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The reconfigurable intelligent surface (RIS) is considered a promising technique to improve the performance of user equipments in service dead zones in cell-free massive multiple-input multiple-output (CFmMIMO) systems. In this article, we investigate the impact of channel aging on the uplink CFmMIMO system aided by RISs with spatially correlated elements. We derive the closed-form expression of the uplink achievable spectral efficiency (SE) with the matched-filter receiver under channel spatial correlation and channel aging. Assuming only the statistical channel state information, we propose a two-step optimization scheme including the RIS phase shift design and power allocation. We first formulate the maximum normalized mean-squared error minimization problem to design the RIS phase shift in uplink training and solve it using the successive convex approximation algorithm. Then, we solve the sum SE maximization problem in the uplink data transmission to accomplish the power allocation by using the Lagrangian dual reformulation and the fractional programming theory. Extensive numerical results demonstrate the effectiveness and convergence of the proposed two-step optimization scheme.

The key idea of this paper is to leverage channel angle information to propose a low-complexity mode switching design in simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided multi-antenna non-orthogonal multiple access (NOMA) systems, which is suitable to both downlink and uplink transmissions by applying the concept of angle reciprocity. Particularly, a location-based assignment algorithm is firstly provided to perform NOMA pairing, then the equal gain transmission at the base station and the cophase matching criterion at STAR-RIS are adopted to improve the performance of the reflected user and the effective channel gain of the paired users, respectively. According to the proposed design, considering the difference in the Rician factors of the channel from the base station to STAR-RIS caused by the change of line-of-sight and non-line-of-sight components, two different cases for channel statistics of the paired users are rigorously characterized. Subsequently, we utilize the above results to analyze the closed-form expressions for the outage probability and ergodic rate of all users under downlink and uplink transmissions. Further, the diversity orders and the high slopes corresponding to all expressions are also determined to pursue more insights. Finally, numerical results are presented to demonstrate our analysis and reveal that: 1) the various system factors have significant impact on the performances of the proposed design; and 2) the low-complexity design can reduce the overheads of channel estimation and signal processing at the expense of extremely-low or even no performance loss in contrast to the traditional maximum ratio transmission based design.

This letter investigates the precoding in multi-static integrated sensing and communication system with integrated active and passive sensing. Specifically, a base station (BS) performs the downlink communication and active sensing tasks simultaneously, while receive access points (RAPs) conduct passive sensing using the reflected signals. Moreover, the system includes a central controller (CC) responsible for centrally processing both active and passive sensing information. Considering the limited backhaul capacity between the CC and RAPs, the result fusion strategy is adopted to analyze the sensing information. Within this setup, we aim to maximize sensing performance by optimizing the precoding vectors subject to the minimum signal-to-interference-plus-noise ratio (SINR) requirements and the BS’s power budget. Particularly, we consider two models of user’s SINR with and without the sensing interference cancellation. Under both models, the non-convex optimization problems are solved via designing two relaxed optimization objectives to approximate the original optimization objective and applying the quadratic transform technique. Finally, numerical results demonstrate that our proposed precoding optimization outperforms the power optimization with fixed precoding vectors.

Wireless power transfer and short packet communications are effective methods to provide sustainable energy supplies to the interest of things (IoT) devices and achieve ultra low latency for IoT applications, respectively. In this letter, the resource allocation for energy efficient wireless powered short packet communication networks is studied under the nonlinear energy harvesting model. The problem of joint optimization of the transmit powers and the time allocation to maximize the system energy efficiency is solved via the golden section search, the bisection search and the successive convex approximation methods. The simulation results demonstrate the superiority of the proposed algorithm compared to various state-of-the-art baseline algorithms.

In this correspondence, we investigate a hardware-impaired uplink cell-free massive multiple-input multiple-output (MIMO) system with intelligent reconfigurable surfaces (IRSs) under correlated Ricean fading channels. Based on the imperfect channel knowledge, we derive tight closed-form expressions for the lower-bound achievable rate and study the achievable rate under various settings, such as different channel conditions, degrees of hardware impairment, and numbers of APs/IRS/elements per IRS. We also formulate the design of the IRS phase-shifts and power control factors as an optimization problem that maximizes the weighted sum-rate (WSR) and develop algorithms to solve it. All theoretical analyses and the effectiveness of the proposed algorithms are verified via extensive simulations.

Cooperation can help unmanned aerial vehicles (UAVs) improve their plans to visit charging stations and avoid congestion, but can be hindered by privacy concerns. We propose a new, privacy preserving, joint routing, and charging scheduling framework which allows multiple cellular-connected UAVs to jointly optimize their routes and charging schedules in a decentralized fashion. The framework allows each UAV to minimize its energy usage and connectivity outage, maximize its recharged energy, ensure its timely arrival, and preserve its privacy concerning its trajectory and destination. The key idea is that we obfuscate probabilistically the destination of each UAV, and design a new noncooperative Bayesian game among the UAVs to find their best routes and charging schedules toward the obfuscated destinations. Another important aspect is that we prove the game is a potential Bayesian game with a pure-strategy Bayesian Nash equilibrium and the best response yielded with the Bellman-Ford algorithm. This new framework preserves the UAVs’ privacy in the sense that an UAV only shares the probability of its visit to a charging station at different times, and its best response is based on an obfuscated destination. Simulations demonstrate that the framework ensures timely arrivals with near-optimal routes and substantially lower complexity than a centralized routing scheme based on brute force.

Reconfigurable intelligent surface (RIS) is a promising solution to programmable wireless channels. However, most existing RIS phase optimization algorithms, relying on iterative processes, suffer from high latency and poor scalability. To address this issue, we propose a low-complexity scheme based on deep unfolding. Specifically, we consider a downlink MISO system aided by a RIS and aim to maximize users’ weighted sumrate (WSR). We employ Lagrangian dual transformation to decouple the original non-convex problem into two sub-problems: transmit beamforming optimization and phase shift design. Then, we introduce a block coordinate descent (BCD) method, which still relies on iterative updates and includes complex operations such as matrix inversion, leading to high computational complexity and latency. To achieve fast solutions, we further propose to unfold the BCD method’s iterative process into layers of an interpretable neural network (NN) with a few trainable parameters. The NN is trained offline and deployed online for realtime solutions. Finally, numerical results validate the performance of the proposed scheme in terms of comparable WSR and reduced computational complexity.

Wireless federated learning (FL) is a distributed learning framework, in which mobile devices (MDs) collaboratively train a model while protecting data privacy. Considering selfishness of MDs, they are unwilling to sacrifice their resources to assist model training without any reward, which may limit performance of FL. In this paper, we propose an incentive mechanism between a base station (BS) and several mobile devices (MDs) modeled as a Stackelberg game. During this game, BS first decides the total reward that can be distributed to MDs, and then each MD determines its number of local iterations according to its utility function. In addition, we derive the closed-form expressions of the optimal reward function of the BS and the number of local iterations of the MDs. Finally, the numerical results validate the efficacy of our proposed scheme.

Aircraft is an important means of travel and the most convenient and fast vehicle in long-distance transportation. The aircraft engine is one of the most critical parts of an aircraft, and its reliability and safety are extremely important. In this article, we consider that the operating conditions of aero-engines are complex and changeable, and a multi-resolution long short-term memory (MR-LSTM) model is proposed. The model can effectively predict the remaining useful life (RUL) of an aero-engine, which is a priority issue within the Prognostics and Health Management (PHM) framework - and thus it can support maintenance decisions. Sequences with multiple temporal resolutions are generated by a reconstruction of the decomposed wavelets. A two-layer LSTM model is then designed: 1) the first layer LSTM is used to learn attention at different time resolutions as well as to generate an integrated historical representation; 2) the second layer LSTM is used to learn the long and short-term time dependencies in the integrated historical representation. Experimental evaluations using the C-MAPSS datasets (FD002 and FD004) and the N-CMAPSS dataset showed that compared to other state-of-the-art RUL prediction methods, the FD002 sub-dataset showed a 12.1% reduction in RMSE and a 3.8% reduction in Score; the FD004 sub-dataset showed a 21.8% reduction in RMSE and a decreased by 62.1%; the RMSE of the N-CMAPSS dataset decreased by at most 25.8%.

In industrial Internet of Things, the combination of specific emitter identification (SEI) and key authenticated technologies can effectively resist spoofing attacks and improve system security. However, most existing SEI approaches extract features based on real valued operations and only work in static scenario. This motivated us to develop a novel SEI method tasked with: exploiting the high potential model for SEI based on the inphase/quadrature (I/Q) signal that is represented by complex number, and realizing rapid reconstruction of the model in the face of dynamic scenarios. To this end, in this article, we introduce a two-stage cross-domain identification model. First, a complex-valued separate residual network (CVSRN) with novel separate residual modules is proposed as the pretrained model. The CVSRN can automatically extract effective inherent features directly from raw signals in an end-to-end manner, which favors complex-valued signals that are found in two distinct signal paths. Second, three transfer strategies are proposed to achieve rapid construction of the target SEI model. They leverage the knowledge learned from the pretrained CVSRN to facilitate the recognition of a new but similar emitters. We benchmark our proposed approach against four state-of-the-art SEI methods on real-world data and exhibit that it is not only competitive but also able to cope with complex dynamic scenarios.

Intelligent reflecting surface (IRS) assisted cell-free (CF) massive multiple-input multiple-output (mMIMO) offers substantial potential for enhancing spectral efficiency, energy efficiency, and coverage areas. In this letter, we introduce a novel approach aimed at maximizing the sum-rate in the downlink of IRS-assisted CF mMIMO systems. Our approach employs an alternating optimization strategy to design both active and passive beamforming techniques. Specifically, for active beamforming, we adopt zero-forcing beamforming to effectively eliminate inter-user interference, and we employ geometric programming to obtain optimal power allocation coefficients. For passive beamforming, we present innovative solutions through the alternating direction method of multipliers (ADMM) algorithm and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Additionally, we rigorously analyze the computational complexity of our proposed algorithms, providing a comparative assessment against existing approaches. Our simulations substantiate the superior sum-rate performance achieved by our algorithms, underscoring their efficacy in IRS-assisted CF mMIMO systems.

Targeting the high-quality signal and transmission robustness of sensing and communications in future intelligent vehicles, this article proposes an ultra-wideband compact antenna which can work at left-hand circular polarization (LHCP) and right-hand circular polarization (RHCP). It consists of two cross-arranged dipoles, four parasitic elements, a feeding network, and a ground plane. The radiation arm of the dipoles is shaped in a truncated elliptical form to broaden the 3-dB axial ratio bandwidth. To stabilize the radiation pattern and improve the axial ratio at higher frequencies in the proposed cross-dipole antenna, four sequential rotating patches are strategically positioned on the two cross-arranged dipoles. The design incorporates a specialized and highly-integrated feed network, comprising a miniaturized 90° stripline coupler and two double-sided parallel lines. This feed network is meticulously engineered to excite the dipoles, resulting in the generation of dual-circularly polarized radiation. Through the implementation of this innovative approach, an ultra-wideband dual-circularly polarized antenna having stable radiation pattern is designed, manufactured, and measured. The proposed cross-dipole antenna achieves impedance bandwidth (Reflection coefficient ≤−10 dB) from 1.25 to 4.7 GHz and axial ratio bandwidth (Axial ratio ≤ 3 dB) from 1.3 to 4.7 GHz, indicating that the overlapped impedance and axial ratio bandwidth reaches 113.3%. Good radiation performance is obtained within the ultra-wide bandwidth.

Federated edge learning (FEEL) emerges as a privacy-preserving paradigm to effectively integrate edge computing for the implementation of deep learning-based vehicular applications. Nevertheless, the incentive mechanism for vehicles participating in varied learning tasks, has not been well explored yet. In this paper, software-defined network (SDN) technology is adopted for the training control among vehicles, and a novel FEEL framework, namely SDN-assisted semi-decentralized FEEL (SSD-FEEL) is investigated, where multiple edge servers collectively coordinate a large number of vehicular models from different learning tasks. By exploiting the low-cost and similar learning tasks among vehicles and edge servers, SSD-FEEL incorporates more training samples, while enjoying the flexibility of edge server assisted model aggregation. Aiming at motivating vehicles to actively participate in training while improving the model accuracy of multiple learning tasks, a joint contract design and task reorganization problem, combined with the evaluation of model convergence and contract performance, is formulated. Then, we propose a two-stage optimization algorithm incorporating iterative reward allocation and task matching, where the model parameters in different tasks are reconstructed according to the matching results with the mobility constraints. Extensive experiments conducted on multiple data sets validate that the proposed algorithm can achieve higher cluster utility and outperform the conventional multi-task FEEL schemes in terms of learning performance.

Electric vehicles (EVs) provide sustainable and eco-friendly transportation. However, long-range driving is challenging due to limited battery capacity. While charging stations can replenish the battery, they are not as widely deployed as gas stations, causing the battery depletion of EVs. This paper presents a novel optimal charging strategy for EV long-range driving. The strategy offers EV drivers the optimal charging instructions for a given trip. A new finite-horizon Markov decision process (FH-MDP) problem is formulated to minimize the travel time of an EV while preventing its battery depletion, by optimally selecting the charging stations and charging times along the trip. By confirming the monotonicity and subadditivity of the FH-MDP, we prove the existence of a monotone deterministic Markovian policy for the optimal charging decision and reveal the optimal charging time is monotone regarding the remaining battery level and driven distance. We also reveal that the optimal charging time only changes when either of two thresholds regarding the remaining battery level or driving distance is met. By comparing its state with the thresholds, the EV can make optimal decisions with linear complexity. Simulations corroborate that our algorithm can save travel time by at least 12.6% under our simulation settings, compared to alternative methods.

Cell-free massive multiple-input-multiple-out (CF-mMIMO) systems provide limited coverage because of expensive wired fronthaul between access points (APs) and central processing unit (CPU). To address this challenge, we propose a novel framework where an unmanned aerial vehicle (UAV), acting as an aerial AP, works coherently with the ground APs to expand the coverage of conventional CF-mMIMO system. To fully utilize the spectrum resource, the wireless fronthaul between the CPU and UAV shares the total bandwidth with the radio access networks. Considering limited power supply of the UAV and for the goal of green communications, we formulate a weighted sum power minimization problem to jointly optimize downlink beamforming and fronthaul compression, as well as UAV placement. The formulated problem is a mixed timescale problem, thus we propose a two-timescale optimization framework in which the UAV placement is optimized in each long timescale based on statistical channel state information (CSI), then the downlink beamforming and fronthaul compression are optimized in each short timescale based on instantaneous CSI. Specifically, uplink-downlink duality and semidefinite relaxation (SDR) based alternating optimization techniques are introduced to find solutions to the short timescale issue, while successive convex approximation and SDR methods are invoked to find solutions to the long timescale issue. Finally, simulation results corroborate the performance of the proposed algorithm.

This letter considers the cell-free massive multiple-input multiple-output (MIMO) system for ultra reliable and low-latency communication (URLLC) under non-reciprocal channels and pilot contamination. To investigate the impact of the channel non-reciprocity, we model it as a random variable using the more realistic truncated Gaussian distribution. In this spirit, we derive the closed-form expression of the downlink achievable rate, which depends only on the statistics of channel non-reciprocity (NRC) and channel state information. To improve the system performance and fairness among users, we formulate the max-min power control problem to maximize the minimum user downlink achievable rate. We propose a path-following method to reformulate the non-convex original problem as a series of geometric programming. Simulation results show new findings that both amplitude and phase reciprocity errors at access points can significantly degrade the achievable rate when only the channel NRC variable statistics are available. Moreover, the proposed power control algorithm converges rapidly and greatly improves the system performance.

Clustered Vehicular Federated Learning (CVFL) can be used to improve traffic safety, increase traffic efficiency, and reduce vehicle carbon emissions. Therefore, it is extremely promising in intelligent transportation systems. However, in practice, it is difficult to accurately cluster vehicular clients with mobility according to data distribution. In addition, vehicular clients may be reluctant to contribute their computation and communication resources to perform learning tasks if the CVFL server does not give them proper incentives. In this paper, we would like to address the above issues. Specifically, considering the mobility of vehicular clients, we first propose a clustering method to cluster vehicular clients into several clusters based on the cosine similarity between the model gradient of local vehicular clients and the K-means method. Then, we design a set of optimal contracts specifically for the clusters, aiming to motivate them to select the optimal number of intra-cluster iterations for model training and give the closed-form solution to the contracts under the constraints of individual rationality, incentive compatibility, and task accuracy. The proposed contract theory based incentive mechanism not only effectively motivates every cluster, but also overcomes the information asymmetry problem to maximize the utility of the CVFL server. Finally, simulation results validate the effectiveness of the proposed clustering method and the designed contract.

Most existing works on dual-function radar-communication (DFRC) systems mainly focus on active sensing, but ignore passive sensing. To leverage multi-static sensing capability, we explore integrated active and passive sensing (IAPS) in DFRC systems to remedy sensing performance. The multi-antenna base station (BS) is responsible for communication and active sensing by transmitting signals to user equipments while detecting a target according to echo signals. In contrast, passive sensing is performed at the receive access points (RAPs). We consider both the cases where the capacity of the backhaul links between the RAPs and BS is unlimited or limited and adopt different fusion strategies. Specifically, when the backhaul capacity is unlimited, the BS and RAPs transfer sensing signals they have received to the central controller (CC) for signal fusion. The CC processes the signals and leverages the generalized likelihood ratio test detector to determine the present of a target. However, when the backhaul capacity is limited, each RAP, as well as the BS, makes decisions independently and sends its binary inference results to the CC for result fusion via voting aggregation. Then, aiming at maximize the target detection probability under communication quality of service constraints, two power optimization algorithms are proposed. Finally, numerical simulations demonstrate that the sensing performance in case of unlimited backhaul capacity is much better than that in case of limited backhaul capacity. Moreover, it implied that the proposed IAPS scheme outperforms only-passive and only-active sensing schemes, especially in unlimited capacity case.

In this paper, a two-layer large scale fading precoding (LSFP) method is proposed in a downlink cell-free massive multiple-input multiple-output (mMIMO) system. In the first LSFP layer, the access points are responsible for designing distributed precoding and power control coefficients for each user equipment (UE). While for the second LSFP layer, the central processing unit performs the proposed zero forcing LSFP (ZF-LSFP) for the copilot UEs in order to mitigate the interference caused by pilot contamination. To address power constraints and enhance the system's spectral efficiency (SE), we introduce the copilot power scaling factors. Specifically, we derive closed-form expressions for the lower-bound achievable SE under distributed precoding schemes. These expressions are versatile and applicable to downlink SE irrespective of the availability of ZF-LSFP. Based on the derived closed-form results, we formulate a sum SE maximization problem with respect to copilot power scaling factors. Two approaches are designed to solve this problem, i.e. the weighted minimum mean square error (W-MMSE) based approach and the geometric programming (GP) based approach. While the W-MMSE-based approach is suitable for various systems, the GP-based approach is specifically tailored for systems with high signal-to-noise ratios, offering fast running speed. Finally, we validate all theoretical analyses and assess the effectiveness of the proposed two-layer LSFP method through numerical simulations.

ABSTRACTThis study aims to investigate the durability and microstructure of cement-sodium silicate grouts with high water to binder ratios (w/b) of 1.1-2.5 upon exposure to sea water. Grout specimens were [...]

Security assessment (SA) system is crucial to ensure the production safety of a smart factory with rapid development of artificial intelligence. In this article, we propose a novel SA framework. Different from the conventional static monitoring systems based on traditional sensing technologies, the proposed framework can automatically detect objects via visual sensing. We use a skeleton-based graph convolutional network to generate action vocabulary for the intermediate representations of action-to-action cooccurrence relations. These representations are encoded into the sequential interaction models to form the interaction representations. Integrating the states of molten steel levels as the reference labels, the sequential representations are fed into a recurrent neural network model with multilayer gated recurrent units (GRUs) to capture the key interactions leading to the accidents, in which an attention mechanism is used to reweight the actions and eliminate the invalid interactions. The predicted labels and the hidden states of the scenes are passing among multilayer GRUs. Finally, we optimize the global output to dynamically assess the security by calculating a joint objective function with a regularized cross-entropy loss. On the self-collected dataset from our partner Iron and Steel company and on-line video clips, the proposed framework performs better than the existing SA schemes.

Driven by the development of time-sensitive applications, short packet transmission (SPT) design has become the key point in the ultra-reliable and low-latency communications (URLLC) area. The primary challenge in it is that the delay caused by pilot overhead cannot be neglected. To deal with this issue, this paper presents a frame structure adopting partial-superimposed-pilot (PSP) scheme for SPT. The key of PSP scheme is that the number of data symbols is equal to the available blocklength, and the number of pilot symbols transmitted with data in the training stage needs to be optimized. Under the finite blocklength regime, we first derive a closed-form lower bound achievable rate of an uplink massive MIMO system with imperfect pilot removal for maximal-ratio-combining (MRC) receiver. Then, we formulate a weighted sum rate maximization problem by jointly optimizing the pilot length, pilot power, and data power. We derive a closed-form solution of optimal pilot length. Using the log-function and successive convex approximation (SCA) method, we develop an iterative optimization framework to find a locally optimal power solution. For comparison, the conventional frame structures based on complete-superimposed-pilot (CSP) and regular pilot (RP) schemes are also shown. Simulation results indicate that the proposed PSP scheme is superior to the existing CSP and RP schemes.

A hybrid control strategy is proposed for a SS compensated three-phase wireless power transfer (3P-WPT) system, which realizes full zero-voltage switching (ZVS) operation of the converter during the entire charging process of a battery. First, a mathematical equivalent model of 3P-WPT is established by fundamental harmonic approximation, the ZVS operation of the active bridge in the constant current (CC) charging mode is optimized by considering the relationship between the input impedance, operation frequency, and duty cycle. Consequently, an optimal modulation strategy featuring full ZVS operation is proposed for the constant current and constant voltage (CV) charging modes of 3P-WPT. Second, based on the NSGA-II algorithm, a mathematical model for the multi-objective optimization of coils is established, which aims for a high transmission frequency and power density. The parameters of coils are optimized accordingly. Finally, a 2 kW experimental prototype is built. Experimental results verify the effectiveness of the proposed control strategy, where the maximum efficiency is up to 90.26% at a full load.

This paper investigates the uplink performance of a general cell-free massive multiple-input multiple-output (CF-mMIMO) system, in which all access points (APs) and user equipments (UEs) suffer from hardware impairments (HWIs). Besides, there are several reconfigurable intelligent surfaces (RISs) that aim to improve the coverage quality, spectral efficiency (SE), and energy efficiency (EE). Relying on the knowledge of only imperfect channel state information, a tight closed-form expression for the lower-bound achievable SE is derived. Based on this expression, we quantitatively investigate the impacts of different system parameters on uplink SE and EE, and conduct a tradeoff analysis between using more APs versus using more RISs with respect to the above performance metrics. In addition, we also design a max-min SE algorithm that takes into account both large-scale fading decoding weights and power control coefficients to guarantee UE fairness. Specifically, the proposed algorithm admits a closed-form solution and is therefore memory-efficient and time-saving. Both the theoretical analysis and the effectiveness of the proposed max-min SE algorithm are verified via extensive simulations.

This letter investigates the physical layer security of reconfigurable intelligent surface (RIS) aided integrated-sensing-and-communication (ISAC) systems, in which the high-power radar signal is used as the interference to disturb the eavesdropper. We formulate a joint beamforming optimization problem of active beamforming of base station and passive beamforming of RIS to minimize the maximum eavesdropping signal-to-interference-plus-noise (SINR). To find its solution, we decompose the original problem into two subproblems and then use the alternating optimization (AO) technique based on the fraction programming (FP) and semi-definite programming (SDP) methods. Simulation results show that the proposed FP-SDP-AO algorithm has good convergence performance and can effectively suppress the SINR of eavesdroppers while maintaining a satisfactory quality of service for legitimate users.

ABSTRACT Fly ash (FA) can improve the workability and durability of concrete. However, the utilization of FA is usually limited due to its low pozzolanic activity. In this study, a [...]

This paper aims to improve the reliability of multi-type load power supply, reduce the disorder of electric vehicle (EV) charging, and ensure the low-carbon economic operation of virtual power plant (VPP). Therefore, the optimal allocation method of low-carbon economy for VPP involving load aggregator (LA) participated by EV is proposed. First, a probabilistic model of electric load demand of a VPP is constructed, and an aggregation model of multiple types of uncertain loads is established based on the probabilistic model. Secondly, the disorder of EV charging is analyzed. Considering the time-of-use price policy, an optimization model with the maximum profit of load aggregator is established. Finally, the supply and demand matching model of VPP is established, and the minimum carbon emission and maximum net income of virtual power plant are optimized. Through simulation analysis, it is verified that this optimal configuration method can effectively improve the economics of EVLA and VPP, and ensure low-carbon operation of VPP.

Purpose: A high-performance tracking system will be crucial for the realization of the full physics potential of the Circular Electron Positron Collider (CEPC) project. A baseline tracker has been proposed in the Conceptual Design Report and requires detailed studies of the layout geometry and comprehensive understanding of its tracking performance. Methods: A Common Tracking Software (ACTS) is an experiment-independent software package and introduced for the track reconstruction for particle and nuclear physics experiments. The CEPC baseline tracker geometry has been implemented in ACTS, and its tracking performance has been evaluated with the provided track finding and fitting algorithms based on the truth tracking method. Results and Conclusion: The tracking geometry implemented in ACTS, including the sensitive elements and simplified material distribution, has been validated. The track parameter resolutions obtained with ACTS are consistent with the full simulation results over the momentum range under investigation. ACTS has proven to be an excellent platform for the tracking performance evaluation and the tracker layout optimization.

Unmanned aerial vehicles (UAVs) have been extensively applied to goods delivery and in-situ sensing. It becomes increasingly probable that multiple UAVs are delivering goods and carrying out sensing tasks at the same time. The destinations of the UAVs are usually required to jointly design their trajectories and sensing selections, leading to privacy concerns for the UAVs. This paper presents a new game-theoretic routing framework for joint goods delivery and sensing of multiple cellular-connected UAVs, where the UAVs minimize their energy consumption and connectivity outage, maximize their sensing reward, and ensure timely goods delivery and trajectory privacy by optimizing their trajectories and sensing task selections in a decentralized manner. The key idea is that we unify routing and sensing in a single task selection process, which is further transformed into routing on a task-time graph. Another important aspect is that we design a non-cooperative potential game for the routing on the task-time graph. A distributed strategy is developed, where each UAV only reports its sensing task selections and withholds its destination information and its best response produced by the Bellman-Ford algorithm. By this means, the destination and trajectory privacy of the UAVs are protected. Simulations show that the new game-theoretic approach can ensure timely delivery and achieve close-to-optimal solutions with significantly lower complexity compared to a centralized brute-force approach.

Machine learning-based random access schemes have gained significant attention in recent years. However, optimizing the access delay of such schemes remains a challenge. In this letter, we focus on analyzing and optimizing the mean access delay of the deep neural network-based double-contention random access (DCRA) scheme, proposed in our earlier work. Specifically, by leveraging the characterization of the state transition of each access request, the mean sojourn time of each state can be derived explicitly, based on which the mean access delay of the DCRA scheme is further obtained. To optimize the mean access delay, we propose optimizing the backoff parameters of every MTDs. Simulation results demonstrate that the mean access delay of the DCRA scheme can be significantly reduced compared to existing schemes, by appropriately selecting the backoff parameters of every MTDs.

We study the physical layer security for short packet communications in wireless powered Internet of Things networks. The problem of optimizing the packet error rate and the packet length for maximizing the total secrecy throughput is investigated. Since the problem is challenging to solve due to its non-convexity, we develop a low-complexity algorithm based on the relaxation method and the block coordinate descent method to obtain a suboptimal solution. The simulation results demonstrate the effectiveness of the proposed algorithm. It is shown that the proposed algorithm achieves similar performance as compared to the optimal solution based on the exhaustive search, and outperforms various baseline algorithms in existing literature.

To handle the ever-increasing IoT devices with computation-intensive and delay-critical applications, it is imperative to leverage the collaborative potential of edge and cloud computing. In this paper, we investigate the dynamic offloading of packets with finite block length (FBL) in an edge-cloud collaboration system consisting of multi-mobile IoT devices (MIDs) with energy harvesting (EH), multi-edge servers, and one cloud server (CS) in a dynamic environment. The optimization problem is formulated to minimize the average long-term service cost defined as the weighted sum of MID energy consumption and service delay, with the constraints of the available resource, the energy causality, the allowable service delay, and the maximum decoding error probability. To address the problem involving both discrete and continuous variables, we propose a multi-device hybrid decision-based deep reinforcement learning (DRL) solution, named DDPG-D3QN algorithm, where the deep deterministic policy gradient (DDPG) and dueling double deep Q networks (D3QN) are invoked to tackle continuous and discrete action domains, respectively. Specifically, we improve the actor-critic structure of DDPG by combining D3QN. It utilizes the actor part of DDPG to search for the optimal offloading rate and power control of local execution. Meanwhile, it combines the critic part of DDPG with D3QN to select the optimal server for offloading. Simulation results demonstrate the proposed DDPG-D3QN algorithm has better stability and faster convergence, while achieving higher rewards than the existing DRL-based methods. Furthermore, the edge-cloud collaboration approach outperforms non-collaborative schemes.

AbstractIn this study, eco-friendly lightweight high-strength engineered cementitious composites (ELHECC) were developed using lightweight sand fines (LSF) and fly ash cenospheres. The effects of LSF content and pretreatment methods on mechanical properties, shrinkage and micro-mechanism of ELHECCs were investigated. Two pretreatment methods—normal pressure immersion and vacuum saturation—were employed. The internal curing effects of treated and untreated LSF were compared. The results showed that increasing the content of LSF slightly reduced compressive strength but improved the tensile properties and ductility of ELHECCs. Vacuum-saturated LSF improved the mechanical properties of ELHECCs more than normal pressure-immersed LSF. In addition, increasing the substitution amount of LSF increased the corresponding internal relative humidity, extended the duration of saturated humidity, and subsequently reduced the autogenous shrinkage of ELHECC. The reduction effect of autogenous shrinkage and drying shrinkage after LSF pre-saturation was much greater than without saturation. Moreover, LSF significantly delayed the hydration process and reduced the hydration heat rate and hydration heat, and the content of Ca(OH)2in ELHECCs decreased with increased LSF substitution rate. The effect of vacuum saturation for LSF in reducing the porosity of ELHECC was greater than that of normal pressure saturation. Furthermore, LSF had superior bonding with the ELHECC matrix, forming a well-integrated sand–matrix interface. The experimental results provide inspiration for green applications in bridge bearing structure, building structure reinforcement and repair in terms of impressive tensile properties, low shrinkage, light weight, and waste utilization.

ABSTRACT A foamed cement-base material is proposed as the sacrificial cladding of tunnel lining structures to provide the blast mitigation effect. An experimental investigation on responses of the foamed cement-base [...]

With the rapid development of edge intelligence (EI) and machine learning (ML), the applications of Cyber-Physical Systems (CPS) have been discovered in all aspects of the life world. As one of its most essential branches, Medical CPS (MCPS) determines human health and medical treatment in the Internet of Everything (IOE) era. Knowledge sharing is the critical point of MCPS and has also been humanity's best dream through the ages. This paper explores a novel knowledge-sharing model in MCPS and takes a pulmonary nodule detection task as a significant case for building an Unet-based mask generator. A Classification-guided Module (CGM)-based discriminator with knowledge from EMRs is set against a generator to offer a promising result for each mask from the inexperienced participant of federated ML. After an iterative communication between the federated server and its clients for knowledge sharing, the segmented sub-image owns a coincident attribute distribution with that of the EMRs from the experts. Besides, the adversarial network augment the data to normalize the data distribution for all the clients as a remission for none independent identically distributed (non-IID) data problem. We implement a detection framework on the simulated EI environment following an existing adaptive synchronization strategy based on data sharing and median loss function. On 1304 scans of the merged dataset, our proposed framework can help boost the detection performance for most of the existing methods of pulmonary nodule detection.

Underwater image enhancement (UIE) is an essential task for intelligent environment perception in underwater remote visual sensing scenarios. However, the computing power of mobile platforms limits the usage of larger scale models. In this article, we propose a lightweight encoder-decoder architecture [UIE network (UIENet)] to enhance underwater images from visual sensors. We also involve the architecture into a generative adversarial network (UIEGAN) model against a supervised discriminator to further perfect its corrective capabilities for the photorealistic images with more global appearance and local details. The multiresolution counterparts are embedded into the generator to diversify the feature representation of the original inputs. Further, UIEGAN guides the spatial attention module (SAM) and the channel attention module (CAM) to jointly enhance the global-local connection of the image. We evaluate the proposed method on benchmark datasets of UIEB and UFO-120 and report better performance than the state-of-the-art (SOTA) schemes, exceeding 15.43% and 12.85% on peak signal-to-noise ratio (PSNR) than the baselines of these datasets. Besides, by testing on the UIEB challenge, URPC and SQUID datasets without any reference images, our scheme outperforms the other methods on evaluation metrics to validate its generalization performance, and meanwhile uses a series of ablation study demonstrates the effectiveness of the functional modules.

This paper puts forth a new application of an unmanned aerial vehicle (UAV) to joint goods delivery and in-situ sensing, and proposes a new algorithm that jointly optimizes the route and sensing task selection to minimize the UAV’s energy consumption, maximize its sensing reward, and ensure timely goods delivery. This problem is new and non-trivial due to its nature of mixed integer programming. The key idea behind the new algorithm is that we interpret the possible waypoints of the UAV as location-dependent tasks to incorporate routing and sensing in one task selection process. Another critical aspect is that we construct a new task-time graph to describe the process, where each vertex corresponds to a task associated with its location, time and reward, and each edge indicates the propulsion energy required for the UAV to travel between two tasks. By redistributing the weight of a vertex to its incoming edges, the new UAV routing and sensing task selection problem can be converted to a weighted routing problem in the new task-time graph and solved optimally using the Bellman-Ford algorithm. Validated by a real-world case study, our approach can outperform its alternatives by over 18% in task reward.

The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) is a promising paradigm for providing extensive coverage and additional computation services to the Internet of Things (IoT) devices with smart applications, which are normally computation-intensive and delay-critical, especially in some remote areas or emerging scenarios without available infrastructure. However, considering the restricted computation capabilities of UAVs, even multiple UAVs may not satisfy the quality of service (QoS) requirement of IoT users with heavy-computation applications. In this paper, a heterogenous-multiple-UAVs enabled collaborative aerial-ground MEC framework is proposed. The total computation bits maximization problem under the time-division multiple access (TDMA) scheme and partial offloading mode is formulated by jointly optimizing the user association, the CPU-cycle frequencies of users and computing UAVs, the transmit power of users and UAVs, the offloading time of users and UAVs, as well as the UAVs' trajectories. Due to the non-convexity of the original problem with the nonlinear coupling of different variables, a two-layered alternative optimization (TLAO) algorithm is proposed, where the outer layer optimizes the user association and resource allocation, while the inner layer optimizes the UAV trajectory scheduling. Simulation results demonstrate that our proposed TLAO scheme can improve the total computation bits compared with other benchmark schemes, and achieve fast convergence. Moreover, comparative results also verify that our proposed strategy is flexible to apply to homogeneous multi-UAVs and single-UAV scenarios.

With the rapid development of 5G, massive machine-type communication is expected to experience significant growth, leading to severe random access collisions. To address this issue, we first adopt deep neural networks to detect random access collisions by learning the features of the received signals. Based on the collision-detection results, we propose a double-contention random access (DCRA) scheme, with which the base station can schedule one more contention process for devices experiencing collisions. To fully harness the collision-resolution capability of the proposed DCRA scheme, we further analyze its performance and illustrate how to tune the backoff parameters to optimize the network throughput. It is revealed that the maximum throughput of the DCRA scheme depends on the number of random access preambles and the collision recognition accuracy. The corresponding optimal backoff parameters are then obtained, which greatly facilitates implementations in practice. Simulation results show that with a high collision recognition accuracy, the proposed scheme can achieve significant throughput improvement.

Beamforming is an effective way to improve spectrum efficiency in multi-antenna systems. However, conventional iterative algorithms suffering from high computational delay renders beamforming solutions outdated and deep learning (DL) based realtime solutions suffer from the “task mismatch” problem, leading to severe performance deterioration. To resolve these issues, this letter proposes a beamforming adaptation scheme, named continual learning based beamforming neural network (CL-BNN), which can continuously learn and optimize the downlink beamforming in dynamic environment. The proposed CL-BNN scheme is model-driven and uses domain/expert knowledge to reduce training complexity. By combining our customized selection criteria into the structure of the CL-BNN, which selects the appropriate samples from the existing data and newly received data into a memory set for model training, taking into account the objective function, such that the tradeoff between maintaining performance of the trained task and adapting to the new task is achieved. Simulation results show that the proposed CL-BNN scheme achieves good adaptability in dynamic environments with low complexity.

Current fifth-generation (5G) networks do not cover maritime areas, causing difficulties in developing maritime Internet of Things (IoT). To tackle this problem, we establish a nearshore network by collaboratively using on-shore terrestrial base stations (TBSs) and tethered unmanned aerial vehicles (UAVs). These TBSs and UAVs form virtual clusters in a user-centric manner. Within each virtual cluster, non-orthogonal multiple access (NOMA) is adopted for agilely including various maritime IoT devices, which are sparsely distributed over the vast ocean. The nearshore network also shares the spectrum with marine satellites. In such a NOMA-based hybrid satellite-UAV-terrestrial network, interference among different network segments, different clusters, and different users occurs. We thereby formulate a joint power allocation problem to maximize the sum rate of the network. Different from existing studies, we use large-scale channel state information (CSI) only for optimization to reduce system overhead. The large-scale CSI is obtained by using the position information of maritime IoT devices. The problem is non-convex with intractable non-linear constraints. We tackle these difficulties by adopting max-min optimization, the auxiliary function method, and the successive convex approximation technique. An iterative power allocation algorithm is accordingly proposed, which is shown to be effective for coverage enhancement by simulations. This shows the potential of NOMA-based hybrid satellite-UAV-terrestrial networks for maritime on-demand coverage.

To legitimately eavesdrop communications of suspicious users such as criminals, proactive eavesdropping has been proposed as an effective approach. Current works considered that suspicious communication links (SCLs) carry homogeneous services, which is unrealistic due to randomness of user behaviors. Thus, this paper investigates proactive eavesdropping over multiple SCLs with heterogeneous services. The relative eavesdropping non-outage probability and the relative eavesdropping rate are adopted as eavesdropping utilities for delay-sensitive (DS) and delay-tolerant (DT) suspicious services, respectively. The jamming power allocation problem to maximize the weighted sum eavesdropping utility under the average transmit power constraint at the monitor is investigated under the non-adaptive fixed power allocation and the adaptive power allocation schemes adopted by the SCLs. The problem belongs to sum-of-ratios optimization and is transformed to an equivalent parameterized subtractive-form with some extra auxiliary variables. Two nested optimizations are proposed to solve the equivalent problem based on the Lagrange duality method and the damped Newton method. Simulation results demonstrate the superiority of the proposed algorithms compared to various benchmark algorithms in existing literature. It is shown that the impacts of the power allocation schemes of the SCLs on the eavesdropping performance may be different for the DS and DT suspicious services under different system setups.

In this paper, a recursive robust set-membership fusion estimator (RSMFE) is presented to track the moving targets treated as the mobile nodes in a mobile wireless sensor network (MWSN) system with unknown but bounded (UBB) anchor location uncertainty. Firstly, the accurate ellipsoidal set containing the Lagrange remainder of nonlinear distance measurement, which contains the effect of anchor location uncertainty, is estimated and adopted to improve the stability and accuracy of the moving target tracking. Secondly, the recursive RSMFE-based tracking formulae of reduced computation complexity, consisting of the prediction formulae and the fusion update formulae, are derived to estimate the robust minimized or optimal ellipsoidal set containing the actual moving target state by appropriately fusing the measured distances from the nearby anchors subject to the UBB anchor location uncertainty, where the computation complexity of the fusion update formulae is reduced substantially based on the decoupled techniques and the equivalent transformation. Finally, the numerical simulation and experiment of the WSN-assisted moving target tracking system with various anchor location uncertainty settings are performed to verify the effectiveness and superiority of the proposed recursive RSMFE-based moving target tracking or localization method.

Proactive eavesdropping is an effective approach to improve the performance of wireless information surveillance, where suspicious users are legitimately eavesdropped by authorized monitors. For the situation when no monitor is available, we propose to authorize unsuspicious users to surveil suspicious users provided that the quality-of-service (QoS) of unsuspicious users is guaranteed. This paper considers an interference network consisting of a suspicious-transmitter (STx)/suspicious-receiver (SRx) pair and an unsuspicious-transmitter (UTx)/unsuspicious-receiver (URx) pair. We assume that URx acts as a monitor to eavesdrop the suspicious communication, and UTx adopts improper Gaussian signaling (IGS) for flexibly managing the interference to SRx. The QoS of the unsuspicious communication is guaranteed by enforcing its outage performance to be higher than a desirable level. The problem of optimizing the transmit power and the IGS circularity coefficient of UTx to maximize the average eavesdropping rate is optimally solved by the Lagrange duality method. Theoretical results show that IGS is effective when the eavesdropping is successful and the unsuspicious communication is in non-outage status. Simulation results confirm that the proposed design works well even when no additional outage of the unsuspicious communication is allowed, and show that IGS can greatly outperform proper Gaussian signaling in most cases.

It is an irreversible trend to build a green and sustainable vehicular network facing with the dramatic increase in urban traffic. Reducing energy consumption has been an important aspect for green transportation. Reconfigurable intelligent surface (RIS) is considered as a promising technology to enhance the communication quality with higher energy efficiency. In this paper, we focus on the RIS-assisted vehicular networks. We obtain the closed-form analytical expressions for outage probability, ergodic achievable rate and average energy efficiency. A series of insights are further explored. Based on these, we discuss the performance under high SNR case, as well as, weak interference case. And then, the approximations in simpler form expressions are provided for each case, respectively. Outage diversity order and high SNR rate slope are also investigated. In addition, we propose a power allocation algorithm to maximize the ergodic achievable sum rate guaranteeing the outage probability and average energy efficiency. Numerical results show that our analytical results agree well with the Monte Carlo simulations in various network configurations. Besides, our proposed power allocation scheme significantly enhances the ergodic achievable sum rate compared with the equal power strategy.

This paper studies a simultaneously transmitting and reflecting reconfigurable intelligent surface aided non-orthogonal multiple access system with two paired users. To ensure the quality of service requirement of the reflected user, we propose a joint power and discrete amplitude allocation scheme, which can reduce channel estimation workload and hardware complexity. To characterize the performance of our proposed scheme, a novel channel statistic over Nagakami-$m$ fading is derived by adopting Beaulieu series. Subsequently, closed-from expressions for outage probability and diversity order of the reflected user are obtained, respectively. Moreover, owing to the complex channel statistic of the transmitted user, we analyze a lower bound of outage probability for the transmitted user. Finally, numerical results confirm that our proposed scheme greatly improves the performance of the reflected user and yields a larger throughput in comparison with the existing work.

Semantic communication in the 6G era has been deemed a promising communication paradigm to break through the bottleneck of traditional communications. However, its applications for the multi-user scenario, especially the broadcasting case, remain under-explored. To effectively exploit the benefits enabled by semantic communication, in this paper, we propose a one-to-many semantic communication system. Specifically, we propose a deep neural network (DNN) enabled semantic communication system called MR_DeepSC. By leveraging semantic features for different users, a semantic recognizer based on the pre-trained model, i.e., DistilBERT, is built to distinguish different users. Furthermore, the transfer learning is adopted to speed up the training of new receiver networks. Simulation results demonstrate that the proposed MR_DeepSC can achieve the best performance in terms of BLEU score than the other benchmarks under different channel conditions, especially in the low signal-to-noise ratio (SNR) regime.

In this paper, the performance of cell-free massive multiple-input multiple-output (CF-mMIMO) systems under the transceiver non-ideal radio-frequency responses (NI-RF-RPs) that rely on the superimposed pilot (SP) and regular pilot (RP) transmission protocols is investigated. Specifically, the transceiver NI-RF-RPs encapsulate the non-ideal amplitude responses (NI-A-RPs) and non-ideal phase responses (NI-P-RPs) at both access points (APs) and users (UEs). By virtue of a generic RF-RP model, rigorous closed-form expressions of uplink (UL) and downlink (DL) achievable spectral efficiencies (SEs) with different NI-RF-RPs and pilot transmission protocols are respectively derived, which enable us to quantify the impact of the key system parameters, such as the transceiver amplitude (and phase) response variances and the transceiver reciprocity. Numerical results demonstrate that the SP protocol is superior to the RP protocol in terms of the UL and DL SEs, regardless of the presence of the NI-RF-RPs. Besides, the NI-A-RPs have negative effect on both UL and DL SEs, and the DL SE is immune to the transceiver amplitude non-reciprocity. Moreover, from the perspective of the NI-P-RPs, it is shown that the DL SE is only sensitive to the AP NI-P-RPs and whether the AP phase is reciprocal or not will significantly disturb the effect of the NI-P-RPs on the DL SE.

Intelligent manufacturing in future factories requires ultra-reliable and low-latency communications (URLLC) between central controllers and actuators (e.g., robots). This letter studies a superimposed pilot (SP)-aided uplink transmission system in industrial scenarios, where each actuator sends short-packet messages containing a superimposition of pilot and data signals to the central controller. A practical setting is considered where the blocklength is finite and imperfect pilot removal (IPPR) is available. Then, a closed-form lower bound of the achievable rate with a maximum ratio combining receiver is derived, which can be easily extended to the special cases of perfect pilot removal (PPR) and regular pilot (RP). Based on the derived lower bound, the pilot and data power allocation is optimized by exploiting successive convex approximation method to transform the weighted sum rate maximization problem into a series of geometric programs. Simulation results reveal that the SP-aided transmission outperforms the RP-aided transmission in both the PPR and IPPR cases.