Aerial reconfigurable intelligent surfaces (ARIS), deployed on unmanned aerial vehicles (UAVs), could enhance anti-jamming communication performance by dynamically configuring channel conditions and establishing reliable air-ground links. However, large-scale ARIS faces critical deployment challenges due to the prohibitive computational complexity of conventional discrete optimization methods and sophisticated jamming threats. In this paper, we introduce a mean-field modeling approach to design the spatial configuration of ARIS by a continuous density function, thus bypassing high-dimensional combinatorial optimization. We consider an adaptive jammer which adjusts its position and beamforming to minimize the sum-rate. A key finding reveals that the jammer’s optimal strategy is governed by a proximity-directivity trade-off between reducing path loss and enhancing spatial focusing. To combat the jamming, we propose a robust anti-jamming transmission framework that jointly optimizes the BS beamforming, the ARIS reflection, and the ARIS spatial distribution to maximize the worst-case sum-rate. By leveraging variational optimization and Riemannian manifold methods, we efficiently solve the functional optimization problems. Our analysis further unveils that the optimal ARIS deployment follows a spatial water-filling principle, concentrating resources in high-gain regions while avoiding interference-prone areas. Simulation results demonstrate that the proposed framework remarkably improves the sum-rate. Furthermore, the computational complexity of the proposed algorithm is independent of the number of UAVs, validating its effectiveness for scalable ARIS-assisted anti-jamming communications.

Multi-reconfigurable intelligent surfaces (RIS) has recently gained significant interest as emerging technology for exploiting Intelligent electromagnetic environment. Inspired by opportunistic communications, a low-complexity adaptive selection and training method for multi-RISs is proposed in this paper. Firstly appropriate number of the multi-RISs is selected to assist communication. The elements of the selected RIS are grouped, and the grouped elements share a common coefficient to reduce training overhead. Secondly, the communication performance is evaluated and other RIS will be selected to assist communication if the communication performance not meet user requirement. In this way, the system performance and the training complexity of multi-RISs can be trade-off efficiently. Simulation results show that the proposed scheme outperforms the state-of-the-art benchmarks in terms of training overhead and robustness in different channel condition, and the training overhead is 30% lower compared to the centralized deployment scheme proposed in [13].

To achieve stringent performance requirements in next generation wireless networks, such as ultra-high data rates, ubiquitous connectivity, and extremely high reliability, this paper proposes a radically novel rate splitting assisted dual-polarized stacked metasurface (RS-DPSM) transceiver architecture. In this architecture, a multi-layer dual-polarized metasurface is stacked at the active antennas and its two inherent polarizations are implemented to enable RS’s common and private messages in parallel. In sharp contrast to the conventional multiple-input multiple-output (MIMO) and metasurface-based transceiver designs, our proposed transceiver is capable of enhancing the channel capacity and introducing multi-dimensional degrees of freedom (DoFs) in the power, spatial, and polarization domains, thus enabling multi-functional, broad-spectrum, and all-time/domain/space communications without requiring massive radio-frequency (RF) chains. In addition, we derive new analytical expressions for the upper bounds of RS-DPSM transceiver’s channel capacity and ergodic sum rate, and provide some key insights. To highlight its potential benefits, we apply the proposed RS-DPSM transceiver to anti-jamming communications, and formulate a generalized sum rate maximization problem under the jammer’s imperfect angular channel state information and unknown cross-polarization discrimination. To enable an efficient resource management under the above practical conditions, we present a low-complexity optimization framework by leveraging the discretization method, properties of the quadratic function, reduced-majorization-minimization algorithm, and block successive upper-bound minimization, which admit the semi-closed-form solutions. Finally, our numerical simulations verify the superiority of our proposed transceiver architecture and optimization framework over key benchmarks.

Due to the users' overlapping channels and the open nature of the wireless medium, inter-user interference and malicious jamming attacks deteriorate the performance of unmanned aerial vehicle (UAV) communications. With this focus, this paper proposes a novel integration of dual polarization, rate-splitting multiple access (RSMA), and stacked reconfigurable intelligent surface (RIS) transceiver into UAV networks, thus simultaneously mitigating the inter-user interference and malicious interference by fully exploiting their potentials in the power, space, and polarization domains. Building upon this architectural framework, a generalized sum rate maximization problem is formulated under the jammer's imperfect angular channel state information and unknown cross-polarization discrimination. To efficiently tackle the challenges posed by the intractable non-convex design problem with both high-dimensional variables and the multiple QoS constraints, a low-complexity optimization framework is presented, where a discretization method combined with quadratic property, a reduced-majorization-minimization algorithm, and two computationally efficient algorithms using block successive upper-bound minimization are developed to obtain the semi-closed-form solutions. Finally, numerical simulations verify the superiority and validity of our proposed architecture and optimization framework over benchmarks.

Mobile edge computing-assisted integrated aerial-ground network (MEC-IAGN) emerges as a promising key component of the sixth-generation (6G) wireless networks due to its potential capabilities in providing ubiquitous connectivity for global coverage and computing services. However, the inevitable existences of computation-intensive tasks, uncontrollable propagation environment, and malicious jamming attacks pose three significant bottlenecks for enabling efficient MEC-IAGN. With these focuses, we propose a novel framework of multi-functional reconfigurable intelligent surface (MF-RIS) aided semantic anti-jamming communication and computing in MEC-IAGN. Under this framework, a semantic transceiver exhibits inherent robustness and data compression capability, and MF-RIS can customize the full-space wireless environment by leveraging its signal reflection, refraction, amplification, and energy harvesting functions, thereby achieving substantial global coverage, reliable connectivity, and high-rate computing. Based on our proposed framework, we formulate a semantic computation rate maximization problem considering the impacts of jammer’s channel state information (CSI) imperfection, while maintaining the energy partition constraint for computation offloading decision, semantic similarity requirement, semantic computation rate target, and MF-RIS’s self-sustainability. Then, by transforming the imperfect CSI into a worst-case one by exploiting a discretization method, we propose a fast-converging monotonic optimization algorithm that is combined with decoupling second-order cone programming to obtain a globally optimal solution with fewer feasibility evaluations. Furthermore, to strike a satisfactory tradeoff between performance and computational complexity, we develop a suboptimal generalized power iteration algorithm. Numerical simulations demonstrate the superiority of our proposed framework and algorithms compared to various benchmarks.

Aiming to circumvent the severe large-scale fading and the energy scarcity dilemma in high-altitude platform (HAP) networks, this paper investigates the benefits of the reconfigurable intelligent surface (RIS) and simultaneous wireless information and power transfer (SWIPT) on HAP communications. Specifically, we propose a concept of multi-layer refracting RIS-assisted receiver to achieve concurrent transmission of the information and energy, which is conducive to overcoming the severe fading effect induced by extreme long-distance HAP links and fully exploits RIS’s degrees-of-freedom (DoFs) for the SWIPT design. Based on the RIS-enhanced receiver, we then formulate a worst-case sum-rate maximization problem by considering the channel state information (CSI) error, the information rate requirements, and the energy harvesting constraint. To handle the intractable non-convex problem, a scalable robust optimization framework is proposed to obtain semi-closed-form solutions. Specifically, a discretization method is adopted to convert the imperfect CSI into a robust one. Then, by utilizing the LogSumExp inequality to smooth the objective and constraints, we develop a dual method to obtain the optimal solution for the HAP transmit precoder. In addition, a modified cyclic coordinate descent (M-CCD) is adopted to update the block-wise RIS coefficients. Moreover, closed-form solutions for power splitting (PS) ratios and the receive decoder are derived. Finally, the asymptotic performance of our proposed RIS-enhanced receiver is provided to reveal the substantial capacity gain for HAP communications. Numerical simulations demonstrate that the proposed architecture and optimization framework are capable of achieving superior performance with low complexity compared to state-of-the-art schemes in HAP networks.

In this paper, for a single-input multiple-output (SIMO) system aided by a passive reconfigurable intelligent surface (RIS), the joint transmission accomplished by the single transmit antenna and the RIS with multiple controllable reflective elements is considered. Relying on a general capacity upper bound derived by using a maximum-trace argument, we respectively characterize the capacity slope of low-signal-to-noise-ratio channels and the exact capacity of rank-one channels, in which the optimal configuration of the RIS is proved to be beamforming-only with carefully-chosen phase shifts. To exploit the potential of modulating extra information on the RIS, by leveraging a strategy named partially beamforming and partially information-carrying based on QR decomposition and successive interference cancellation, we propose a novel transceiver architecture with only a single RF front end at the transmitter, by which the considered channel can be regarded as a concatenation of a vector Gaussian channel and several phase-modulated channels. Especially, we investigate a class of vector Gaussian channels with a hypersphere input support constraint, and not only generalize the existing result to arbitrary-dimensional real spaces but also present its high-order capacity asymptotics, by which both capacities of hypersphere-constrained channels and achievable rates of the proposed transceiver with two different signaling schemes can be well-approximated. Information-theoretic analyses show that the transceiver architecture designed for the SIMO channel has a boosted multiplexing gain, rather than one for the conventionally-used optimized beamforming scheme. Numerical results verify our derived asymptotic results and show notable superiority of the proposed transceiver as compared with the beamforming and the receive spatial modulation schemes.

The utilization of a large-scale antenna array has led to substantial performance improvements in anti-jamming communications. However, due to the practical constraints of hardware cost and power consumption, deploying such a large-scale antenna array at the user side is impractical. Inspired by the remarkable advantages of reconfigurable intelligent surfaces (RIS), we propose an active-passive cascaded RIS-aided receiver architecture that facilitates the cost- and energy-efficient deployment of a large-scale antenna array at the user side, while also providing additional degrees-of-freedom for effective beamforming design. Building upon this architectural framework and taking into account the practical imperfections in the angular channel state information (CSI), we formulate a worst-case achievable rate maximization problem for anti-jamming communications. To address the challenges posed by the intractable non-convex design problem, we present a low-complexity optimization framework that obtains semi-closed-form solutions. Specifically, we first develop a Pareto-dual scheme to handle the general power constraints in devising the optimal precoder for the base station. Subsequently, by introducing a novel anti-jamming criterion and employing the discretization method to transform the imperfect CSI of jammers into a robust form, we derive two jamming-nulling feasibility conditions and a unified unit-modulus zero-forcing scheme to determine the coefficients of the passive RIS. To strike a satisfactory balance between complexity and performance, we further design three computationally-efficient algorithms based on alternating majorization-minimization (AMM) and conventional/modified cyclic coordinate descent (C/M-CCD) methods to obtain the coefficients of the active RIS. Finally, through comprehensive numerical simulations, we validate the effectiveness of the proposed architecture and optimization framework, demonstrating their capacity to achieve exceptional performance in a cost-effective manner.

To mitigate the severe large-scale fading and the energy scarcity problem in long-distance high-altitude platform (HAP) networks, in this paper, we investigate the potentials of a multi-layer refracting reconfigurable intelligent surface (RIS) -assisted receiver for enabling simultaneous wireless information and power transfer (SWIPT) in HAP networks. Unlike the existing RIS-aided reflector and transmitter, the multi-layer RIS-receiver can well overcome the severe “double fading” effect induced by the extreme long-distance HAP links and fully exploit RIS's degrees-of-freedom (DoFs) for SWIPT design. Building on the proposed RIS-receiver, this paper formulates a worst-case sum rate maximization problem under angular channel state information (CSI) imperfection, while satisfying the information rate requirements of the earth stations (ESs) and the harvested energy constraint. To handle the intractable non-convex problem, a scalable robust optimization framework utilizing the discretization method, LogSumExp-dual scheme, and modified cyclic coordinate descent (M-CCD) is proposed to obtain the semi-closed-form solutions. Numerical simulations demonstrate that the proposed architecture and optimization framework achieve superior performance with lower complexity compared with state-of-the-art schemes in HAP networks.

The use of a large-scale antenna array has achieved significant performance gains in anti-jamming communications. However, due to the hardware cost and power consumption constraints, it is impractical to deploy such large-scale antenna array at the user side. Inspired by the remarkable advantages of reconfigurable intelligent surface (RIS), we propose an active-passive cascaded RIS-aided receiver architecture, which facilitate the deployment of a large-scale antenna array at the user side in a cost- and energy-efficient way and provides additional degree-of-freedom for beamforming design. Building upon this architecture and considering the practical angular channel state information (CSI) imperfection, a worst-case achievable rate maximization problem is formulated for anti-jamming communications. To handle the non-convex problem, a low-complexity optimization framework is proposed, where the new anti-jamming criterion, Pareto-dual scheme, unified unit-modulus zero-forcing scheme, and conventional-cyclic coordinate descent algorithm are developed to obtain the semi-closed-form solutions. Finally, numerical simulations verify that the proposed architecture and optimization framework are capable of achieving excellent performance with low complexity.

In multimodal video event localization, we usually leverage feature fusion across different axes, such as the modality and temporal axes, for better context. To reduce the costs of detailed annotations, recent solutions explore weakly supervised settings. However, we observe that when feature fusion meets weakly supervised localization, problems can occur. It may cause "feature cross-interference", which produces a smearing effect on the localization result and can’t be effectively supervised with conventional multiple instance learning loss. We verify it quantitatively on the audio-visual video parsing (AVVP) task, and propose a cross-instance data-augmentation framework, which can preserve the benefits of feature fusion while providing explicit feedbacks for feature cross-interference. We show that our method can enhance performance of existing models on two weakly supervised audio-visual localization tasks, i.e. AVVP and AVE.

Malicious jamming attacks have been regarded as a serious threat to Internet of Things (IoT) networks, which can significantly degrade the Quality of Service (QoS) of users. This article utilizes an intelligent reflecting surface (IRS) to enhance anti-jamming performance due to its capability in reconfiguring the wireless propagation environment via dynamically adjusting each IRS reflecting elements. To enhance the communication performance against jamming attacks, a robust beamforming optimization problem is formulated in a multiuser IRS-assisted anti-jamming communications scenario with or without imperfect jammer’s channel state information (CSI). In addition, we further consider the fact that the jammer’s transmit beamforming can not be known at BS. Specifically, with no knowledge of jammers transmit beamforming, the total transmit power minimization problems are formulated subject to the outage probability requirements of legitimate users with the jammer’s statistical CSI, and signal-to-interference-plus-noise ratio requirements of legitimate users without the jammer’s CSI, respectively. By applying the decomposition-based large deviation inequality, Bernstein-type inequality, Cauchy–Schwarz inequality, and penalty nonsmooth optimization method, we efficiently solve the initial intractable and nonconvex problems. Numerical simulations demonstrate that the proposed anti-jamming approaches achieve superior anti-jamming performance and lower power-consumption compared to the non-IRS scheme and reveal the impact of key parameters on the achievable system performance.

The integration of aerial platforms to provide ubiquitous coverage and connectivity for densely deployed terrestrial networks is expected to be a reality in the emerging sixth-generation networks. Energy-effificient and secure transmission designs are two important components for integrated terrestrial-aerial networks (ITAN). Inlight of the potential of reconfigurable intelligent surface (RIS) for significantly reducing the system power consumption and boosting information security, this paper proposes a multi-layer RIS-assisted secure ITAN architecture to defend against simultaneous jamming and eavesdropping attacks, and investigates energy-efficient hybrid beamforming for it. Specifically, with the availability of imperfect angular channel state information (CSI), we propose a block coordinate descent (BCD) framework for the joint optimization of the user’s received decoder, the terrestrial and aerial digital precoder, and the multi-layer RIS analog precoder to maximize the system energy efficiency (EE) performance. For the design of the received decoder, a heuristic beamforming scheme is proposed to convert the worst-case design problem into a min-max one and facilitate the developing a closed-form solution. For the design of the digital precoder, we propose an iterative sequential convex approximation approach via capitalizing the auxiliary variables and first-order Taylor series expansion. Finally, a monotonic vertex-update algorithm with a penalty convex-concave procedure (P-CCP) is proposed to obtain the analog precoder with satisfactory performance. Numerical results show the superiority and effectiveness of the proposed optimization framework and architecture over various benchmark schemes.

Wireless communications are increasingly vulnerable to simultaneous jamming and eavesdropping attacks due to the inherent broadcast nature of wireless channels. With this focus, due to the potential of reconfigurable intelligent surface (RIS) in substantially saving power consumption and boosting information security, this paper is the first work to investigate the effect of the RIS-assisted wireless transmitter in improving both the spectrum efficiency and the security of multi-user cellular network. Specifically, with the imperfect angular channel state information (CSI), we aim to address the worst-case sum rate maximization problem by jointly designing the receive decoder at the users, both the digital precoder and the artificial noise (AN) at the base station (BS), and the analog precoder at the RIS, while meeting the minimum achievable rate constraint, the maximum wiretap rate requirement, and the maximum power constraint. To address the non-convexity of the formulated problem, we first propose an alternative optimization (AO) method to obtain an efficient solution. In particular, a heuristic scheme is proposed to convert the imperfect angular CSI into a robust one and facilitate the developing a closed-form solution to the receive decoder. Then, after reformulating the original problem into a tractable one by exploiting the majorization-minimization (MM) method, the digital precoder and AN can be addressed by the quadratically constrained quadratic programming (QCQP), and the RIS-aided analog precoder is solved by the proposed price mechanism-based Riemannian manifold optimization (RMO). To further reduce the computational complexity of the proposed AO method and gain more insights, we develop a low-complexity monotonic optimization algorithm combined with the dual method (MO-dual) to identify the closed-form solution. Numerical simulations using realistic RIS and communication models demonstrate the superiority and validity of our proposed schemes over the existing benchmark schemes.

Support recovery from multiple measurement vectors has been regarded as a critical aspect of compressive sensing. Most existing recovery algorithms require the prior knowledge of the sparsity or the noise power, which are unknown or even time-varying in actual applications, to determine the termination condition of the iterative recovery process. Motivated by the entropy concept from information theory, a frequency-domain entropy (FDE)-based blind support recovery algorithm is proposed, where the FDE is employed as the test statistic to determine whether there is a sparse signal remains in the residual signal. Specifically, the statistical distribution of the residual signal is proved to be Gaussian when the recovery iterations are adequately satisfied. Then, the cumulative distribution function of the test statistic and the closed-form expressions of the stop threshold are derived. Our proposed algorithm exploits the merit that the FDE is uncorrelated with the noise power, thus the estimation of the noise power is not required. Simulation results illustrate that the proposed algorithm is robust to different signal-to-noise ratios.

A network coding based energy-efficient multi-path routing algorithm is presented for wireless sensor network. This algorithm, developed from the ReInForM routing algorithm, considers the data redundancy optimization and residual energy balance of node, which can prolong the network lifetime, balance the network energy consumption and reduce the energy consumption of global network to extreme extent in the wireless sensor network of uneven node energy. The simulation results show that this algorithm, compared with other sensor routing algorithm, may consume less energy under the same channel error rate and network scale, and balance more efficiently the network energy consumption as the network scale increases.