Showing papers by "Penn State College of Communications published in 2021"
TL;DR: This article proposes a beamforming (BF) scheme for a cognitive satellite terrestrial network, where the base station and a cooperative terminal are exploited as green interference resources to enhance the system security performance in the presence of unknown eavesdroppers.
Abstract: This article proposes a beamforming (BF) scheme for a cognitive satellite terrestrial network, where the base station (BS) and a cooperative terminal (CT) are exploited as green interference resources to enhance the system security performance in the presence of unknown eavesdroppers. Different from the related works, we assume that only imperfect channel information of the mobile user (MU) and earth station (ES) is available. Specifically, we formulate an optimization problem with the objective to degrade the possible wiretap channels within the private signal beampattern region, while imposing constraints on the signal-to-interference-plus-noise ratio (SINR) at the MU, the interference level of the ES and the total transmit power budget of the BS and CT. To solve this mathematically intractable problem, we propose a joint artificial noise generation and cooperative jamming BF scheme to suppress the interception. Finally, the effectiveness and superiority of the proposed BF scheme are confirmed through computer simulations.
83 citations
TL;DR: Zhang et al. as discussed by the authors proposed an auction-based market model for incentivizing data owners to participate in federated learning and designed an approximate strategy-proof mechanism which guarantees the truthfulness, individual rationality, and computational efficiency.
Abstract: In traditional machine learning, the central server first collects the data owners’ private data together and then trains the model. However, people’s concerns about data privacy protection are dramatically increasing. The emerging paradigm of federated learning efficiently builds machine learning models while allowing the private data to be kept at local devices. The success of federated learning requires sufficient data owners to jointly utilize their data, computing and communication resources for model training. In this article, we propose an auction-based market model for incentivizing data owners to participate in federated learning. We design two auction mechanisms for the federated learning platform to maximize the social welfare of the federated learning services market. Specifically, we first design an approximate strategy-proof mechanism which guarantees the truthfulness, individual rationality, and computational efficiency. To improve the social welfare, we develop an automated strategy-proof mechanism based on deep reinforcement learning and graph neural networks. The communication traffic congestion and the unique characteristics of federated learning are particularly considered in the proposed model. Extensive experimental results demonstrate that our proposed auction mechanisms can efficiently maximize the social welfare and provide effective insights and strategies for the platform to organize the federated training.
65 citations
TL;DR: Research on political misinformation is booming as mentioned in this paper, and the field is continually gaining more key insights about this important and complex social problem, and academic interest on misinformation has been consiste...
Abstract: Research on political misinformation is booming. The field is continually gaining more key insights about this important and complex social problem. Academic interest on misinformation has consiste...
64 citations
TL;DR: This article addresses the security problem for wireless powered cognitive satellite-terrestrial network, where a multibeam satellite sub-network shares the portion of millimeter wave bands with multiple cellular networks, each consisting of a base station, several mobile users (MUs) and energy receivers (ERs).
Abstract: This article addresses the security problem for wireless powered cognitive satellite-terrestrial network, where a multibeam satellite sub-network shares the portion of millimeter wave bands with multiple cellular networks, each consisting of a base station, several mobile users (MUs) and energy receivers (ERs). Considering that the ERs are potential eavesdroppers of the MUs, and only imperfect knowledge of the angles of departure for the wiretap channels is available, we aim at maximizing aggregated rate of the considered network while guaranteeing the signal-to-interference-plus-noise ratio requirements of the MUs, the energy harvesting thresholds and the secrecy constraints at ERs. Since the formulated optimization problem is mathematically intractable, we exploit a discretization method and the Taylor expansion method to transform the non-convex objective and constraints into convex ones, and then propose an iterative beamforming (BF) algorithm to solve the problem. Furthermore, we present a combined multibeam scheme to obtain suboptimal BF weight vectors with low computational burden. Finally, simulation results reveal that the proposed BF schemes can efficiently improve the aggregated rate with fast convergence compared to the benchmark schemes.
57 citations
TL;DR: A cooperative reconnaissance and spectrum access scheme for task-driven heterogeneous coalition-based UAV networks by jointly optimizing task layer and resource layer and a joint bandwidth allocation and coalition formation (JBACF) algorithm to achieve stable coalition partition.
Abstract: Coalition structure is an efficient networking architecture for task implementation in unmanned aerial vehicle (UAV) networks. However, both the formation of coalition and the spectrum resource for intra-coalition communication affect the reconnaissance performance. In this paper, we investigate a cooperative reconnaissance and spectrum access (CRSA) scheme for task-driven heterogeneous coalition-based UAV networks by jointly optimizing task layer and resource layer. Specifically, coalition formation game (CFG) is formulated to jointly optimize task selection and bandwidth allocation. In addition to the traditional Pareto order and selfish order, coalition expected altruistic order maximizing coalitions’ utility is proposed. The CFG under the proposed order is proved to be an exact potential game (EPG). Then the existence of stable coalition partition is guaranteed with the help of Nash equilibrium (NE). We propose a joint bandwidth allocation and coalition formation (JBACF) algorithm to achieve stable coalition partition wherein an efficient gradient projection (GP) based method is applied to solve bandwidth allocation. The effectiveness of the proposed scheme and algorithms are demonstrated through in-depth numerical simulations. The results show that our proposed CRSA scheme is superior to non-joint optimization scheme. Also, the proposed order is superior to traditional Pareto order and selfish order.
51 citations
DOI•
13 Nov 2021TL;DR: In this paper, the authors provide a tutorial overview on how to efficiently design IRS-aided WET systems as well as IRSaided systems with both wireless information and power transfer (SWIPT) and wireless powered communication network (WPCN).
Abstract: Intelligent reflecting surface (IRS) is a promising technology for achieving spectrum and energy-efficient wireless networks cost-effectively. Most existing works on IRS have focused on exploiting IRS to enhance the performance of wireless communication or wireless information transmission (WIT), while its potential for boosting the efficiency of radio frequency (RF) wireless energy transmission (WET) still remains largely open. Although IRS-aided WET shares similar characteristics with IRS-aided WIT, they differ fundamentally in terms of design objective, receiver architecture, practical constraints, and so on. In this article, we provide a tutorial overview on how to efficiently design IRS-aided WET systems as well as IRS-aided systems with both WIT and WET, namely, IRS-aided simultaneous wireless information and power transfer (SWIPT) and IRS-aided wireless powered communication network (WPCN), from a communication and signal processing perspective. In particular, we present state-of-the-art solutions to tackle the unique challenges in operating these systems, such as IRS passive reflection optimization, channel estimation, and deployment. In addition, we propose new solution approaches and point out important directions for future research and investigation.
47 citations
TL;DR: This letter proposes a LOS/NLOS identification method based on Morlet wave transform and convolutional neural networks (MWT-CNN), capable of identifying LOS /NLOS in the time-frequency domain and results show that MWT- CNN is more suitable to be deployed in static scenarios.
Abstract: In indoor ultra-wideband (UWB) positioning systems, positioning accuracy can be improved by determining the conditions of line-of-sight (LOS) and non-line-of-sight (NLOS) propagation and taking appropriate measures. The existing methods, such as support vector machine (SVM), decision tree (DT), k-Nearest Neighbor (KNN), identify LOS/NLOS mainly using time-domain characteristics. However, using only time-domain characteristics cannot achieve satisfactory performance. In this letter, we propose a LOS/NLOS identification method based on Morlet wave transform and convolutional neural networks (MWT-CNN). MWT-CNN is capable of identifying LOS/NLOS in the time-frequency domain. Our simulations are based on the 802.15.4a UWB model and an open-source dataset. The simulation results show that MWT-CNN achieves an accuracy of 100% in the office scenario, 99.89% in the industrial scenario, 96.10% in the residential scenario, and 98.84% in a real experimental scenario. Further simulation results show that MWT-CNN is more suitable to be deployed in static scenarios.
45 citations
TL;DR: In this paper, a new application of severely occluded vehicle detection in the complex wild background of weak infrared camera aerial images was introduced, in which more than 50% area of the vehicles are occlated.
Abstract: Infrared small target detection is still a challenge in the field of object detection. At present, although there are many related research achievements, it surely needs further improvement. This paper introduced a new application of severely occluded vehicle detection in the complex wild background of weak infrared camera aerial images, in which more than 50% area of the vehicles are occluded. We used YOLOv4 as the detection model. By applying secondary transfer learning from visible dataset to infrared dataset, the model could gain a good average precision (AP). Firstly, we trained the model in the UCAS_AOD visible dataset, then, we transferred it to the VIVID visible dataset, finally we transferred the model to the VIVID infrared dataset for a second training. Meanwhile, added the hard negative example mining block to the YOLOv4 model, which could depress the disturbance of complex background thus further decrease the false detecting rate. Through experiments the average precision improved from90.34% to 91.92%, the F1 score improved from 87.5% to 87.98%, which demonstrated that the proposed algorithm generated satisfactory and competitive vehicle detection results.
41 citations
TL;DR: This article proposes a better reply-based distributed multiuser computation task offloading algorithm (BR-DMCTO) and results show that the proposed offloading mechanism can improve the benefit of users, and verify the effectiveness and convergence of the proposed algorithm.
Abstract: The emergence of intelligent applications produces the demand for computing. How to reduce the computation pressure in mobile edge computing (MEC) under massive computation demand is an urgent problem to solve. Specifically, the allocation of heterogeneous resources including communication resources and computing resources needs to be optimized simultaneously. From the perspective of joint optimization of channel allocation, device-to-device (D2D) pairing, and offloading mode, this paper studies the multi-user computing task offloading problem in device-enhanced MEC. The objective is maximizing the aggregate offloading benefits, i.e., the tradeoff between delay and energy consumption, of all compute-intensive users in the network. By introducing game theory, the problem is modeled as a multi-user computation task offloading game, which is proved to be an exact potential game (EPG) with at least one pure-strategy Nash equilibrium (NE) solution. In order to find a desirable solution, this paper proposes a better reply based distributed multi-user computation task offloading algorithm (BR-DMCTO). Simulation results show that the proposed offloading mechanism can improve the benefit of users, and verify the effectiveness and convergence of the proposed algorithm.
40 citations
TL;DR: Simulation results show that CIM has better detection of GNSS fault than traditional receiver autonomous integrity monitoring (RAIM) and can be applied to many existing multi-sensor cooperative positioning algorithms.
Abstract: A cooperative integrity monitoring (CIM) algorithm is proposed in this work. Under the CIM architecture, the algorithm can fully exploit the global navigation satellite system (GNSS) data and inter-vehicle measurements data to improve the detection and isolation of faulty measurements due to multipath or non line of sight (NLOS). Taking the advantages of cooperative scheme, a residual decomposition method is used to model the measurement errors into common and specific parts, a greedy search strategy is used to exclude the faulty measurements based on its sub-statistics. Simulation results show that CIM has better detection of GNSS fault than traditional receiver autonomous integrity monitoring (RAIM). Also, CIM is capable of detecting the faulty outliers in inter-vehicle measurements. The results indicate that CIM can be applied to many existing multi-sensor cooperative positioning algorithms.
35 citations
TL;DR: A new framework to jointly characterize covertness and timeliness of short-packet communications is developed, in which a new metric named covert age of information (CAoI) is first proposed and then a closed-form expression for the average CAoI is derived.
Abstract: In this letter, we develop a new framework to jointly characterize covertness and timeliness of short-packet communications, in which a new metric named covert age of information (CAoI) is first proposed and then a closed-form expression for the average CAoI is derived. Our examination explicitly reveals the tradeoff between communication covertness and timeliness affected by the block-length, transmit power and prior transmission probability. Multiple transmission designs are tackled in order to minimize the average CAoI subject to covertness constraint, where the resultant differences relative to the designs with traditional metrics (e.g., effective covert rate, AoI) as objective functions are clarified. Our examination demonstrates that the optimal block-length is not the largest one, which is optimal in delay-constrained covert communication without considering the communication timeliness, and the optimal prior transmission probability may not be 1/2, which has been widely assumed in the literature of covert communications.
TL;DR: This letter investigates sum rate maximization problem with transmit power, quality of service (QoS) constraints, imperfect successive interference cancelation (SIC) and imperfect channel state information (CSI) considered, and proposes an iterative algorithm to get the optimal sum rate.
Abstract: In this letter, we consider non-orthogonal multiple access (NOMA) applying into massive multiple input multiple output (mMIMO) low earth orbit (LEO) satellite communication system (SCS) to improve spectral efficiency. Specifically, we investigate sum rate maximization problem with transmit power, quality of service (QoS) constraints, imperfect successive interference cancelation (SIC) and imperfect channel state information (CSI) considered. We decouple the original problem into two parts: precoding vectors design and transmit power optimization. Precoding vectors are derived for maximizing the average signal power in each beam and eliminating inter-beam interference (IBI). Then, transmit power optimization problem is transformed into a convex optimization problem by utilizing the first order Taylor expansion, an iterative algorithm is proposed to get the optimal sum rate. Finally, simulation results verify the convergence of the proposed algorithm and our proposed mMIMO NOMA approach is better than other approaches.
TL;DR: A deep learning (DL)-based CSI prediction scheme is proposed to address channel aging problem by exploiting the correlation of changing channels and numerical results demonstrate that the proposed DL-based predictor can mitigate the channel Aging problem in LEO satellite mMIMO system effectively.
Abstract: Low Earth orbit (LEO) satellite is one of the most promising infrastructures for realizing next-generation global wireless networks with enhanced data rates. Applying massive multiple-input multiple-output (mMIMO) to LEO satellite communication systems is a novel idea to enhance communication capacity and realize the global high-speed interconnection. However, obtaining effective instantaneous channel state information (iCSI) is challenging due to the time-varying propagation environment and long transmission delay. In this letter, a deep learning (DL)-based CSI prediction scheme is proposed to address channel aging problem by exploiting the correlation of changing channels. Specifically, we design a satellite channel predictor (SCP) that is composed by long short term with memory (LSTM) units. The predictor is first trained by offline learning and then feeds back the corresponding output results online based on the input data to realize channel feature extraction and future CSI prediction in LEO satellite scenarios. Numerical results demonstrate that the proposed DL-based predictor can mitigate the channel aging problem in LEO satellite mMIMO system effectively.
02 Feb 2021
TL;DR: In this paper, a joint multi-agent anti-jamming algorithm (JMAA) is proposed to obtain the optimal antijamming strategy in intelligent multi-channel blocking jamming environment.
Abstract: In this article, we investigate intelligent anti-jamming communication method for wireless sensor networks. The stochastic game framework is introduced to model and analyze the multi-user anti-jamming problem, and a joint multi-agent anti-jamming algorithm (JMAA) is proposed to obtain the optimal anti-jamming strategy. In intelligent multi-channel blocking jamming environment, the proposed JMAA adopts multi-agent reinforcement learning to make online channel selection, which can effectively tackle the external malicious jamming and avoid the internal mutual interference among sensor nodes. The simulation results show that, the proposed JMAA is superior to the frequency-hopping method, the sensing-based method and the independent reinforcement learning. Specifically, the proposed JMAA has the higher average packet receive ratio than both the frequency-hopping method and the sensing-based method. Compared with the independent reinforcement learning, JMAA has faster convergence rate when reaching the same performance of average packet receive ratio. In addition, since the JMAA does not need to model the jamming patterns, it can be widely used for combating other malicious jamming such as sweep jamming and probabilistic jamming.
TL;DR: In this article, the authors considered covert communications for bistatic backscatter systems, where a tag transmits information passively to a reader by reflecting incident carrier signals with artificial noise (AN) generated from a dedicated carrier emitter under the supervision of a warden.
Abstract: In this paper, we consider covert communications for bistatic backscatter systems, where a tag transmits information passively to a reader by reflecting incident carrier signals with artificial noise (AN) generated from a dedicated carrier emitter (CE) under the supervision of a warden. By exploiting the channel uncertainty introduced by CE, we analytically derive the warden's minimum sum of error probabilities to evaluate communication covertness. We find that communication covertness can be improved by reducing the tag's reflection coefficient, but is not affected by the CE's transmit power. Therefore, in order to achieve energy-efficient design, we optimize the tag's reflection coefficient to minimize the CE's transmit power under the constraints of communication covertness and reliability. Our analysis shows that the minimum CE's transmit power can be further reduced with a minor loss of the achievable covert rate.
TL;DR: In this paper, the authors considered a new application scenario for the intelligent reflecting surface (IRS) aided cell-free massive MIMO system where multiple APs serve several users through an AIRS.
Abstract: The intelligent reflecting surface (IRS) is considered a core technology of next-generation mobile communication. It has significant advantages in enhancing network coverage, spectrum efficiency, energy efficiency, and deployment cost. Compared with the conventional massive multiple-input-multiple-output (MIMO) system, cell-free massive MIMO overcomes the limitation imposed by inter-cell interference in traditional cellular mobile networks and realizes coherent transmission centered on users. In this article, we consider a new application scenario for the IRS — an aerial IRS (AIRS)-aided cell-free massive MIMO system where multiple APs serve several users through an AIRS. The users are in a “shadow area” where we cannot provide good quality of service (QoS) due to the remote location and the shelter of tall buildings. Our goal is to optimize the power allocation and beamforming of each AP, the placement and reflection phase shift parameters of the AIRS to maximize the user’s achievable rate. Firstly, we consider the optimization for fixed placement of the AIRS, where we propose a joint optimization strategy to maximize the achievable rate of the user. Then, we propose a fast optimal location search algorithm base on the path loss model to determine the optimal location of the AIRS and decrease the computational complexity. The simulation results show that the proposed methods can improve the performance for the achievable rate of the system. To the best of our knowledge, this article is the first to study the application scenario for the combination of IRS technology and a cell-free massive MIMO system.
TL;DR: In this paper, an anchor-assisted channel estimation approach was proposed to solve the problem of channel training overhead in the RIS-aided wireless communication. But the channel estimation is not a practical problem for intelligent reflecting surface (IRS) aided wireless communication, and it is not suitable for large number of antennas at the BS.
Abstract: Channel estimation is a practical challenge for intelligent reflecting surface (IRS) aided wireless communication. As the number of IRS reflecting elements or IRS-aided users increases, the channel training overhead becomes excessively high, which results in long delay and low throughput in data transmission. To tackle this challenge, we propose in this paper a new anchor-assisted channel estimation approach, where two anchor nodes, namely A1 and A2, are deployed near the IRS for facilitating its aided base station (BS) in acquiring the cascaded BS-IRS-user channels required for data transmission. Specifically, in the first scheme, the partial channel state information (CSI) on the element-wise channel gain square of the common BS-IRS link for all users is first obtained at the BS via the anchor-assisted training and feedback. Then, by leveraging such partial CSI, the cascaded BS-IRS-user channels are efficiently resolved at the BS with additional training by the users. While in the second scheme, the BS-IRS-A1 and A1-IRS-A2 channels are first estimated via the training by A1. Then, with additional training by A2, all users estimate their individual cascaded A2-IRS-user channels simultaneously. Based on the CSI fed back from A2 and all users, the BS resolves the cascaded BS-IRS-user channels efficiently. In both schemes, the channels among the fixed BS, IRS, and two anchors are estimated in a large timescale, which greatly reduces the real-time training overhead. Simulation results demonstrate that our proposed anchor-assisted channel estimation schemes achieve superior performance as compared to existing IRS channel estimation schemes, under various practical setups. In addition, the first proposed scheme outperforms the second one when the number of antennas at the BS is sufficiently large, and vice versa.
TL;DR: A joint subcarrier assignment and cooperative NOMA pairing (JSACNP) approach is proposed to minimize the max completion time in terrestrial networks by utilizing matching theory, and the optimal linear receiver expression is derived with fixed transmit power.
Abstract: In this article, we investigate max completion time optimization for Internet of Things (IoT) in LEO satellite-terrestrial integrated networks (STINs), in which IoT devices use non-orthogonal multiple access (NOMA) scheme to transmit data to central earth stations (CESs), and orthogonal multiple access (OMA) scheme is used for data transmission from CESs to LEO satellite. We decouple this problem into two subproblems: 1) max completion time optimization in terrestrial networks and 2) max completion time optimization among satellite beams. Different from the existing works about NOMA data transmission in terrestrial networks, we propose a cooperative NOMA scheme, and derive the closed expressions of the optimal cooperative data and the optimal transmit power of IoT devices. Based on the closed-form expressions, a joint subcarrier assignment and cooperative NOMA pairing (JSACNP) approach is proposed to minimize the max completion time in terrestrial networks by utilizing matching theory. Then, to minimize the max completion time among satellite beams, the optimal linear receiver expression is derived with fixed transmit power. Convex optimization is utilized to solve transmit power optimization, we propose an algorithm to solve it by CVX tool. An iterative algorithm is proposed for improved performance. Finally, numerical results are provided to evaluate our proposed algorithms, compared with some other proposed approaches or algorithms.
TL;DR: This paper jointly optimize the service placement, UAV movement trajectory, task scheduling, and computation resource allocation, to minimize the overall energy consumption of all terrestrial user equipments (UEs) and proposes two alternating optimization-based suboptimal solutions with different time complexities.
Abstract: Unmanned aerial vehicle (UAV)-enabled mobile edge computing has been recognized as a promising technology to flexibly and efficiently handle computation-intensive and latency-sensitive tasks in the era of fifth generation (5G) and beyond. In this paper, we study the problem of Service Provisioning for UAV-enabled mobile edge computiNg (SPUN). Specifically, under task latency requirements and various resource constraints, we jointly optimize the service placement, UAV movement trajectory, task scheduling, and computation resource allocation, to minimize the overall energy consumption of all terrestrial user equipments (UEs). Due to the non-convexity of the SPUN problem as well as complex coupling among mixed integer variables, it is a non-convex mixed integer nonlinear programming (MINLP) problem. To solve this challenging problem, we propose two alternating optimization-based suboptimal solutions with different time complexities. In the first solution with relatively high complexity in the worst case, the joint service placement and task scheduling subproblem, and UAV trajectory subproblem are iteratively solved by the Branch and Bound (BnB) method and successive convex approximation (SCA), respectively, while the optimal solution to the computation resource allocation subproblem is efficiently obtained in the closed form. To avoid the high complexity caused by BnB, in the second solution, we propose a novel approximation algorithm based on relaxation and randomized rounding techniques for the joint service placement and task scheduling subproblem, while the other two subproblems are solved in the same way as that of the first solution. Extensive simulations demonstrate that the proposed solutions achieve significantly lower energy consumption of UEs compared to three benchmarks.
TL;DR: In this paper, the resource management problem for large-scale UAV communication networks from game-theoretic perspective was investigated from a distributed and autonomous manner by exploring the inherent features, the distinctive challenges, and the broad application prospects.
Abstract: As a result of rapid development in electronics and communication technology, large-scale unmanned aerial vehicles (UAVs) are harnessed for various promising applications in a coordinated manner Although it poses numerous advantages, resource management among various domains in large-scale UAV communication networks is the key challenge to be solved urgently Specifically, due to the inherent requirements and future development trend, distributed resource management is suitable In this article, we investigate the resource management problem for large-scale UAV communication networks from game-theoretic perspective which are exactly coincident with the distributed and autonomous manner By exploring the inherent features, the distinctive challenges are discussed Then, we explore several game-theoretic models that not only combat the challenges but also have broad application prospects We provide the basics of each game-theoretic model and discuss the potential applications for resource management in large-scale UAV communication networks Specifically, mean-field game, graphical game, Stackelberg game, coalition game and potential game are included After that, we propose two innovative case studies to highlight the feasibility of such novel game-theoretic models Finally, we give some future research directions to shed light on future opportunities and applications
TL;DR: Simulation results validate the effectiveness and fairness of the proposed auction-based approach as well as the superiority of the NOMA scheme in secondary relays selection and the influence of key factors on the performance of the proposal is analyzed in detail.
Abstract: In this article, we investigate the multichannel cooperative spectrum sharing in hybrid satellite-terrestrial Internet of Things (IoT) networks with the auction mechanism, which is designed to reduce the operational expenditure of the satellite-based IoT (S-IoT) network while alleviating the spectrum scarcity issues of terrestrial-based IoT (T-IoT) network. The cluster heads of selected T-IoT networks assist the primary satellite users transmission through cooperative relaying techniques in exchange for spectrum access. We propose an auction-based optimization problem to maximize the sum transmission rate of all primary S-IoT receivers with the appropriate secondary network selection and corresponding radio resource allocation profile by the distributed implementation while meeting the minimum transmission rate of secondary receivers of each T-IoT network. Specifically, the one-shot Vickrey–Clarke–Groves (VCG) auction is introduced to obtain the maximum social welfare, where the winner determination problem is transformed into an assignment problem and solved by the Hungarian algorithm. To further reduce the primary satellite network decision complexity, the sequential Vickrey auction is implemented by sequential fashion until all channels are auctioned. Due to incentive compatibility with those two auction mechanisms, the secondary T-IoT cluster yields the true bids of each channel, where both the nonorthogonal multiple access (NOMA) and time division multiple access (TDMA) schemes are implemented in cooperative communication. Finally, simulation results validate the effectiveness and fairness of the proposed auction-based approach as well as the superiority of the NOMA scheme in secondary relays selection. Moreover, the influence of key factors on the performance of the proposed scheme is analyzed in detail.
TL;DR: A blockchain-based collaborative crowdsensing (BCC) scheme to support secure and efficient vehicular crowdsensing in AVNs is developed and results show that the scheme can lead to a lower TEC for completing crowdsensing tasks and bring higher rewards to ECDs than the conventional schemes.
Abstract: The vehicular crowdsensing, which benefits from edge computing devices (ECDs) distributedly selecting autonomous vehicles (AVs) to complete the sensing tasks and collecting the sensing results, represents a practical and promising solution to facilitate the autonomous vehicular networks (AVNs). With frequent data transaction and rewards distribution in the crowdsensing process, how to design an integrated scheme which guarantees the privacy of AVs and enables the ECDs to earn rewards securely while minimizing the task execution cost (TEC) therefore becomes a challenge. To this end, in this paper, we develop a blockchain-based collaborative crowdsensing (BCC) scheme to support secure and efficient vehicular crowdsensing in AVNs. In the BCC, by considering the potential attacks in the crowdsensing process, we first develop a secure crowdsensing environment by designing a blockchain-based transaction architecture to deal with privacy and security issues. With the designed architecture, we then propose a coalition game with a transferable reward to motivate AVs to cooperatively execute the crowdsensing tasks by jointly considering the requirements of the tasks and the available sensing resources of AVs. After that, based on the merge and split rules, a coalition formation algorithm is designed to help each ECD select a group of AVs to form the optimal crowdsensing coalition (OCC) with the target of minimizing the TEC. Finally, we evaluate the TEC of the task and the rewards of the ECDs by comparing the proposed scheme with other schemes. The results show that our scheme can lead to a lower TEC for completing crowdsensing tasks and bring higher rewards to ECDs than the conventional schemes.
TL;DR: In this article, the authors proposed an online rolling traffic flow prediction method for edge computing-enhanced autonomous and connected vehicles (CAVs), which can learn low-dimensional embeddings in the online setting and impute missing ones simultaneously.
Abstract: The development of edge computing based autonomous and connected vehicles (CAVs) provides a very promising solution for the construction of intelligent transportation system Unfortunately, the existing methods are difficult to predict traffic flow accurately in such case due to not only the dynamic nature of the CAVs but also the considerable amount of missing traffic flow data Based on this, we propose an online rolling traffic flow prediction method to provide support for the CAVs can be seen into practice The new matrix factorization techniques proposed can learn the low-dimensional embeddings in the online setting and impute missing ones simultaneously Moreover, instead of directly predicting the high-dimensional traffic flow data, a standard vector autoregressive (VAR) process is employed on low-dimensional embeddings to predict future values Further, a multidimensional Cadzow method is proposed to solve the coefficient matrices of VAR efficiently even if there is noise The simulation results on two real datasets show the applicability of the proposed method to online traffic flow prediction for edge computing-enhanced CAVs
TL;DR: In this article, an iterative algorithm based on successive convex approximation (SCA) technique and Dinkelbach's algorithm is proposed to solve the problem of maximizing the energy efficiency of the UAV by optimizing its trajectory.
Abstract: In this letter, we investigate a UAV-enabled communication system, where a UAV is deployed to communicate with the ground node (GN) in the presence of multiple jammers. We aim to maximize the energy efficiency (EE) of the UAV by optimizing its trajectory, subject to the UAV’s mobility constraints. However, the formulated problem is difficult to solve due to the non-convex and fractional form of the objective function. Thus, we propose an iterative algorithm based on successive convex approximation (SCA) technique and Dinkelbach’s algorithm to solve it. Numerical results show that the proposed algorithm can strike a better balance between the throughput and energy consumption by the optimized trajectory and thus improve the EE significantly as compared to the benchmark algorithms.
TL;DR: The UAV-enabled relay communication is investigated, where the UAV relay forwards the information received from the ground source node to the ground destination node under malicious jamming, and an efficient algorithm is proposed based on the block coordinate descent (BCD) and successive convex approximation techniques.
Abstract: In this letter, the UAV-enabled relay communication is investigated, where the UAV relay forwards the information received from the ground source node to the ground destination node under malicious jamming. We aim to maximize the end-to-end throughput by optimizing the trajectory of UAV and transmit power of both UAV and source node jointly. However, the formulated problem is intractable due to the non-convex objective function and constraints. Thus, with the aid of slack variables, we propose an efficient algorithm to solve it based on the block coordinate descent (BCD) and successive convex approximation (SCA) techniques. Numerical results are provided to show that the joint design of trajectory and power control improves the throughput significantly.
TL;DR: In this article, superhydrophobic conjugated microporous polymer-coated sponges (CMP@sponges) were synthesized by the in-situ Sonogashira polymerization of monomers with multiple carboxyl or hydroxy functional groups in the presence of melamine sponge in a one-pot synthesis.
TL;DR: In this article, three kinds of chiral conjugated microporous polymer composite membranes with porous silica as the support layer were successfully prepared by surface-initiated Sonogashira-Hagihara polymerization for the first time.
TL;DR: This work investigates the performance of the HST-CD network with the NOMA scheme, where the satellite proactively pushes/broadcasts the popular contents to the cache-enabled relay, and then the user is able to directly retrieve the required content from the relay with less transmission delay.
Abstract: Wireless content delivery (CD) and non-orthogonal multiple access (NOMA) have been confirmed to be promising and effective approaches to gain substantial performance improvement for hybrid satellite-terrestrial (HST) networks. To improve the spectrum efficiency and reduce the delay of retrieving the content for the satellite user, we investigate the performance of the HST-CD network with the NOMA scheme, where the satellite proactively pushes/broadcasts the popular contents to the cache-enabled relay, and then the user is able to directly retrieve the required content from the relay with less transmission delay. Specifically, based on the practical propagation model along with the stochastic geometry, the outage probability for the cache-enabled relays of the considered network is theoretically derived. Besides, the hit probability of the user in the HST-CD network is also provided. Finally, both simulation and analytical results are provided to validate the effect of the HST-CD network with the NOMA scheme and proclaim the influence of key factors on the performance.
TL;DR: In this article, the authors proposed a spectrum sharing scheme for low earth orbit (LEO) satellite constellation systems in a beam-hopping (BH) manner, where the GEO satellite system is served as the primary system and the LEO satellite constellation system is a secondary system whose frequency bands and transmitting power are strictly limited.
Abstract: In recent years, low earth orbit (LEO) satellite constellation systems have been developed rapidly. However, the scarcity of satellite spectrum resources has become one of the major obstacles to this trend. LEO satellite constellation communication systems sharing the spectrum of incumbent geostationary earth orbit (GEO) satellite system is a feasible way to alleviate spectrum scarcity. Therefore, it has practical significance to study the optimization of satellite resources allocation (RA) in a spectrum sharing scenario. This paper focuses on the RA problem that LEO satellites share a GEO high throughput satellite’s spectrum in a beam-hopping (BH) manner. The GEO satellite system is served as the primary system and the LEO satellite constellation system is served as the secondary system whose frequency bands and transmitting power are strictly limited. Compared with conventional multibeam satellites, BH satellites have the advantage of flexibility in the time dimension. Therefore, we make full use of the flexibility of LEO BH satellites to realize the matching of traffic demand and traffic supply. The RA problem is decomposed into three sub-problems, namely, frequency band selection (FBS) problem, illuminated cell selection (ICS) problem, and transmitting power allocation (TPA) problem. We solve each sub-problem in order and finally form a complete RA scheme. The performance evaluation of the proposed RA scheme is carried out in real-time and simulation results show that the LEO BH satellite paired with the RA scheme we proposed has good adaptability to the uneven distribution of traffic demand in the spectrum sharing scenario.
TL;DR: In this paper, the authors describe the synthesis of cobalt-nickel oxide nanowires decorated with molybdenum disulfide nanosheets, directly grown on Ni foam (MoS2/NiCo2O4/NF), by a simple stepwise hydrothermal method and calcination process.