Movable antennas have recently emerged as a promising paradigm to overcome the inherent inflexibility of conventional fixed antenna arrays. By enabling the physical movement of antenna elements, movable antennas introduce additional spatial degrees of freedom to wireless systems. Although the importance of the movement delay has been recognized, a critical yet unexplored problem is that the movement schemes used to transition from the initial to the target positions are overlooked. This paper presents a systematic investigation of two fundamental movement schemes: concurrent movement and cumulative movement, and addresses a key design question: Should we prioritize minimizing the total configuration time or maximizing the communication performance under a limited movement budget? Specifically, two different optimization problems are formulated to maximize the sum rate under different movement constraints, thereby introducing tighter coupling between antenna positions and beamforming design, increasing computational complexity in joint optimization, and necessitating efficient allocation of delay budgets across multiple antennas. To this end, we develop an alternating-optimization-based algorithm to obtain the corresponding suboptimal solutions. A theoretical degeneration analysis is further conducted to provide fundamental insights. The optimal strategy for a single antenna can surprisingly be to not move. While in multi-antenna systems, the performance gap scales with antenna displacement, movement budgets, and transmit power. Simulation results show that movable antennas substantially improve achievable rates over fixed antennas, with concurrent movement benefiting low-latency scenarios, while cumulative movement favoring high-rate or delay-tolerant scenarios.

Deep video compression has made significant progress in recent years, achieving rate-distortion performance that surpasses that of traditional video compression methods. However, rate control schemes tailored for deep video compression have not been well studied. In this paper, we propose a neural network-based$\lambda$-domain rate control scheme for deep video compression, which determines the coding parameter$\lambda$for each to-be-coded frame based on the rate-distortion-$\lambda\ (\mathrm{R}-\mathrm{D}-\lambda)$relationships directly learned from uncompressed frames, achieving high rate control accuracy efficiently without the need for pre-encoding. Moreover, this content-aware scheme is able to mitigate inter-frame quality fluctuations and adapt to abrupt changes in video content. Specifically, we introduce two neural network-based predictors to estimate the relationship between bitrate and$\lambda$, as well as the relationship between distortion and$\lambda$for each frame. Then we determine the coding parameter$\lambda$for each frame to achieve the target bitrate. Experimental results demonstrate that our approach achieves high rate control accuracy at the mini-GOP level with low time overhead and mitigates inter-frame quality fluctuations across video content of varying resolutions.

The phase shift information (PSI) overhead poses a critical challenge to enabling real-time intelligent reflecting surface (IRS)-assisted wireless systems, particularly under dynamic and resource-constrained conditions. In this paper, we propose a lightweight PSI compression framework, termed meta-learning-driven compression and reconstruction network (MCRNet). By leveraging a few-shot adaptation strategy via model-agnostic meta-learning (MAML), MCRNet enables rapid generalization across diverse IRS configurations with minimal retraining overhead. Furthermore, a novel depthwise convolutional gating (DWCG) module is incorporated into the decoder to achieve adaptive local feature modulation with low computational cost, significantly improving decoding efficiency. Extensive simulations demonstrate that MCRNet achieves competitive normalized mean square error performance compared to state-of-the-art baselines across various compression ratios, while substantially reducing model size and inference latency. These results validate the effectiveness of the proposed asymmetric architecture and highlight the practical scalability and real-time applicability of MCRNet for dynamic IRS-assisted wireless deployments.

Mobile edge computing (MEC) has been regarded as a promising technique to support latency-sensitivity and computation-intensive serves. However, the low offloading rate caused by the random channel fading characteristic becomes a major bottleneck in restricting the performance of the MEC. Fortunately, reconfigurable intelligent surface (RIS) can alleviate this problem since it can boost both the spectrum- and energy-efficiency. Different from the existing works adopting either fully active or fully passive RIS, we propose a novel hybrid RIS in which reflecting units can flexibly switch between active and passive modes. To achieve a tradeoff between the latency and energy consumption, an optimization problem is formulated by minimizing the total cost via jointly optimizing the transmission time, the transmit power, the receive beamforming vector, the phase-shift matrix, the mode-switching factor, the amplification factor, the offloading ratio factor, and the computation ability of the user, where the constraints of the maximum energy of users, the maximum power of the RIS, the minimum computation tasks, the transmission time, the offloading ratio factor, the mode-switching factor, the unit moduli of passive units, and the computation ability are taken into account. Considering the complexity of the aforementioned problem, we develop an alternating optimization-based iterative algorithm by combining the successive convex approximation method, the variable substitution, and the singular value decomposition to obtain sub-optimal solutions. Furthermore, in order to gain more insight into the problem, we consider two special cases involving a latency minimization problem and an energy consumption minimization problem, and respectively analyze the tradeoff between the number of active and passive units. Simulation results verify that the proposed algorithm can achieve flexible mode switching and significantly outperforms existing algorithms.

Cognitive radio has been suggested as a solution to address the shortage of accessible spectrum caused by the significant demand for wideband services and the fragmentation of spectrum resources. Nevertheless, the sensing performance is rather inadequate owing to the diminished sensing signal-to-noise ratio, especially in complex environments with severe channel fading. Fortunately, applying reconfigurable intelligent surfaces (RIS) for spectrum sensing can efficiently address the aforementioned problems. However, the passive RIS may experience the “double fading” effect, seriously limiting the effectiveness of passive RIS-aided spectrum sensing. Thus, a crucial challenge is how to fully exploit the potential advantages of the RIS and further improve the sensing performance. In this paper, we utilize the passive and active RIS to further enhance detection probability and subsequently develop two different problems for both the passive and active RIS to achieve the detection probability maximization. Considering the complexity of the above problems, we design a one-stage optimization algorithm featuring inner approximation and a two-stage optimization algorithm that employs the bisection method to derive corresponding solutions, and further establish the upper bound and lower bound of the detection probability by employing the Rayleigh quotient. Moreover, we separately explore how many reflecting elements are needed for passive RIS and active RIS and investigate the detection performance comparison of the two types (passive and active) of RIS. Simulation results show that the proposed algorithms outperform existing algorithms under the same parameter configuration, and achieve a detection probability close to 1 with even fewer reflecting elements or antennas than existing schemes.

In this article, we investigate and analyze energy recycling for a reconfigurable intelligent surface (RIS)-aided wireless-powered communication network. As opposed to the existing works where the energy harvested by Internet of Things (IoT) devices only comes from the power station, IoT devices are also allowed to recycle energy from other IoT devices. In particular, we propose group switching- and user switching-based protocols with time-division multiple access to evaluate the impact of energy recycling on the system performance. Two different optimization problems are, respectively, formulated for maximizing the sum throughput by jointly optimizing the energy beamforming vectors, the transmit power, the transmission time, the receive beamforming vectors, the grouping factors, and the phase-shift matrices, where the constraints of the minimum throughput, the harvested energy, the maximum transmit power, the phase shift, the grouping, and the time allocation are taken into account. In light of the intractability of the above problems, we, respectively, develop two alternating optimization-based iterative algorithms by combining the successive convex approximation method and the penalty-based method to obtain corresponding suboptimal solutions. Simulation results verify that the energy recycling-based mechanism can assist in enhancing the performance of IoT devices in terms of energy harvesting and information transmission. Besides, we also verify that the group switching-based algorithm can obtain more sum throughput of IoT devices, and the user switching-based algorithm can harvest more energy.

Backscatter communication (BackCom), one of the core technologies to realize zero-power communication, is expected to be a pivotal paradigm for the next generation of the Internet of Things (IoT). However, the “strong” direct link (DL) interference (DLI) is traditionally assumed to be harmful, and generally drowns out the “weak” backscattered signals accordingly, thus deteriorating the performance of BackCom. In contrast to the previous efforts to eliminate the DLI, in this paper, we exploit the constructive interference (CI), in which the DLI contributes to the backscattered signal. To be specific, our objective is to maximize the received signal-to-noise ratio (SNR) by jointly optimizing the receive beamforming vectors and tag selection factors under different detection error probability (DEP) requirements, which leads to two different optimization problems. However, the resulting problems are non-convex and unanalyzable due to constraints on the DEP. To solve these problems, the Kullback-Leibler divergence is first applied to transform the DEP into a tractable form. Then, inspired by the alternating optimization, we respectively propose two successive convex approximation (SCA)-based algorithms to solve the corresponding sub-problems with beamforming design, and a greedy algorithm to solve the sub-problem with tag selection. In order to gain insight into the CI, we consider a special case with the single-antenna reader to reveal the channel angle between the backscattering link (BL) and the DL, in which the DLI will become constructive. Simulation results show that significant performance gain can always be achieved with the proposed algorithms compared to the traditional algorithms without the CI in terms of the received SNR. The derived constructive channel angle for the BackCom system with a single-antenna reader is also confirmed by simulation results.

Energy efficiency (EE) is a crucial performance metric in wireless-powered backscatter communication networks (WP-BackComNets) for achieving a good tradeoff between data rates and the overall energy consumption, which however has not been sufficiently exploited by the existing works. In this paper, an EE-based maximization resource allocation (RA) problem is studied in a downlink orthogonal frequency division multiple access-based WP-BackComNet, where the circuit power consumption of the backscatter device, the minimum energy harvesting (EH) constraint, and the maximum transmit power constraint of the power station are considered. To deal with the non-convex problem, we firstly transform it into an equivalently subtractive form via Dinkelbach's method. Then, we apply a variable substitution approach to transform the non-convex problem into a convex one, where the closed-form solutions of the reflection coefficient, the transmit power, and the EH time are deduced by using Lagrange dual method. Simulation results demonstrate that the proposed algorithm can achieve better EE performance than other benchmark algorithms.

Nowadays, smart contracts manage more and more digital assets and have become an attractive target for adversaries. To prevent smart contracts from malicious attacks, a thorough test is indispensable and must be finished before deployment because smart contracts cannot be modified after being deployed. Fuzzing is an important testing approach, but most existing smart contract fuzzers can hardly solve the constraints which involve deeply nested conditional statements, resulting in low coverage. To address this problem, we propose Targy, an efficient targeted mutation strategy based on dynamic taint analysis. We obtain the taint flow by dynamic taint propagation, and generate a more accurate mutation strategy for the input parameters of functions to simultaneously satisfy all conditional statements. We implemented Targy on sFuzz with 3.6 thousand smart contracts running on Ethereum. The numbers of covered branches and detected vulnerabilities increase by 6% and 7% respectively, and the average time required for covering a branch is reduced by 11 %.