracy and robustness in different scenarios. To solve this problem, we proposed an efficient and robust fusion positioning system based on entangled photons. In this system, we derive the ranging accuracy limit with many factors and propose a fast data grouping and selection algorithm to improve real-time performance. Furthermore, we propose a fusion extended fingerprint localization method for robust positioning in the dynamic environment. The effectiveness and robustness of the system are verified by extensive experiments. When the range is 15m, the ranging accuracy can be limited to 0.0018m. The proposed system achieves the probability of positioning errors 90% within 0.13m with only two APs.

Change detection based on synthetic aperture radar (SAR) images is an important application in the remote-sensing technology field. However, the lack of labeled data has been a difficult problem in SAR image detection, especially for pixel-level change detection. In this letter, we propose a novel unsupervised change detection algorithm, which improves the detection accuracy by exploring features from both spatial and frequency domains of SAR images. In particular, first clustering is used as preclassification to obtain pseudo-labels and then by incorporating classifiers and pseudo-labels in terms of feature learning, a novel unsupervised detection algorithm is proposed. To improve the sensitivity of the algorithm to changed details and enhance the antinoise ability of the change detection network, the attention mechanism (AM) is integrated into the network to fully extract important spatial structure information. Moreover, a multidomain fusion module is proposed to integrate spatial and frequency domain features into complementary feature representations. This module contains multiregion features weighted by the channel-spatial AM and deep features filtered out by the gated linear units (GLUs) in the frequency domain. To verify the effectiveness of the proposed algorithm, it is compared against the other four SAR image change detection algorithms using three real datasets. The experimental results show that the proposed method outperforms the other four algorithms in terms of percent correct classification (PCC) and Kappa coefficient (KC).

With the development of the radar sensing technology, hand gesture sensing and recognition has attracted much attention. This letter adopts a frequency-modulated continuous wave (FMCW) radar to achieve short-range hand gesture sensing and recognition. Specifically, the range, Doppler, and angle parameters of hand gestures are measured by fast Fourier transformation (FFT) and multiple signal classification (MUSIC) algorithm, respectively. The mixup (MP) algorithm combined with augmentation (AU) algorithm using a weight factor is applied to expand the hand gesture data. Then, a complementary multidimensional feature fusion network-based hand gesture recognition (CMFF-HGR) is designed to extract the features and achieve HGR. Finally, a series of experiments are carried out to verify the effectiveness of the proposed approach, and the results show that the recognition accuracy is higher than the existing alternatives with low computational complexity.

Channel state information (CSI) can provide phase and amplitude of multichannel subcarrier to better describe signal propagation characteristics. Therefore, CSI has become one of the most commonly used features in indoor Wi-Fi localization. In addition, compared to the CSI geometric localization method, the CSI fingerprint localization method has the advantages of easy implementation and high accuracy. However, as the scale of the fingerprint database increases, the training cost and processing complexity of CSI fingerprints will also greatly increase. Based on this, this article proposes to combine backpropagation neural network (BPNN) and adaptive genetic algorithm (AGA) with CSI tensor decomposition for indoor Wi-Fi fingerprint localization. Specifically, the tensor decomposition algorithm based on the parallel factor (PARAFAC) analysis model and the alternate least squares (ALSs) iterative algorithm are combined to reduce the interference of the environment. Then, we use the tensor wavelet decomposition algorithm for feature extraction and obtain the CSI fingerprint. Finally, in order to find the optimal weights and thresholds and then obtain the estimated location coordinates, we introduce an AGA to optimize BPNN. The experimental results show that the proposed algorithm has high localization accuracy, while improving the data processing ability and fitting the nonlinear relationship between CSI location fingerprints and location coordinates.

Recently, hand gesture detection (HGD) system have become increasingly interesting to researchers in the field of human-computer interfaces. However, the traditional HGD has low robustness and detection accuracy, as well as privacy protection problem. Therefore, we present Rammar, a residual attention module (RAM) assisted Mask R-CNN, for frequency modulated continuous wave (FMCW) sensor based on HGD system. Firstly, by analyzing the time domain and frequency domain of the FMCW sensor signal, the three-dimensional feature maps of Range-Time-Map (RTM), Doppler-Time-Map (DTM) and Angle-Time-Map (ATM) of each hand gesture are obtained, respectively, avoiding insufficient information of single dimension parameter. Secondly, RTM, DTM and ATM images of each hand gesture are simultaneously sent to Rammar for training. To focus on the features of gesture, RAM in Rammar employs average-pooling and max-pooling to extract time and spatial features. Finally, the extracted three-dimensional feature maps are merged in the fully connected layer. The experimental results show that Rammar not only makes the average detection accuracy of the hand gestures to 98.1%(increased by 5%), but also reduces the detection time effectively.

Due to the rapid development of indoor Unmanned Aerial Vehicles (UAVs) in recent years, the localization of indoor UAVs has become a focus of attention in UAVs applications. Among them, the Wireless Local Area Network (WLAN) based localization approach has become an effective means to achieve indoor localization due to the widely-deployed WLAN infrastructure. At the same time, with the increased use of WLAN module in the state-of-the-art mobile devices, various types of mobile WLAN Access Points (APs) exist in indoor environment. In this circumstance, the mobile WLAN APs deteriorates localization accuracy since their associated Received Signal Strength (RSS) data become unstable with the variation of locations. To address this problem, a new approach based on the Density-based Spatial Clustering of Applications with Noise (DBSCAN) is proposed to detect mobile WLAN APs for manifold alignment localization of UAVs. Specifically, first of all, the DBSCAN is conducted at the Reference Points (RPs) on motion paths to detect mobile APs. Second, the RSS data from mobile APs are removed from the database to enhance the location-dependency of RSS data used for the localization. Third, the concept of augmentation process is considered in manifold alignment to achieve satisfactory localization accuracy. Finally, the extensive experimental results show that the proposed system performs better in localization accuracy compared with the existing CIMLoc and WILL under the presence of mobile WLAN APs.

The Internet of Things (IoT) has gradually changed the way of people’s lives due to its ability of connecting everything together, and meanwhile the accurate location sensing plays a crucial role in achieving this goal. Up to now, as one of the most representative outdoor localization systems, the global positioning system has been widely used, but its performance may be dramatically declined in indoor environment due to the serious multipath effect and signal attenuation caused by the complicated indoor structure. At the same time, the location fingerprint-based localization approach has become a popular one in indoor environment, and meanwhile the corresponding calibrated signal simplification in location database construction has been primarily considered due to its significant practical meaning in avoiding the blind signal sampling. In this paper, we propose to use an information-theoretic lens to construct the energy-efficient location fingerprint database for the localization in IoT. Interestingly, by analyzing the information loss in signal sampling, we analogize the database construction process into the information propagation process in a lossy channel, and then formulate the relations of sample capacity and localization error from an information-theoretic view. After that, by selecting an appropriate time interval to sample the independent and nonredundant signal, the minimum number of sampled signal under the given expected localization accuracy is determined. Finally, the extensive experimental results show that compared with the state-of-the-art approaches, the proposed one can effectively simplify the calibrated data for the energy-efficient location database construction in different wireless localization networks.

To facilitate green and secure communications, the energy efficiency (EE) and physical (PHY) layer security are desirable for cloud radio access network (C-RAN). However, the problem of EE optimization with guaranteed PHY layer security in C-RAN is challenging, especially in the presence of channel state information (CSI) uncertainty of both information receivers (IRs) and eavesdropper receivers (ERs). Thereby, in this paper, we investigate the worst-case EE maximization problem in a downlink C-RAN, subject to limited power budget of each BS and infinite number of PHY layer security constraints, which is a non-convex fractional programming problem and is NP-hard even in the event of perfect CSI. To solve this non-trivial problem, a fast-converging algorithm is developed. Specifically, based on successive convex approximation (SCA) technique, we transform the original problem into a semi-definite program (SDP) one, allowing for solving it iteratively. The tightness of the SDR is proved, and the SCA algorithm is also proved to converge to a Karush- Kuhn-Tucker point. Extensive simulations are carried out to verify the effectiveness of the proposed algorithm.

Applications on Location Based Services (LBSs) have attracted significant attention due to its personalized, convenient, and smart user experience, and meanwhile the accurate mapping and localization algorithm plays a crucial role in satisfying the LBSs. At the same time, motivated by the widely-deployed Wi-Fi network, the Wi-Fi signal based localization has become one of the superior positioning techniques in indoor environment, and the corresponding sample capacity involved in positioning database establishment should be given much attention due to its significant guidance meaning in practice. In this paper, we propose a new sample capacity optimization approach for indoor Wi-Fi localization from the information-theoretic view, namely Sample Capacity Optimization for Positioning database Establishment (SCOPE) in indoor Wi-Fi environment. Interestingly, we analogize the positioning database establishment process in indoor Wi-Fi environment into the information propagation process in a lossy channel, and meanwhile formulate the relations between the sample capacity and localization error. Experimental result shows that the proposed SCOPE can accurately estimate the minimum sample capacity with a given expected localization accuracy under different Access Point (AP) combination.

This paper considers an energy efficient (EE) maximization problem in a downlink cloud radio access network (CRAN), using the worst-case design criteria. We formulate the joint problem of robust EE and base station (BS) activation design by maximizing the worst-case EE under channel state information CSI uncertainty and per-BS power constraint, which is a nonconvex mixed-integer related fractional program and is difficult to solve. With the transformation of worst-case signal-to-interference-plus-noise ratio (SINR), we propose a single-stage branch-and-bound (BnB) algorithm to find the global optimal solution via solving a series of second-order cone programming (SOCP) problems for any given active BS set. Then, a heuristic BS activation (BSA) algorithm is proposed to choose active BSs successively to further improve the worst-case EE. Simulation results suggest that the BnB algorithm converges to the global optimum, and a higher EE can be achieved by selecting the active BSs properly.

This paper studies robust energy optimization for the cloud radio access network (C-RAN). The objective of this paper is to jointly minimize network power consumption through optimizing the base station (BS) mode, multi-user (MU)-BS association, and beamforming vectors given imperfect channel state information (CSI). To solve this non-trivial problem, we first transform the problem to a semi-definite programming (SDP) one using the S-lemma with the aid of the semi-definite relaxation technique, and then propose a SDP-based group sparse beamforming approach to solve it iteratively. Since the computational complexity of solving SDP problems is intractable, we propose to translate the uncertainty in the CSI to the uncertainty in its covariance matrix, and then recast the original problem as a mixed-integer second-order cone programming problem. We further propose a two-stage rank selection framework to determine the BS mode and MU-BS association separately and successively. Simulation results demonstrate the convergence of our proposed algorithms, and validate the effectiveness of the proposed algorithms in minimizing the network power consumption of the C-RAN.

This paper considers a downlink Coordinated Multi Point (CoMP) transmission network, where the base stations (BSs) are connected to a central unit via backhaul links to serve mobile users (MUs). The objective is to jointly optimize the BS mode (active or sleep), MU association, and cooperative beamforming to minimize the total power consumption including backhauling power. We formulate the problem as a mixed-integer non-convex problem under the constraints of signal-to-interference-plus-noise ratio (SINR) requirements and limited BS power. By exploiting a hidden convexity structure of the SINR, the problem is first transformed equivalently into a mixed-integer second-order core programming (MI-SOCP) problem. Due to the coupled integer variables, the complexity of exhaustive search grows exponentially with network size, which motivates developing low-complexity heuristic algorithms. We propose a novel Two-stage Rank Selection (TRS) method to approximately solve the coupled MI-SOCP problem by determining the BS operation mode and MU association successively, which enables the remaining beamforming computation to be achieved through an efficient SOCP solution. We compare the computational complexity and numerical performance of TRS against several state-of-the-art alternatives. Analytical and simulation results demonstrate that the proposed method can significantly reduce the computational complexity and the total power consumption over existing alternatives.

This paper investigates the power efficiency in downlink cooperative networks under spherical channel uncertainties, using a worst-case robust formulation. The aim of this paper is to jointly optimize the base station (BS) operation mode, mobile user (MU) association, and beamformings to achieve the robust network power gain. We formulate the problem as a mixed-integer non-convex problem with infinite number signal-to-interference-plus-noise ratio (SINR) and maximum transmit power constraints, which is nontrivial to solve even without this constraint. By firstly translating the uncertainty in CSI to the uncertainty in its covariance matrix, the SINR constraint spans a convex set using the Lagrangian based method. Due to the coupled integer variables, the complexity of exhaustive search grows exponentially with network size, motivating to develop low-complexity heuristic algorithms. We propose to solve the problem via a novel two-layer (TL) algorithm: inner and outer layer, successively. Specifically, the BS operation mode is determined successively in the outer layer using a Rank based Successive Search (RSS) method, while in the inner layer, an iterative Difference of Convex (DC) based algorithm is proposed to determine the MU association. Simulation results illustrate that the proposed algorithm can significantly reduce the total power consumption over existing alternatives.

There are some challenges associated with the spectrum sensing for cognitive radio (CR), one main challenge of spectrum sensing is hardware requirements. The paper proposes the energy detection and a direct-conversion spectrum sensing receiver (DCSSR) to respond to the challenge of hardware requirements. However, the main problem is DC offsets of the DCSSR. A DC offset adaptive energy detection (DC-AED) algorithm is proposed to resolve the problem. The paper gives the theory analysis of DC-AED and simulates its detection performance. The simulation results show that DC-AED can remove the influence of DC offsets perfectly while the change period of DC offsets is larger than 64 times the detection length.