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Indoor location-based services have become more and more important due to their potential applications in a wide range of personalized services in recent years. The accuracy of smartphone based 3D indoor localization is subjected to the poor performance of low-cost sensors and limited coverage of location sources. In order to solve these problems, this paper proposes a precise 3D indoor localization and trajectory optimization framework that uses the combination of sparse Wi-Fi Fine Time Measurement (FTM) anchors and built-in sensors (3D-LOWS). The inertial navigation system (INS) mechanization, multi-level constraints and observed values are integrated by the adaptive unscented Kalman filter to eliminate effects of cumulative error, indoor magnetic interference, and diversity of handheld modes. The Wi-Fi based ranging and landmark detection information is used to provide an accurate absolute reference to the built-in sensors based method. In addition, this paper proposes and evaluates two different trajectory optimization algorithms and compares the improved localization performance. The comprehensive experiments indicate that the proposed 3D-LOWS is proved to achieve accurate and stable 3D indoor positioning and trajectory optimization performance under complex indoor environments using sparse wireless stations. [ABSTRACT FROM AUTHOR]

Indoor drone or Unmanned Aerial Vehicle (UAV) operations, automated or with pilot control, are an upcoming and exciting subset of drone use cases. Automated indoor flights tighten the requirements of stability and localization accuracy in comparison with the classic outdoor use cases which rely primarily on (RTK) GNSS for localization. In this paper the effect of multiple sensors on 3D indoor position accuracy is investigated using the flexible sensor fusion platform OASE. This evaluation is based on real-life drone flights in an industrial lab with mm-accurate ground truth measurements provided by motion capture cameras, allowing the evaluation of the sensors based on their deviation from the ground truth in 2D and 3D. The sensors under consideration for this research are: IMU, sonar, SLAM camera, ArUco markers and Ultra-Wideband (UWB) positioning with up to 6 anchors. The paper demonstrates that using this setup, the achievable 2D (3D) indoor localization error varies between 4.4 cm and 21 cm (4.9 cm and 67.2 cm) depending on the selected set of sensors. Furthermore, cost/accuracy trade-offs are included to indicate the relative importance of different sensor combinations depending on the (engineering) budget and use case. These lab results were validated in a Proof of Concept deployment of an inventory scanning drone with more than 10 flight hours in a 65 000 m2 warehouse. By combining lab results and real-life deployment experiences, different subsets of sensors are presented as a minimal viable solution for three different indoor use cases considering accuracy and cost: a large drone with little weight- and cost restrictions, one or more medium sized drones, and a swarm of weight and cost restricted nano drones. [ABSTRACT FROM AUTHOR]

This paper presents a new linear eddy current speed sensor with rectangular-shaped coils. The excitation coil and the pick-up coil have a perpendicular configuration without a magnetic yoke. The proposed sensor is shorter than the previous designs. The sensor works for a conductive moving target; in this paper we present calculations and an experimental verification for solid iron and aluminum moving part materials. A novel 3D analytical method is presented for the description and for the design of an eddy current speed sensor that is fast and has high precision. The source fields and the reaction fields caused by induced eddy currents are separated in our 3D analytical method, which facilitates an enhanced investigation of the features of the speed sensor. Evaluations of the effects of the moving part material and of coil lift off on the performance of the speed sensor are made with the use of a 3D analytical method. Measurements are performed for an eddy current speed sensor at different speeds up to 11.65 m/s and at different frequencies with a novel analytical model in terms of the induced voltage in the pickup coil versus speed. Simplicity and high precision are the main advantages of the proposed speed sensor. The achieved linearity error is 0.47% (measured) up to 11.6 m/s, and 0.43% (calculated) up to 117 m/s (420 km/h). [ABSTRACT FROM AUTHOR]

Target detection is an important problem in multiple-input multiple-output (MIMO) radar. Many existing target detection algorithms were proposed without taking into consideration the quantization error caused by analog-to-digital converters (ADCs). This paper addresses the problem of target detection for MIMO radar with one-bit ADCs and derives a Rao’s test-based detector. The proposed method has several appealing features: 1) it is a closed-form detector; 2) it allows us to handle sign measurements straightforwardly; 3) there are closed-form approximations of the detector’s distributions, which allow us to theoretically evaluate its performance. Moreover, the closed-form distributions allow us to study the performance degradation due to the one-bit ADCs, yielding an approximate 2 dB loss in the low-signal-to-noise-ratio (SNR) regime compared to $\infty$ -bit ADCs. Simulation results are included to showcase the advantage of the proposed detector and validate the accuracy of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper proposed a method for estimating the suspension relative velocity using a single six-dimensional measurement unit attached to a vehicle body. This method can reduce the number of sensors, reduce costs, and address sensor packaging issues. A wheelbase preview assumption is used to overcome the lack of unsprung mass information. The proposed estimation method is verified by the vehicle simulator Carsim and the stability of the estimation method is also analyzed. In addition, the frequency response and the time response of the sky-hook controller based on the estimated relative velocity are compared with the sky-hook controller based on the actual relative velocity. The results show that the proposed estimator produces very robust performance on various road conditions. [ABSTRACT FROM AUTHOR]

Autonomous vehicles are expected to significantly enhance the human mobility. However, recently researchers have discovered and demonstrated some attacks on vehicles, which have caused a panic among the public. Furthermore, these attacks have demonstrated that the security issue is still one of the major challenges of vehicles. In this paper, we propose a novel edge computing based anomaly detection, coined edge computing based vehicle anomaly detection (EVAD), which exploits edge based sensor data fusion to identify the anomaly events. The time domain property, i.e., the correlation between different intra-vehicle sensors, and the frequency domain property of sensor data are utilized to judge whether an anomaly has occurred within the vehicle. Especially, to reduce the computation overhead and improve the performance, multiple sensors will be organized as ring architecture, which is a tradeoff of detection accuracy and complexity. In addition, the major components (e.g., anomaly detection module) of EVAD are embedded in edge computing devices, which make the anomaly detection be more efficient and privacy preserving. Meanwhile, a more appropriate model is generated on the cloud server, of which computation overhead maybe heavy for edge computing devices. This paper evaluates the performance of EVAD under different scenarios, and the experimental results demonstrate its feasibility and efficiency. The average true positive rate achieves 99.5% with 1% false positive rate. [ABSTRACT FROM AUTHOR]

Sphere decoding (SD) based detection schemes for sparse code multiple access (SCMA) systems have recently received attention due to their promising features. However, the existing SD-based schemes can only be applied to SCMA systems with constellations that possess a certain structure. In this paper, we propose a novel SD-based detection scheme, namely improved SD (ISD), for SCMA that achieves the optimal maximum likelihood detector for any arbitrary regular or irregular constellations. To overcome the rank deficiency problem of the SCMA channel matrix, we fix a portion of the transmitted symbols and obtain an optimal detection problem that is equivalent to the original SCMA detection problem for all types of constellations. Moreover, due to the sparse nature of SCMA, the partial metric at each layer is evaluated in such a way that is independent of users assigned to each resource element. This, in turn, reduces the average complexity of ISD. [ABSTRACT FROM AUTHOR]

High-speed and low-latency communications are one of the major requirements for connected vehicle environments. Safety messages in vehicular networks must arrive on time for each vehicle, and the optimization of driving paths also strongly depends on low-latency traffic information. To comply with the demands, a base station take important roles to provide a short service delay for the vehicles, even at the expense of performance. The relationship between delay time and decoding performance at receiver sides in physical layers can be explained by the performance/complexity tradeoff as follows. To achieve better performance in terms of the minimization of word error rates in decoding, the receiver usually requires higher complexity, which results in larger delays. We analyze this tradeoff based on the receiver queue model. This paper proposes a novel algorithm to adaptively select one of receiver candidates under the constraint of queue stability so as not to degrade low-latency communications. Based on the configurable architecture for multi-input multioutput (MIMO) detectors, this paper shows that the proposed algorithm works well on adaptive MIMO detector selection. [ABSTRACT FROM PUBLISHER]

The problem of sensor fault estimation has been studied for Unmanned Surface Vehicle (USV) systems in networked environment. Assuming that the process noise is related to the measurement noise and packet loss will occur during the measurement transmission. We use an event-trigger mechanism (ETM) to save communication resources. Accordingly, an observer is proposed to estimate the state and sensor fault, and the trigger threshold is associated with the correlation coefficient. By adjusting the threshold, the observer performance and energy consumption can be balanced in an ideal situation. The observer designed in this paper reduces the effects of correlation noises effectively and good handling network packet losses, and it has been proved that the expected covariance of the estimation error is bounded. Finally, the simulation experiment on the identified model of USV in the laboratory is performed to validate the relevant conclusions. [ABSTRACT FROM AUTHOR]

This paper proposes a scheme for constructing an Internet Protocol (IP) version 6 over low-power wireless personal area networks (6LoWPAN) wireless sensor network based on a cluster tree. In the cluster generation algorithm, a cluster tree is always an isolated node with the maximum number of neighbor isolated nodes that launches the cluster generation process; therefore, the total number of cluster heads is minimized. In the cluster-tree construction algorithm, the cluster-tree architecture is proposed, and the cluster-tree construction algorithm minimizes the total number of nodes included in a cluster tree; therefore, the routing cost is reduced. In the cluster-tree repair algorithm, when a cluster head or a cluster associate node fails or moves, a new cluster head or cluster associate node is elected to maintain the cluster-tree topology. This paper compares the performance parameters of Least Cluster Change algorithm with Lowest-ID, Root-based Lowest-ID cluster constructing mechanism, and the proposed scheme, including the cluster-tree topology stability, the routing memory cost, and the routing update cost. The analytical data show that the performance of the proposed scheme is better. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a novel generalized space-code index modulation (GSCIM) scheme for spectrum-efficient and energy-saving transmission. In our proposed scheme, the input information bits are transmitted not only by modulated symbols, but also by the activated transmit antennas pattern and the selected spreading code patterns for the in-phase and quadrature components. Due to the increase of the index dimension, more information bits can be implicitly transmitted, which can improve energy efficiency and have a higher data rate compared with spatial modulation (SM), generalized SM (GSM), generalized CIM-aided SM (GCIM-SM) and CIM-aided quadrature spatial modulation (CIM-QSM). In order to further reduce the complexity of despreading-based maximum likelihood (ML) detection, we propose an enhanced low-complexity detector that can achieve near-ML error performance. An analytical upper bound on the average bit error probability associated with the optimal ML detection is derived, which is verified by simulation results. Simulation results also show that the proposed GSCIM scheme outperforms SM, GSM, GCIM-SM and CIM-QSM schemes in terms of error performance. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of decoding in large-scale multiple-input–multiple-output (MIMO) systems. In this case, the optimal maximum-likelihood (ML) detector becomes impractical due to an exponential increase in the complexity with the signal and the constellation dimensions. This paper introduces an iterative decoding strategy with a tolerable complexity order. We consider a MIMO system with finite constellation and model it as a system with sparse signal sources. We propose an ML relaxed detector that minimizes the Euclidean distance with the received signal while preserving a constant \ell1 -norm of the decoded signal. We also show that the detection problem is equivalent to a convex optimization problem, which is solvable in polynomial time. Two applications are proposed, and simulation results illustrate the efficiency of the proposed detector. [ABSTRACT FROM PUBLISHER]

This paper investigates the capacity of radio channels when iterative channel estimation, data detection, and decoding are employed. Knowing the capacity gain from iterative detection versus purely pilot-based channel estimation helps a designer compare the performance of an iterative receiver against a noniterative receiver and select the best balance between performance and cost. A bound is put on the linear minimum mean square error (LMMSE) channel estimation error, based on which a bound on the capacity is obtained. The attainable capacity is related to the channel estimation error of the receiver. The bounds take into account the uncertainty in symbol detection on channel estimation and incorporate the effect of channel estimation error on channel capacity. The interaction between the symbol detector and the decoder is analytically characterized and depicted in an extrinsic information transfer (EXIT) chart, where a bound on the detector curve is found. With optimal LMMSE pilot-based channel estimation, the results of this paper demonstrate that iterative channel estimation provides insignificant capacity advantage at fading rates below 1% of the symbol rate, although a computational-cost gain is still available. Iterative channel estimation provides a capacity benefit if suboptimal pilot signaling is used to provide initial channel estimates. [ABSTRACT FROM PUBLISHER]

In this paper, efficient deployment algorithms are proposed for a mobile sensor network to improve the coverage area. The proposed algorithms find the target position of each sensor iteratively, based on the existing coverage holes in the network. The multiplicatively weighted Voronoi (MW-Voronoi) diagram is used to discover the coverage holes corresponding to different sensors with different sensing ranges. Three sensor deployment algorithms are provided, which tend to either move sensors out of densely packed areas or place them in proper positions with respect to the boundaries of the MW-Voronoi regions. Under the proposed procedures, the sensors move in such a way that the coverage holes in the target field are reduced. Simulations confirm the effectiveness of the deployment algorithms proposed in this paper. [ABSTRACT FROM PUBLISHER]

Multiple-input–multiple-output (MIMO) technology can efficiently use the spectrum to increase the communication throughput. Designing low-complexity detection algorithms with high performance for the MIMO system has been an important issue. In this paper, we propose efficient detection algorithms for MIMO systems based on differential metrics. We first define differential metrics and give their recursive calculation of different orders. Based on differential metrics, we give the principle of gradient search. We then propose a gradient search algorithm (GSA) that can provide a tradeoff between performance and complexity. The GSA applies the indicative functions such that we can determine in advance some maximum-likelihood (ML) bits of the initial sequence and reduce the searching range. The GSA also uses a stop condition with which we can stop the search if the proper condition is satisfied. The GSA does not need QR decomposition (QRD) or matrix inversion. The multiplicative operations are only necessary before the searching process, during which only the additive operations are needed. For large-scaled MIMO systems, we also give a simple searching algorithm based on differential metrics. Finally, we propose a fixed-complexity gradient algorithm (FCGA), which has a fixed number of operations during the searching process and is appropriate for pipelined hardware implementation. The simulation results validate the efficiency of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the PHY-layer authentication that exploits radio channel information (such as received signal strength indicators) to detect spoofing attacks in wireless networks. The interactions between a legitimate receiver and spoofers are formulated as a zero-sum authentication game. The receiver chooses the test threshold in the hypothesis test to maximize its utility based on the Bayesian risk in the spoofing detection, whereas the spoofers determine their attack frequencies to minimize the utility of the receiver. The Nash equilibrium of the static authentication game is derived, and its uniqueness is discussed. We also investigate a repeated PHY-layer authentication game for a dynamic radio environment. As it is challenging for the radio nodes to obtain the exact channel parameters in advance, we propose spoofing detection schemes based on Q-learning and Dyna-Q, which achieve the optimal test threshold in the spoofing detection via reinforcement learning. We implement the PHY-layer spoofing detectors over universal software radio peripherals and evaluate their performance via experiments in indoor environments. Both simulation and experimental results have validated the efficiency of the proposed strategies. [ABSTRACT FROM PUBLISHER]

As vehicles get equipped with increasingly complex sensors and processors, the communication requirements become more demanding. Traditionally, vehicles have used specialized networking technologies designed to guarantee bounded latencies, such as the controller area network (CAN) bus. Recently, some have used dedicated technologies to transport signals from cameras, lidars, radars, and ultrasonic sensors. In parallel, IEEE working groups are defining Ethernet standards for time-sensitive networks (TSN). This paper describes an Ethernet-based architecture with provable guaranteed performance and simple configuration that is suitable for supporting the communication requirements of in-vehicle networks. [ABSTRACT FROM AUTHOR]

Large-scale generalized spatial modulation (GSM) technology has great potential in constructing low-complexity massive multiple-input multiple-output wireless systems, with a simplified structure which employs only a few radio frequency (RF) chains. However, detection is a very complex problem for large-scale GSM systems. In this correspondence paper, we have proposed an innovative probability sorting based approximate message passing (PS-AMP) detector. Specifically, in the proposed scheme, an approximate message passing method is used to obtain the probability for activation of each transmit antenna, and a method based upon probability sorting is developed to ascertain the signal search space. Simulation results demonstrate that the proposed PS-AMP detector exhibits a better bit error rate performance with a reduced complexity compared to its conventional counterparts. [ABSTRACT FROM AUTHOR]

In wireless communication systems, the correlation among the antennas of diversity systems can hardly be neglected. To the best of our knowledge, no previous studies have attended to address, thoroughly, this problem for the cross-correlation-based detectors. This will be the topic of this paper. Namely, in this paper, we consider the performance assessment of a second-order decision metric that is based on the cross correlation of antenna's output. The detector performance analysis has been made concerning the receiver operating characteristic curves, the robustness to estimation uncertainty, and the design parameters. This last introduces the minimum required sample number that guarantees a given detection performance. To this end, the probability distribution of the inner product of two complex non-zero-mean partially correlated random vectors is investigated. Next, we exhibited the relevance of our results through the analytical performance derivation of the cross-correlation-assisted sensing approach in terms of design parameters, spectral efficiency, and decision reliability. Monte Carlo simulations are achieved to prove the tightness of our derived results. [ABSTRACT FROM AUTHOR]

Orthogonal frequency-division multiplexing (OFDM) is a promising technology for communication systems. This paper investigates the impacts of unknown noise variance on the popular spectrum sensing scheme, i.e., energy detection, for OFDM cognitive radios over multipath fading channels. To study the effects of unknown parameters on the energy detector, a new maximum-likelihood estimation of noise and signal powers employing the cyclic prefix of OFDM is presented in this paper. The mean values and Cram $\acute{\text{e}}$ r–Rao lower bounds of the estimation are obtained. Furthermore, the performances of the energy detector for both hypotheses, i.e., false-alarm rate and detection probability, under the influence of unknown noise variance are validated by both simulation and analytical results. The assessment on the required number of samples for the proposed energy detector is conducted, which indicates that an amount of 40–50% samples can be saved compared to the conventional energy detector. [ABSTRACT FROM AUTHOR]

In practical applications, an imperfect channel estimate over a noisy channel is inevitable. This paper investigates an interference-aware receiver in which colored-estimation-noise-based maximum-likelihood (CEN-ML) detection is employed to mitigate the impact of imperfect channel estimates. It is shown that the CEN-ML detection, which treats the channel estimation errors as colored noise, has superior performance and is robust to imperfect channel estimation. However, a CEN-ML interference-aware detector usually requires computational efforts that are too complex for multiple-input–multiple-output (MIMO) interference-limited systems. To reduce the complexity, this paper proposes new methods for both the preprocessing and tree-search stages of an interference-aware sphere detector (SD). First, a channel extended matrix is constructed using the information from channel estimation errors in a preprocessing stage. The channel extension model can reduce the complexity without performance loss. Second, two tree pruning (TP) criteria are developed in a tree-search stage to make the implementation of the CEN-ML SD feasible. The first criterion is based on a lower bound of the CEN-ML metric and can achieve the same result as the optimal brute-force method. The second criterion is derived from an approximated metric and can further reduce the complexity with small performance loss. Simulation results for typical Third-Generation Partnership Project Long-Term Evolution (LTE)/LTE-Advanced scenarios show that the new methods provide the ability to deal with the channel estimation errors and to achieve a better tradeoff between complexity and performance (e.g., in terms of throughput or error rate) in interference-limited scenarios. The proposed low-complexity CEN-ML SD can have a gain of more than 10 dB than the other investigated detection for 16-ary quadrature amplitude modulation (QAM) or 64-QAM at a target coded block error rate (BLER) of \mbox10^-2. [ABSTRACT FROM AUTHOR]

Lane-level vehicle self-localization is a challenging and significant issue arising in autonomous driving and driver-assistance systems. The Global Navigation Satellite System (GNSS) and onboard inertial sensor integration are among the solutions for vehicle self-localization. However, as the main source in the integration, GNSS positioning performance is severely degraded in urban canyons because of the effects of multipath and non-line-of-sight (NLOS) propagations. These GNSS positioning errors also decrease the performance of the integration. To reduce the negative effects caused by GNSS positioning error, this paper proposes to employ an innovative GNSS positioning technique with the aid of a 3-D building map in the integration. The GNSS positioning result is used as an observation, and this is integrated with the information from the onboard inertial sensor and vehicle speedometer in a Kalman filter framework. To achieve stable performance, this paper proposes to evaluate and consider the accuracy of the employed GNSS positioning method in dynamic integration. A series of experiments in different scenarios is conducted in an urban canyon, which can demonstrate the effectiveness of the proposed method using various evaluation and comparison processes. [ABSTRACT FROM PUBLISHER]

This paper proposes two new data detection algorithms for decode-and-forward alternate-relaying cooperative communication systems. These algorithms are discussed in the context of bit-interleaved coded modulation transmission with a multiple-antenna destination. The interference signal, resulting from the concurrent transmission of the source and one of the relays, is exploited as a beneficial resource to develop an optimal data detection algorithm. We show that the optimal algorithm can be implemented by parallel demappers, each based on a family of Bahl, Cocke, Jelinek, and Raviv algorithms, connected to parallel decoders in an iterative way. Due to the need to buffer the entire frame before decoding, the optimal algorithm suffers from long processing time. A suboptimal algorithm is also proposed to avoid this problem, where each packet is decoded by using two consecutive received packets. The suboptimal algorithm reduces processing time, complexity, bandwidth loss, and memory size with a slight increase in the bit error rate (BER). The BER performance of the proposed algorithms is evaluated via Monte Carlo simulations, and the results indicate their effectiveness in improving the BER performance. [ABSTRACT FROM AUTHOR]

We consider spectrum detection in multiple homogeneous two-state channels. At each time, a secondary user (SU) can simultaneously detect multiple channels with imperfect spectrum detectors. The objective of the SU is to obtain the idle channels as quickly as possible under a false detection constraint. This detection problem is formulated as a restless multiarmed bandit (RMAB) problem that is proven to be PSPACE-hard. A feasible approach, namely myopic policy, is to detect the best channels until idle channels are caught. In this paper, we establish the structure of the myopic policy, prove that the myopic policy is optimal in the case of detecting N-\mbox1 of $N$ channels under certain mild assumptions, and further show that the myopic policy is not optimal generally by constructing a counterexample. [ABSTRACT FROM PUBLISHER]

In the era of the fifth generation (5G) communication networks, massive multiple-input multiple-output (MIMO) systems demand even lower computation complexity and power consumption while catching up with good detection performance. In this paper, a low-complexity nonlinear detection algorithm is proposed for massive MIMO systems, which is based on partial tree search and successive interference cancellation (SIC). The proposed scheme allows us to expedite the detection process by coping with the transmit symbols group by group. As compared to vertical-Bell laboratories layered space time (V-BLAST), the major breakthrough of computation reduction lies in the fact that the partial tree search can assist the detection process to avoid the inversion of the detection matrix required in each recursion of the SIC process. Both computational complexity analysis and simulation results show that our proposed algorithm not only significantly reduces computational complexity, but also has better bit error rate (BER) performance. [ABSTRACT FROM AUTHOR]

Autonomous driving technologies can provide greater safety, comfort, and efficiency for future transportation systems. Until now, much of the research effort has been devoted to developing different sensing and control algorithms. However, there has been limited research on how to handle sensor errors efficiently. A simple error in the sensor may lead to an unexpected failure in the whole autonomous driving function. In those cases, the vehicle is then recommended to be sent back to the manufacturer for repair, which costs time and money. This paper introduces an efficient automatic online sensor correction method. The method includes four major functions: sensor error detection, human teaching, vehicle learning, and vehicle self-evaluation. The first function is assumed to be ready and the major contribution of this paper is the human-vehicle teaching and learning framework, which utilizes human-vehicle interaction to collaboratively adjust the parameter in the control model in order to compensate for the errors of the sensors. The self-evaluation function is also briefly introduced. The applications of this method to radar and vision sensors to recover adaptive cruise control and lane keeping functions are introduced in detail. Experimental results acquired from high-fidelity 1/10-scale autonomous driving vehicles illustrate the effectiveness and advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a model predictive control-based obstacle avoidance algorithm for autonomous ground vehicles at high speed in unstructured environments. The novelty of the algorithm is its capability to control the vehicle to avoid obstacles at high speed taking into account dynamical safety constraints through a simultaneous optimization of reference speed and steering angle without a priori knowledge about the environment and without a reference trajectory to follow. Previous work in this specific context optimized only the steering command. In this paper, obstacles are detected using a planar light detection and ranging sensor. A multi-phase optimal control problem is then formulated to simultaneously optimize the reference speed and steering angle within the detection range. Vehicle acceleration capability as a function of speed, as well as stability and handling concerns such as preventing wheel lift-off, are included as constraints in the optimization problem, whereas the cost function is formulated to navigate the vehicle as quickly as possible with smooth control commands. Simulation results show that the proposed algorithm is capable of safely exploiting the dynamic limits of the vehicle while navigating the vehicle through sensed obstacles of different sizes and numbers. It is also shown that the proposed variable speed formulation can significantly improve performance by allowing navigation of obstacle fields that would otherwise not be cleared with steering control alone. [ABSTRACT FROM PUBLISHER]

Common vulnerabilities in typical intelligent cyber-physical systems such as unmanned aerial vehicles (UAVs) can be easily exploited by cyber attackers to cause serious accidents and harm. For successful UAV operations, security against cyber attacks is imperative. In this paper, we propose a modified sliding innovation sequences (MSIS) detector, based on the extended Kalman filter optimal state estimation, for a dynamic quadrotor system to detect cyber attacks inflicted on both its actuators and sensors in real time. These cyber attacks include random attacks, false data injection (FDI) attacks and denial-of-service (DoS) attacks. The MSIS detector computes the operator norm of the normalized innovation (residual) sequence within a sliding time window and triggers the alarm if the value is above the preset threshold. For a quadrotor undergoing rapid turns in a complex trajectory, the detector observes a reduced false alarm rate as compared to other state estimation-based detectors. To address the initial estimation error problem, we implement an iteration procedure to initiate and calibrate the detector. By evaluating the sample covariance of the normalized innovation sequence, the MSIS detector has the capability to isolate cyber attacks. Finally, simulation results of a quadrotor in a periodic, complex trajectory flight are provided to verify the effectiveness of the MSIS detection and isolation method. [ABSTRACT FROM AUTHOR]

The internet of vehicles needs mobile communication systems for efficient communications. On the physical downlink control channel (PDCCH) of 5G, blind detection is performed to identify the downlink control information (DCI). Due to the uncertainty of DCI formats and PDCCH formats, the existing two-stage blind detection schemes separately decode every PDCCH code block under different DCI formats, leading to a high decoding complexity in the first stage. To reduce the detection complexity, this paper proposes an efficient DCI detection method, where a significance-test strategy is designed to detect the DCI formats. The significance-test strategy can forecast the DCI lengths without separately decoding the whole codeword, thus reducing the decoding complexity by several times. Moreover, the accuracy of the DCI detection is analyzed, with which the test scope of the test statistic is optimized for minimizing the decoding complexity on condition of meeting the detection accuracy requirements. Simulation results show that the proposed method significantly reduces the decoding complexity relatively to the existing two-stage detection schemes. [ABSTRACT FROM AUTHOR]

Direct-conversion transceivers are in demand due to low implementation cost of analog front ends. However, these transmitters or receivers introduce imperfections such as in-phase and quadrature-phase (I/Q) imbalances. In this paper, we first investigate the effect of I/Q imbalance on the performance of primary system and show that these impairments can severely degrade the performance of cognitive radio system that are based on orthogonal frequency-division multiplexing (OFDM) multiple-access scheme. Next, we design a new four-level hypothesis blind detector for spectrum sensing in such cognitive radio system and show that the proposed detector is less vulnerable to I/Q imbalance than conventional two-level detectors. [ABSTRACT FROM PUBLISHER]

Massive multi-user (MU) multiple-input multiple-output (MIMO) is an enabling technology for the next-generation wireless systems as it provides significant improvements in terms of spectral efficiency and per-user peak data rates compared to existing, small-scale MIMO. The presence of increasing antennas at the infrastructure base-station results, however, in high computational complexity for data detection, which requires low-complexity algorithms and efficient hardware designs. Descent search (DS) algorithms have been proposed to reduce complexity of data detection and enable efficient hardware implementations. However, their error-rate performance is not always satisfactory for channels that exhibit correlation or for systems with small base-station to user antenna ratios. In this paper, we propose a linear inequality constraint quadratic programming (LICQP) model, which enables the design of a constrained DS (CDS) detector that outperforms existing DS algorithms. To further improve the performance of CDS in the presence of challenging channel conditions, we propose a universal pre-conditioner. Theoretical analysis and numerical results indicate that CDS with pre-conditioning reduces the complexity over the state-of-the-art (SOA) by $60\%$. We develop a reference Xilinx Virtex-7 FPGA design, which demonstrates that our implementation achieves $1.75\boldsymbol{\times }$ higher throughput (35 Mbps in a 128 antenna, 8 user system) at comparable hardware resource utilization. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the classical detection problem for vehicle networks with multiple antennas, by considering practical communication scenarios, where the interfering signals are correlated over time or frequency. In such cases, the conventional detector requires to estimate the joint distribution of the interfering signals, which imposes a huge computational complexity. To tackle this issue, we propose the joint use of a maximum likelihood detector (MLD) and a deep convolutional neural network (DCNN), where MLD is used to produce an initial detection result and DCNN improves the detection by exploiting the local correlation to suppress the interference. Furthermore, the improved DCNN is enhanced by devising the loss function through the cross-entropy of the detection, which can help to suppress the interfering signals and simultaneously force the residual interference to approach the Gaussian distribution. Simulation results are presented to verify the effectiveness of the proposed detector compared to the conventional one. The trained model and source code for this work are available at https://github.com/skypitcher/project_dcnnmld. [ABSTRACT FROM AUTHOR]

In this paper, deep neural network (DNN) is utilized to improve message passing detectors (MPDs) for massive multiple-input multiple-output (MIMO) systems. A general framework to construct DNN architecture for MIMO detection is first introduced by unfolding iterative MPDs. DNN MIMO detectors are then proposed based on modified MPDs including damped belief propagation (BP), max-sum (MS) BP, and simplified channel hardening-exploiting message passing (CHEMP). The correction factors are optimized via deep learning for better performance. Numerical results demonstrate that, compared with state-of-the-art (SOA) detectors including minimum mean-squared error (MMSE), BP, and CHEMP, the proposed DNN detectors can achieve better bit-error-rate (BER) and improve robustness against various antenna and channel conditions with similar complexity. The DNN is required to be trained only once and can be reused for multiple detections, which assures its high efficiency. The corresponding hardware architecture is also proposed. Implementation results with 65 nm CMOS technology approve the efficiency and flexibility of the proposed DNN framework. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is an effective way to improve the spectrum efficiency of multiple-input multiple-output (MIMO) systems, where this advancement can be fully exploited by employing a proper user selection strategy in the multiuser MIMO system. However, for multiuser scenarios, information-theoretic throughput may not be properly used as the performance indicator since the constraints at the receiver (e.g., suboptimal detectors) are not taken into consideration. In this paper, we adopt the error probability (EP) as the performance metric, which is equivalent to the practical throughput efficiency and can take the critical constraints into consideration. We analyze the pair-wise error probability (PEP) performance of different detectors and propose two kinds of user selection methods to minimize the PEP. To facilitate the detection performance evaluation, we further investigate the multiuser diversity of the proposed selection methods and derive a tight lower PEP bound for the case with randomly selected users. From the analytical and numerical results, we demonstrate that the proposed multiuser selection methods can suppress the inter-user interference effectively and achieve better performance than that of the state-of-the-art method. [ABSTRACT FROM AUTHOR]

This paper investigates the sensor selection problem in time difference of arrival (TDOA) tracking scenario to find the optimal sensor activation strategy for the upcoming time step. We propose a multiobjective optimization framework to minimize two conflicting objectives, i.e., tracking accuracy and quantity budget, which are represented by the trace of the conditional posterior Cramér Rao lower bound (CPCRLB) and the number of selected sensors, respectively. Due to the reduced measurement dimension and correlated noise caused by the common reference sensor (CRS), sensor selection algorithms for the general nonlinear model in existing literature cannot be applied to the TDOA tracking directly as they mostly assume that each sensor produces an independent measurement. Therefore, we introduce two Boolean vectors to indicate the CRS and ordinary sensors respectively. The sensor selection problem is then formulated as a multiobjective optimization problem (MOP), which can be further transformed as a single objective optimization problem (SOOP) using the linear weighted-sum method. We prove that the SOOP satisfies the rules of discrete monotonic optimization (DMO), and propose the polyblock outer approximation (POA)-based algorithms to seek for a globally optimal solution. For comparison, we introduce the conventional semidefinite program (SDP)-based algorithm to solve the SOOP with multi-step relaxation. Simulation results demonstrate that the proposed POA-based algorithms can considerably outperform the SDP-based ones in solving the optimization problem. [ABSTRACT FROM AUTHOR]

This paper investigates the symbol detection of the multiple antenna ambient backscatter communication systems. In particular, to overcome the problem of the noise uncertainty, the maximum-eigenvalue detector is derived from the general likelihood ratio test, and the approximate bit error rate (BER) performance is characterized. The analytical results show that the BER of the maximum-eigenvalue detector converges to a fixed number in large signal-to-noise ratio or number of receiving antennas $M$ regime. Finally, numerical results are presented to confirm the correctness of theoretical analysis, and reveal that the maximum-eigenvalue detector outperforms the energy detector regarding the BER performance. [ABSTRACT FROM AUTHOR]

In this paper, two priori-aided detectors are proposed for generalized space-and-frequency index modulation (GSFIM) systems with the objective of mitigating the effect of the multi-carrier frequency offset (CFO). Specifically, based on the priori information, the first- and second-order moments of the sparse GSFIM transmit signal are calculated, and the inter-carrier interference (ICI) is cancelled via soft interference cancellation. Furthermore, unlike cancellation-based detectors only treating the signals from other transmit frequency-interval as interference to be cancelled, a multipath-based iterative detector is proposed by treating all the signals from the same transmit frequency-interval as multi-path gains, yielding a favorable system performance. Simulation results demonstrate that the proposed detectors are capable of effectively mitigating the effect of multiple CFOs and of providing considerable system performance improvements at a moderate expense of the complexity. [ABSTRACT FROM AUTHOR]

This paper investigates the selection of a subset of sensors for time difference of arrival (TDOA) localization under the non-line-of-sight (NLOS) condition in wireless sensor networks (WSN). Specifically, we aim to optimize the sensor activation for the sake of minimizing the localization error when considering NLOS condition subject to energy constraints. In contrast to existing sensor selection strategies, two independent Boolean selection vectors are utilized to determine the reference sensor and other sensors simultaneously in TDOA localization. Upon presenting expressions of the Cramer-Rao lower bound (CRLB) under three different scenarios, including: 1) line-of-sight (LOS), 2) Prior statistics unknown NLOS (PSU-NLOS), 3) Prior statistics known NLOS (PSK-NLOS), the optimization problems for sensor selection are formulated to minimize the CRLB based on two independent Boolean selection vectors. Analytical scheme is developed by solving the tractable semidefinite program (SDP) problems which are converted from the original nonconvex problem. Furthermore, two low-complexity heuristic algorithms, namely best option filling (BOF) algorithm and iterative swapping greedy (ISG) algorithm, are proposed for the sake of practical implementation. Simulation results validate that the localization accuracy for sensors selected by the SDP with randomization algorithm and the ISG algorithm achieves the exhaustive search method. Additionally, these two algorithms are stable under several random sensor network geometries. [ABSTRACT FROM AUTHOR]

In this correspondence paper, a novel code-index modulation (CIM) aided quadrature spatial modulation (QSM) scheme, called CIM-QSM, is proposed for highly spectrum and energy efficient multiple-input multiple-output (MIMO) systems. The CIM-QSM scheme transmits information over three different transmit entities: modulated symbols, activated antenna indices, and spreading code indices. Hence, a higher efficiency in terms spectrum and energy use, and better error performance compared to the conventional direct sequence spread spectrum, spatial modulation (SM), QSM, and CIM aided SS (CIM-SS) schemes are obtained by the CIM-QSM technique for MIMO communication systems. Analytical expressions for the average bit error rate, energy efficiency, and throughput of the CIM-QSM system are performed and the theoretical results are validated with Monte Carlo simulation results. [ABSTRACT FROM AUTHOR]

Generalized quadrature spatial modulation (GQSM) seamlessly amalgamates the generalized spatial modulation (GSM), quadrature spatial modulation (QSM) and vertical Bell Laboratories layered space-time (V-BLAST) techniques. In contrast to traditional multiple-input multiple-output (MIMO) schemes transmitting information bits only through the constellation defined on the complex plane, GQSM transmits additional information bits implicitly by selecting the transmit antenna (TA) activation pattern. The philosophy of QSM is that of separating the indices of the real and the imaginary parts of the transmit symbols, which increases the attainable throughput. In this paper, we analyze the inherent benefits of GQSM in terms of throughput. More specifically, we first derive the achievable rate expression of the recent GQSM scheme and unveil the condition for GQSM to approach the maximum rate. Furthermore, we compare the rate of the GQSM scheme to that of other transmission schemes and reveal the conditions for the GQSM’s maximum throughput to exceed that of the benchmark schemes. Simulation results show that when the number of TAs is 40 and quadrature phase shift keying (QPSK) is adopted, the rate of the GQSM scheme may reach 150% of that of V-BLAST. Additionally, a pair of low-complexity detectors are conceived-one based on ordered successive interference cancellation (OSIC) and another one on the orthogonal matching pursuit (OMP) algorithm. [ABSTRACT FROM AUTHOR]

This paper develops three low-complexity detection schemes for the spatial modulation (SM) Multiple Input Multiple Output (MIMO) systems. We first propose a reduced complexity maximum likelihood (RC-ML) detection scheme. For the practical values of the modulation index, the proposed RC-ML method is simpler than the existing hard-limiting-based ML schemes. The two other proposals are based on a suboptimal Maximum Ratio Combining (MRC) detection method. We present a new analysis of the MRC detector in the uncorrelated Rayleigh fading channel and identify the system operational scenarios for which the MRC performance is nearly identical to the ML performance. Our proposals — a hybrid MRC-ML method and an MRC scheme with rank reduction — are inspired by this analysis. The analytical and simulation results show that the proposed MRC-based schemes can attain a near-optimal performance while reducing the complexity of the ML-based SM receiver. [ABSTRACT FROM AUTHOR]

We study packet detection in wireless networks in the presence of a practical analog automatic-gain-control (AGC) amplifier, which is adopted to scale the average power of received signals to an appropriate fixed level. However, when packets are not received, the input of AGC is pure noise, which has a small amplitude and may not be scaled to the desired level, due to the limited gain of AGC. In light of this feature, a generalized likelihood ratio test (GLRT) detector, which is termed AGC-GLRT, is proposed and formulated in this paper for packet detection. The effect of AGC on AGC-GLRT is analyzed, and it is proven that the performance of AGC-GLRT depends on the ratio of output power in hypotheses \cal H1 and \cal H0. A comparison is performed between AGC-GLRT and general GLRT (GEN-GLRT), which considers the output power of AGC in the two hypotheses, as well as some other common detectors. Results show that, in the presence of a practical AGC, AGC-GLRT outperforms GEN-GLRT and the other detectors. Moreover, as the maximum gain of AGC decreases, the ratio of the output power in the two hypotheses increases, and the performance of AGC-GLRT is improved. In addition, it is shown that AGC-GLRT is more sensitive to fading effects than the other detectors. Based on these analysis and comparisons, a general rule on AGC design is achieved to take full advantage of AGC-GLRT. [ABSTRACT FROM PUBLISHER]

In this paper, we study the tradeoff between sensing time and achievable throughput of the secondary user that employs robust eigenvalue-based spectrum sensing techniques in the presence of noise uncertainty. First, we study exact distributions of the test statistics for two types of robust eigenvalue-based sensing techniques, namely, the blind generalized likelihood ratio test (B-GLRT) detection and energy with minimum eigenvalue (EME) detection. The developed threshold setting is more accurate than benchmark methods in achieving a target constant false alarm rate (CFAR). Second, prior to the throughput analysis, the necessary asymptotic detection and false alarm probabilities under noise uncertainty are formulated for eigenvalue-based detectors such as maximum eigenvalue detection (MED) and maximum–minimum eigenvalue (MME) detection. Finally, the throughput is maximized using eigenvalue-based spectrum sensing techniques which are B-GLRT, EME, MME, and MED detectors. The results are compared with the commonly used energy detector (ED). An improved achievable throughput is obtained under low-signal-to-noise-ratio (SNR) regime by incorporating the robust eigenvalue-based techniques, which are insusceptible to noise uncertainty. [ABSTRACT FROM PUBLISHER]

Heterogeneous networking is a promising solution for improving wireless coverage, relieving overcrowded bandwidth, and enhancing system throughput for next-generation wireless networks. However, one of the main concerns to put through heterogeneous networks is the interference among overlaid cells. In this paper, we consider femtocell uplink transmissions with dynamic and short-period interference (also called shot interference) from macrocell users. It is shown that, without the information on interferer channels between femtocells and macrocells at any base station, femtocell networks suffer from severe performance degradation when they adopt orthogonal frequency-division multiple access (OFDMA) or single-carrier FDMA (SC-FDMA) techniques. To mitigate the stringent shot interference, we propose a precoded FDMA (P-FDMA) transmission with novel detectors. Our results show that the proposed designs collect multipath diversity in the presence of interference without the knowledge of interferer channels, and our designs are robust to the power levels of shot interference. Simulation results validate our theoretical findings. [ABSTRACT FROM PUBLISHER]

Vehicular sensor networks (VSNs) are composed of vehicular sensor nodes that collaboratively sample, communicate, and reconstruct the event signal at the sink node. Samples of event signals are subjected to jitter based on the propagation speed of signal and locations of vehicular sensors. In this paper, a theoretical analysis is presented to understand the effects and how to exploit the jitter in the sensed event signal for energy-efficient and reliable communication in VSNs. Results reveal that sampling jitter can be advantageous and can be exploited in developing adaptive communication techniques, which can provide significant energy conservation while maintaining reliability in VSNs. [ABSTRACT FROM PUBLISHER]

Radio tomographic imaging systems use received signal strength measurements between static wireless sensors to image the changes in the radio propagation environment in the area of the sensors, which can be used to localize a person causing the change. To date, spatial models used for such systems are set a priori and do not change. Imaging and tracking performance suffers because of the mismatch between the model and the measurements. Collecting labeled training data requires intensive effort, and the data degrade quickly as the environment changes. This paper provides a means for a radio tomographic imaging system to bootstrap to improve its spatial models using unlabeled data, iteratively improving itself over time. A collection of tracking filters are presented to improve the accuracy of image and coordinate estimates. This paper presents an online method to use these estimates to instantaneously update spatial model parameters. Further, a smoothing method is presented to fine-tune the model with a given finite latency. The development efforts are evaluated using simulations and validated with real-world experiments conducted in three different environments. With respect to another state-of-the-art radio tomographic imaging system, the results suggest that the presented system increases the median tracking accuracy by twofold in the most challenging environment and by threefold when the model parameters are trained using the smoothing method. [ABSTRACT FROM AUTHOR]

Convergence and density evolution of a low-complexity iterative multiple-input multiple-output detection based on belief propagation over a ring-type pairwise graph are presented for binary data. The detection algorithm to be considered is effectively a forward–backward recursion and was originally proposed by Yoon and Chae in a work published in 2014, in which link-level performance, computational complexity, and convergence for Gaussian input were analyzed in detail. This paper presents the convergence proof and the density evolution framework for binary input to give an asymptotic performance in terms of average signal-to-interference-plus-noise ratio and bit error rate (BER) without channel coding. The BER curve obtained via density evolution shows a good match with the simulation results for uncoded BER in the paper by Yoon and Chae verifying the effectiveness of the analysis provided and the performance of the detection algorithm. [ABSTRACT FROM AUTHOR]

Mobile crowd sensing has emerged as a novel data collection paradigm by leveraging pervasive mobile sensing devices to enable various applications. To obtain good quality of service, incentive mechanisms are indispensable for attracting enough users. Most of the existing mechanisms focus on the offline scenario in which all users submit profiles in advance. However, the online scenario often appears in the real world wherein users arrive one by one in random order. In this paper, we investigate the frugal online incentive problem based on an online auction model, where users report their strategic profiles to the crowdsourcer in an online mode, and the crowdsourcer selects users before a deadline to complete a specific number of tasks while minimizing the total payment. We design two online mechanisms, namely, Frugal-OMZ and Frugal-OMG, satisfying computational efficiency, individual rationality, truthfulness, consumer sovereignty, and constant frugality under the zero arrival–departure interval model and the general interval model, respectively. Extensive simulations verify the desirable properties of our mechanisms. [ABSTRACT FROM AUTHOR]

Massive spatial-modulation multiple-input multiple-output (SM-MIMO) with high spectrum efficiency and energy efficiency has recently been proposed for future green communications. However, in massive SM-MIMO, the optimal maximum-likelihood detector has the high complexity, whereas state-of-the-art low-complexity detectors for small-scale SM-MIMO suffer from an obvious performance loss. In this paper, by exploiting the structured sparsity of multiple SM signals, we propose a low-complexity signal detector based on structured compressive sensing (SCS) to improve the signal detection performance. Specifically, we first propose the grouped transmission scheme at the transmitter, where multiple SM signals in several continuous time slots are grouped to carry the common spatial constellation symbol to introduce the desired structured sparsity. Accordingly, a structured subspace pursuit (SSP) algorithm is proposed at the receiver to jointly detect multiple SM signals by leveraging the structured sparsity. In addition, we also propose the SM signal interleaving to permute SM signals in the same transmission group, whereby the channel diversity can be exploited to further improve signal detection performance. Theoretical analysis quantifies the gain from SM signal interleaving, and simulation results verify the near-optimal performance of the proposed scheme. [ABSTRACT FROM PUBLISHER]

In this paper, a hybrid RFID tag and reader system is proposed for wireless and batteryless automotive sensors. The hybrid RFID tag operates in dual mode, which includes conventional RFID operation, and tag active transmission. The reader provides the carrier in conventional RFID communication, using which the tag performs backscatter modulation. In tag active transmit mode, the reader does not provide any carrier, and the tag synthesizes its own carrier for communication. The hybrid tag can communicate with the reader for a range of 21 in. A commercial seat belt sensor was integrated with the tag and a working prototype of the complete system was demonstrated. Time response is a critical parameter for the whole system to work coherently and hence the operational states were modeled for time estimation and address the key parameters. Individual subsystems within the tag and reader circuits are presented in detail to capture the complete system. The proposed system can reduce the effective on-time of the reader significantly and the analysis shows that the reader transmit time can be reduced as much as 90% compared to the conventional RFID reader. The system was validated by designing, fabricating, and testing of the tag and reader circuits within a lab environment. [ABSTRACT FROM AUTHOR]