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In this paper, we investigate security and energy efficiency of multicast communication in cognitive satellite terrestrial networks. To achieve high spectrum efficiency and low interference power transmissions in the cognitive terrestrial network, the rate splitting multiple access scheme (RSMA) is employed to prompt the massive access of the secondary users. In particular, we derive analytical and asymptotic closed-form expressions for secrecy outage probability of the satellite terrestrial network with eavesdropping, interfering, and imperfect channel state information. Hence, a beamforming (BF) scheme based on RSMA for secondary network is proposed to suppress eavesdropping, mitigate interference, and enhance energy efficiency of the proposed system model. Then, we transform non-convex optimization constraints into resoluble problems utilizing Taylor series expansion and successive convex approximation. Finally, simulation results corroborate the above theoretical analysis and highlight the superiority of the designed BF scheme on security and energy efficiency. [ABSTRACT FROM AUTHOR]

Vehicle-to-everything communication (V2X) and mobile edge computing (MEC) have introduced significant technological innovations for autonomous ground vehicles (AGVs), enabling most driving task computations to be performed with edge servers with higher quality and effectively reducing computational power consumption for AGVs. For reducing more computational power consumption which can reach hundreds of watts, sending more offloading tasks to edge servers can result in high latency and congestion when the network resources are finite and even cause serious traffic accidents. Besides, in V2X network, multiple transmission paths, including vehicle-to-vehicle (V2V) links and vehicle-to-infrastructure (V2I) links, can provide more valuable resources for sending offloading tasks, but they increase the complexity of resource management. To improve the utilization efficiency of network resources and ensure low power consumption for AGVs, first, this paper analyzes mathematical models of bandwidth, delay and power consumption based on different computing modes and transmission paths. Second, we propose an allocation algorithm for network resource allocation, transmission paths and computing modes to minimize power consumption according to the states of the V2X-MEC network measured and predicted by the navigation and positioning system. Third, a traversal approach simplified by a road segmentation method is adopted to solve for the optimal parameters. Through simulations, we compare our solution against an allocation approach based on the shortest path method, minimum average delay method and game theory method to illustrate the effectiveness of our proposed method in reducing the power consumption and transmission delay of uplinks. [ABSTRACT FROM AUTHOR]

The electric vehicle technology is one of the most promising candidates to reduce fuel consumption and $\rm CO_2$ emission. Although electric vehicles have been widely promoted by governments around the world, their development is seriously hampered due to charger unavailability and range anxiety. Based on this, this paper designs an energy interaction converter between two electric vehicles, which is controlled through disturbance observer based sliding mode control algorithm. For this converter, three main demands should be satisfied, i.e., high power density, weak source and constant power load. Therein, weak source whose minimum short circuit ratio (SCR) belongs to Jia et al., 2020 and Wang et al., 2020, is always called weak grid. Firstly, the equivalent impedance switching process is introduced to eliminate the impact of weak source. Meanwhile, the equivalent six channel interleaved floating dual boost converter is chosen to satisfy the high power density demand, whose generalized state-space function is further built to provide an indispensable preprocessing for following controller design. Moreover, in order to solve the problem regarding low frequency/sub-synchronous oscillation caused through constant power load feature regarding the energy consumption vehicle and weak source feature regarding the energy supply vehicle, a disturbance observer based sliding mode control algorithm is proposed through using generalized state-space function to provide standard DC power with both constant voltage and power. Furthermore, the proportional-resonant controller is proposed to solve the current sharing problem among six parallel channels, which reduces the heat loss and improves the service life of the device. Finally, simulation and experimental results verify the high performance of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

Ambient backscatter communication (AmBC), a promising solution to future Internet of Things (IoT), has attracted increasing attention from both academia and industry in recent years. AmBC systems enable communications between RF-powered devices (e.g., tags and sensors) and AmBC receivers by utilizing ambient RF source signals from existing communication systems. Thus it offers a low-cost and energy-efficient means of communications for future IoT applications. In the current AmBC research, the impact of using channel coding on existing primary networks in terms of the bit-error-rate (BER) performance of the AmBC systems are mostly ignored. Low-density parity-check (LDPC) codes that offer fast encoding and decoding process have been proposed as promising channel codes in the fifth-generation (5 G) networks. In this paper, we investigate the BER performance of an AmBC system that uses LDPC-coded RF source signals. We have analytically derived the BER upper and lower bounds of both the RF source and tag signals for our proposed AmBC system. Simulation results show that the BERs of both signals decrease significantly, compared to conventional AmBC systems with uncoded RF source signals. [ABSTRACT FROM AUTHOR]

Terahertz (THz) transmission technologies constitute a promising candidate for supporting ultra-broadband short-range next generation communications. Hence, we analyse the performance of unmanned aerial vehicle (UAV) in the THz networks. The coverage probability is derived as well as the area spectral efficiency (ASE) and a pair of Line-of-sight (LoS)/non-line-of-sight (NLoS) probability models, namely the macrocell LoS/NLoS probability model and picocell LoS/NLoS probability model are adopted. Furthermore, the lower-bound of the network performance are derived via homogeneous Poisson point process (HPPP) analysis, as well as the upper-bound. The simulation results match the analytical results well, which show that the coverage probability of the network first increases upon increasing the THz UAV BS density, and then decreases beyond the maximum. Given the severe path loss experienced by THz signals, a higher UAV density is required for a certain coverage probability than at lower carrier frequencies. [ABSTRACT FROM AUTHOR]

Automatic vehicle detection from a video synthetic aperture radar (VideoSAR) system presents significant potential to enhance the surveillance performance in dynamic region of interest (DROI). In this paper, a novel VideoSAR low-rank plus sparse decomposition (LRSD) perspective for single-channel single-pass configuration is proposed to track the ground defocusing vehicles. Vehicle imaging features with 2-D motion parameters are derived theoretically by exploiting a priori knowledge of polar format algorithm (PFA). In accordance with the revealed characteristics, a vision-based VideoSAR-LRSD algorithm, called three-term decomposition (TTD) with proximal exchange-based alternating directions method of multipliers (PEADMM), is then proposed to improve the performance of vehicle detection. It can be used to break the limitation for the application of emergency response not permitting the acquisition of multi-channel or multi-pass data. We comprehensively demonstrate using extensive VideoSAR DROI experiments that in comparison with the state-of-the-art algorithms, TTD-PEADMM algorithm presents the improved accuracy and is able to offer competitive results. [ABSTRACT FROM AUTHOR]

Considering the high cost and complexity of multiple-channel blind source separation, single-channel solutions have elicited a significant amount of interest in source separation. Joint maximum likelihood sequence estimation implemented by the Viterbi algorithm (VA) is a seemingly ultimate solution to the problem of data transmission over dispersive channels. However, both joint reduced-state sequence estimation (JRSSE) and joint delayed-decision feedback sequence estimation (JDDFSE) have numerous shortcomings when applied to single-channel blind signal separation. Hence, joint double-direction DDFSE (JDD-DDFSE) is proposed in this article. JDD-DDFSE employs joint VA (JVA) of the low-order channel twice. The estimated symbols from the initial JVA are utilized to equalize the causal and non-causal taps of the channel beyond the trellis state in the second JVA. A method that can derive the log likelihood ratio of the estimated symbols is provided; the complexity of this method is considerably lower than that of soft-output VA or the BCJR algorithm. Hence, the entire computation complexity of the iterative separation structure basically remains unchanged compared with that of the no-iteration structure. For the non-causal channel, the proposed algorithm has the lowest computation complexity and demonstrates a separation performance similar to that of conventional JVA, JRSSE, and JDDFSE algorithms. [ABSTRACT FROM AUTHOR]

The severe bandwidth shortage in conventional microwave bands has spurred the exploration of the millimeter wave (MMW) spectrum for the next revolution in wireless communications. However, there is still a lack of proper channel modeling for the MMW wireless propagation, particularly in the case of outdoor environments. In this paper, we develop a geometry-based stochastic channel model to statistically characterize the effect of all the first-order reflection paths between the transmitter and the receiver. These first-order reflections are generated by the single bounce of signals reflected from the walls of randomly distributed buildings. Based on this geometric model, a closed-form expression for the power delay profile contributed by all the first-order reflection paths is obtained and then used to evaluate their impact on the MMW outdoor propagation characteristics. Numerical results are provided to validate the accuracy of the proposed model under various channel parameter settings. The findings in this paper provide a promising step toward more complex and practical MMW propagation channel modeling. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the low-cost design for point-to-point millimeter-wave (mmW) multiple-input–multiple-output (MIMO) systems in rainfall environments. We first show that rain does not always have a negative impact on system performance, provided that accurate instantaneous channel state information (CSI) is available at both the transmitter and receiver. In addition, we propose a practical and low-cost transmission scheme, referred to as hardware-constrained statistical precoding (HC-SP), for mmW MIMO systems in rainfall environments. This scheme only requires statistical channel information at the transmitter and takes into consideration the hardware constraints on transceiver imposed by the high radio-frequency chain cost. Simulation results demonstrate that the HC-SP scheme allows the mmW MIMO system to have near-optimal performance. [ABSTRACT FROM AUTHOR]

This paper presents a design methodology for the development of self-powered active lateral secondary suspensions for rail vehicles. It first investigates the energy flow in the active lateral secondary suspensions and analyzes the conditions for self-powered control in detail. The impact of the controller design on both ride quality and energy consumption is then used to guide the design/specification of actuators and to define key actuator parameters to achieve both expected performance improvement and zero-energy consumption for the actuators. Furthermore, a control strategy for dealing with larger-than-expected energy consumption by the active suspensions is proposed not only to eliminate excessive power requirements but to ensure ride quality improvement, in comparison to that by passive suspensions, as well. Computer simulations are used to validate the control strategy for the self-powered active suspension. [ABSTRACT FROM AUTHOR]

It is known that in addition to spectrum sparsity, spatial sparsity can also be used to further enhance spectral utilization in cognitive radio systems. To achieve that, secondary users (SUs) must know the locations and signal strength distributions (SSDs) of primary users' base stations (PUBSs). Recently, a group sparse total least squares method was developed to cooperatively sense the PUBSs' signal strength and estimate their locations. It approximates PUBSs' power decay with a path loss model (PLM), assumes PUBSs' locations on some grid points, and then accomplishes the estimation tasks. However, the parameters of the PLM have to be known in advance, and the accuracy of the location estimation is bounded by the resolution of the grid points, which limit its practical applications. In this paper, we propose a sparse Bayesian learning method to solve the problems. We use a Laplacian function to model the power decay of a PUBS and then derive learning rules to estimate corresponding parameters. The distinct features of the proposed method are that most parameters are adaptively estimated, and little prior information is needed. To further enhance the performance, we incorporate source number detection methods in the proposed algorithm such that the number of the PUBSs can be precisely detected, facilitating the estimation of PUBSs' locations and SSDs. Moreover, the proposed algorithm is modified into a recursive mode to adapt to SUs' mobility and time-variant observations. Simulations show that the proposed algorithm has good performance, even when the spatial measurement rate is low. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the efficient compression and feedback of channel state information (CSI) in downlink multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. Inspired by video coding, we propose a novel CSI compression and feedback scheme, referred to as hybrid transform coding (HTC). HTC consists of two coding types, i.e., selective time-domain coding (STDC) and differential time-domain coding (DTDC), which are adopted to exploit the correlation of CSI in both the frequency and time domains. We first develop closed-form expressions for the overhead–distortion performance of these two coding types in HTC. The parameters involved in HTC are then optimized based on the analytical results. Finally, the system-level performance of HTC is evaluated in both maximum eigenmode beamforming (MEB)-based single-user MIMO (SU-MIMO) and zero-forcing beamforming (ZFBF)-based multiuser MIMO (MU-MIMO) under Long Term Evolution Advanced (LTE-A) Release 10-based cellular networks. Simulation results show that HTC can significantly outperform the available alternative. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the performance analysis of the maximum eigenmode beamforming (MEB) scheme with equal power allocation (EPA) over practically coded multiple-input multiple-output (MIMO) multiple-access channels (MACs). Both the scenarios of perfect and imperfect channel state information (CSI) at the transmitters (CSIT) are considered. Analytical and numerical results show that MEB with EPA can achieve significant power savings and is less sensitive to CSIT error than other alternatives. [ABSTRACT FROM PUBLISHER]