Benefiting from the flexible deployment capability and favorable propagation condition, the unmanned aerial vehicle (UAV) relay networks are crucial for rapid coverage extension and resilient connectivity in adversarial communication environments. However, the transmission effectiveness is constrained by scarce energy resource and severe hostile jamming attacks. Motivated by this need, this paper investigates the semantic communication (SemCom) empowered UAV relay networks that integrates SemCom with simultaneous wireless information and power transfer (SWIPT). For the first time, the semantic encoding/ decoding is embedded into the two-hop communication while the power-splitting (PS) jointly coordinates information transmission and energy harvesting. With the imperfect channel state information (CSI), we formulate the worst-case sum SemCom rate maximization problem subject to the constraints of semantic decoding feasibility, energy harvesting and transmit power. To address the intractable problem, we develop a robust three-step monotonic-optimization (MO) framework with low-complexity feasibility checking algorithm (LFCA). Firstly, a robust generalized discretization approach is used to convert the imperfect CSI. Then, the MO framework with LFCA is presented to obtain optimal solution of transmit beamformer, receive decoder and PS ratio with dimension reduction method. Simulations results show that proposed MO-LFCA scheme outperforms conventional bit-level SWIPT scheme and the other benchmark schemes versus different jamming power levels and antenna configurations, demonstrating that the integration of SemCom and SWIPT mechanism can enhance anti-jamming and efficiency performance in UAV relay networks.

Massive multiple-input multiple-output (MIMO) systems with one-bit analog-to-digital converters (ADCs) face a severe trade-off between hardware efficiency and channel estimation accuracy. While generative adversarial networks (GANs) show promise for this challenge, their deployment is hindered by training instability and mode collapse. To address these issues, we propose ReRpGAN, a novel adversarial learning framework that integrates a regularized relativistic pairing GAN loss and anL1loss within a deep residual network. This architecture effectively stabilizes the training process and prevents mode collapse, enabling precise channel reconstruction from severely quantized signals. Extensive experiments on a realistic ray-tracing channel dataset validate our theoretical claims. Key findings demonstrate that ReRpGAN consistently outperforms conventional GAN-based and deep learning estimators, particularly in challenging scenarios with low signal-to-noise ratios and limited pilot overhead. Furthermore, unlike existing methods that suffer from divergence, ReRpGAN exhibits superior scalability, delivering improved estimation accuracy as the number of base station antennas increases. This work sets a new benchmark for robust, data-driven channel estimation in next-generation wireless systems.

The widespread integration of artificial intelligence (AI) in next-generation communication networks poses a serious threat to data privacy while achieving advanced signal processing. Eavesdroppers can use AI-based analysis to detect and reconstruct transmitted signals, leading to serious leakage of confidential information. In order to protect data privacy at the physical layer, we redefine covert communication as an active data protection mechanism. We propose a new parasitic covert communication framework in which communication signals are embedded into dynamically generated interference by generative adversarial networks (GANs). This method is implemented by our CDGUBSS (complex double generator unsupervised blind source separation) system. The system is explicitly designed to prevent unauthorized AI-based strategies from analyzing and compromising signals. For the intended recipient, the pretrained generator acts as a trusted key and can perfectly recover the original data. Extensive experiments have shown that our framework achieves powerful covert communication, and more importantly, it provides strong defense against data reconstruction attacks, ensuring excellent data privacy in next-generation wireless systems.

Satellite communications (SatCom) face fundamental security challenges due to their inherent broadcast nature. To address this, we exploit beyond-diagonal reconfigurable intelligent surface (BD-RIS) to unleash its full-scattering matrix control for enhanced secure beamforming flexibility in SatCom with rate-splitting multiple access (RSMA), where the satellite attempts to convey private signals to legitimate users with blocked direct downlinks and multiple eavesdroppers. To maximize the worst-case secrecy rate among legitimate users, a max-min fairness (MMF) problem is formulated with imperfect wiretap channel state information (CSI) via joint precoding, RIS configuration, and rate splitting optimization. By using the block coordinate descent (BCD) method, these optimization variables are decoupled with different subproblems and solved by the penalty dual decomposition (PDD) method iteratively. Furthermore, we develop a computationally efficient suboptimal solution that employs diagonal RIS (D-RIS) with reduced hardware and computational complexity, where alternating optimization (AO) and successive convex approximation (SCA) methods are employed to solve the non-convex problem. Simulation results demonstrate that our proposed BD-RIS-RSMA scheme achieves significant performance improvements compared to baseline schemes, while the suboptimal diagonal RIS scheme offers a favorable performance-complexity tradeoff.

In the context of future 6G networks, reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA) are emerging as pivotal technologies for enhancing signal quality and eliminating coverage blind spots. This paper addresses the issue of secure and resilient transmission in RISassisted NOMA systems. Specifically, the base station transmits private signals to multiple legitimate users while dealing with the threat of potential eavesdropping. To model this challenge, we optimize the beamforming vectors and the RIS phase-shift matrix to maximize the sum secrecy rate while satisfying the user quality of service (QoS) requirements and the power constraints of the base station. Since the problem involves high-dimensional variables and non-convex objective functions, it is difficult to be solved by traditional optimization methods. Therefore, the twin delayed deep deterministic policy gradient algorithm (TD3) based on deep reinforcement learning (DRL) is proposed in this paper to effectively address the complexity of the original problem. Numerical results show that the proposed scheme exhibits satisfactory performance in improving communication security, transmission efficiency, and resistance to channel errors.

Aiming to circumvent the low energy efficiency (EE) dilemma of multiple-input multiple-output (MIMO) systems induced by employing hundreds of antennas, this paper investigates the potentials of stacked metasurface (SM) and semantic communications (SemCom) for achieving energy-efficient holographic communications in MIMO systems. Specifically, SM enables hybrid beamforming with increased degrees of freedom (DoFs) and reduced energy consumption, while SemCom transmits dramatically compressed key informantion that comes with low power consumption and high EE. To this end, we formulate a worstcase semantic EE (Sem-EE) maximization problem in terms of the transmit beamformer and SM's phase shifts. By proposing a semantic majorization-minimization to handle the fractional and quasi-convex Sem-EE form, quadratically constrained quadratic programs and cyclic coordinate descent can be exploited to solve the optimization variables with low computational complexity. Numerical simulations demonstrate the enhanced EE performance of SMaided semantic beamforming scheme compared to the conventional MIMO systems.

Malicious jamming attacks pose a significant threat to the integrity and performance of Internet of Things (IoT) networks. However, many jamming patterns are rare or infrequent, which makes them difficult to counter effectively. This article addresses the critical issue of anti-jamming (AJ) under few-shot sample conditions in IoT networks. Source separation is a key component of AJ communication. Although deep learning-based source separation has demonstrated significant advantages, it typically requires a large amount of labeled data, which can be impractical in certain environments. To overcome this challenge, we propose two novel schemes that leverage multitask learning (MTL) and meta-learning (ML) to enhance the model’s signal separation capabilities within the constraints of limited sample scenarios. MTL enhances robustness by leveraging shared representations across tasks, while ML allows for rapid adaptation to novel jamming signals with minimal samples. Specifically, we employ a modified separation model, SepFormer, as our baseline and integrate MTL and ML schemes to enable the separation of unknown or few-shot jamming signals. Additionally, we have constructed two datasets encompassing both simulated and real-world environmental data to test and evaluate the performance of the proposed methods. Simulation results demonstrate the superior AJ performance of our schemes, particularly when compared with a direct application of the separation model with few-shot samples. Furthermore, our evaluation of performance across various jamming scenarios and interference-to-signal ratios (ISRs) further confirms the effectiveness of our proposed scheme.

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.

In ground-air networks, dual-function radar and communication (DFRC) enables a base station (BS) to sense and send communication signals to unmanned aerial vehicles (UAVs) simultaneously, which has enormous potential. The emphasis of this letter is on the design of DFRC waveform for ground-air system under inaccurate target angle. The desired covariance matrix is considered to have the best radar performance in the presence of inaccurate target angle and the waveform design objective is to make the corresponding covariance matrix approximated to it. The constraints conclude multi-user interference (MUI) energy for high quality of communication and constant modulus for avoiding signal distortion. To address the problem that minimizes the covariance matrix difference under the constraints, we propose a block coordinate descent (BCD) method. Moreover, to ensure the problem feasible, we design an initial value acquisition strategy using bisection-based alternating direction method of multipliers (ADMM). Numerical results indicate that better communication and radar performance are achieved by the proposed method compared with existing methods.

Multiple-input multiple-output (MIMO) technology can effectively improve transmission throughput and reliability by utilizing spatial wireless resources, which has aroused widespread research attentions. Comparing with the stationary communication environment considered in most studies, the nonstationary channel may cause severe performance degradation of MIMO technology. This promotes the research of adaptive MIMO transmission strategy, which can intelligently adapt to dynamic environment and provide reliable and efficient communication. In this paper, our purpose is to design an intelligent MIMO system that can adjust the MIMO transmission mode and modulation order according to nonstationary environment. The dynamic decision problem is formulated as a markov decision process (MDP) and the state, action and reward function are designed. Then, an adaptive MIMO transmission strategy via leveraging proximal policy optimization (PPO) learning framework is proposed. The trained PPO agent can learn the proper joint transmission strategy so as to maximize the spectral efficiency subject to the constraint of target bit error rate (BER) performance. Simulation results demonstrate that the proposed scheme can achieve significant performance improvement over benchmark schemes.

Hostile jamming and eavesdropping threats bring severe challenges to reliable and secure communication demands of future networks. In light of the potentials of high-altitude platform (HAP) providing wide communication coverage with low cost and Ad-hoc network facilitating flexible access without support by hardware infrastructure, this paper proposes a multi-HAPs assisted air-terrestrial integrated Ad-hoc networks (HAIN) framework to defend against jamming and eavesdropping simultaneously. Specifically, the HAPs align the beamformer to the terrestrial users while nullifying the reception of eavesdropper. In addition, the Ad-hoc network enables cooperative anti-jamming transmission, where the cooperative users (CUs) provide communication assistance by forming anti-jamming coalition for blocked users (BUs). Building upon this framework, we aim to maximize the sum rate of BUs by jointly optimizing the beamforming and cooperative coalition formation with the imperfect channel state information (CSI). To handle the intractable problem, we first convert the imperfect CSI into the worst-case one, and then a sequential convex approximation combined with first order Taylor series expansion is proposed to optimize the beamforming. Furthermore, for the optimization of anti-jamming coalition formation, we reformulate it as the coalition formation game (CFG) and a partial best coalition preference order is put forward to enhance the sum rate of BUs. With the help of exact potential game (EPG), it’s proved that the CFG can converge to stable coalition formation by exploiting the proposed distributed anti-jamming coalition formation algorithm. Simulation results demonstrate that the proposed scheme has the superior secure transmission performance to benchmark schemes.

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.

Accurate product quality prediction can help the enterprise improve the overall emergency response capability of quality issues and reduce quality-related cost losses. In the manufacturing process of multi-stage products, the quality data exhibits some special characteristics such as high dimensionality, nonlinearity, and temporality. Traditional mechanism models and simple statistical analysis prediction models have limitations in these scenarios. This paper constructs a model including the identification of key quality features and the prediction of target quality indicators to address quality prediction for multi-stage products. First, feature selection is conducted in two stages through filtering and wrapping methods to reduce dimensionality and obtain key quality features. Then, the model combining convolutional neural network and bidirectional gated recurrent unit with attention mechanism (CNN-BiGRU-Attention), whose hyperparameters are optimized by the GOOSE algorithm, is used for prediction of product quality indicator. Finally, the model's performance is validated using real-world data collected from the manufacturing process of thin film transistor liquid crystal display (TFT-LCD) products. This study can provide some references for the product quality prediction using highdimensional industrial data.

Sparse code multiple access (SCMA) and mobile edge computing (MEC) are two promising technologies for future Internet of Things (IoT) networks. SCMA enables large-scale connections, while MEC brings computing resources closer to user devices, resulting in faster response time and improved user experiences through task offloading. In this article, we investigate a large-scale grant-free (GF) SCMA enhanced MEC network. First, we propose the offloading protocol for the GF-SCMA enhanced MEC framework and describe the task offloading process using GF-SCMA in detail. Then, we model and analyze the performance of this network, deriving closed-form solutions for the offloading probability and SCMA ergodic rate using stochastic geometry. Additionally, we apply queueing theory to examine the impact of GF-SCMA on task latency and energy consumption in the MEC network. The accuracy of the theoretical expressions is confirmed by simulation results, demonstrating that SCMA outperforms orthogonal multiple access (OMA) in terms of increasing offloading probability and ergodic rate, as well as reducing task delay and energy consumption. Furthermore, this advantage becomes more pronounced with higher user density and task generation rate. Through parameter comparison, it is seen that increasing the pilot and codebook number of GF-SCMA can improve the performance of the proposed scheme in practical implementations.

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.

Integrating sparse code multiple access (SCMA) and mobile edge computing (MEC) into the Internet of Things (IoT) networks can enable efficient connectivity and timely computation for resource-limited IoT users. This paper studies the computation rate maximization problem under task deadline constraints in dynamic SCMA-MEC networks. Specifically, we propose a predictive deep Q-network for SCMA resource allocation and computation offloading (PQ-RACO) algorithm for single-cell scenarios, where IoT devices use long short-term memory (LSTM) networks to predict the states and actions of other agents. However, the PQ-RACO algorithm is not scalable for increasing numbers of IoT devices. To address this issue, an improved multi-agent deep Q-network for SCMA resource allocation and computation offloading algorithm (MQ-RACO) is proposed for multi-cell scenarios. The algorithm is a centralized training and decentralized execution (CTDE) multi-agent reinforcement learning (MARL) algorithm with explicit rewards, which is tailored to the special structure of joint rewards. Simulation results demonstrate that the proposed algorithm outperforms several state-of-the-art MARL algorithms and other benchmark schemes in terms of convergence speed and computation rate.

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.

Large language models (LLMs) have proven to be very superior to conventional methods in various tasks.However, their expensive computations and high memory requirements are prohibitive for deployment.Model quantization is an effective method for reducing this overhead.The problem is that in most previous works, the quantized model was calibrated using a few samples from the training data, which might affect the generalization of the quantized LLMs to unknown cases and tasks.Hence in this work, we explore an important question: Can we design a data-free quantization method for LLMs to guarantee its generalization performance?In this work, we propose EasyQuant, a trainingfree and data-free weight-only quantization algorithm for LLMs.Our observation indicates that two factors: outliers in the weight and quantization ranges, are essential for reducing the quantization error.Therefore, in EasyQuant, we leave the outliers (less than 1%) unchanged and optimize the quantization range to reduce the reconstruction error.With these methods, we surprisingly find that EasyQuant achieves comparable performance to the original model.Since EasyQuant does not depend on any training data, the generalization performance of quantized LLMs are safely guaranteed.Moreover, EasyQuant can be implemented in parallel so that the quantized model could be attained in a few minutes even for LLMs over 100B.To our best knowledge, we are the first work that achieves comparable performance with datadependent algorithms under a data-free setting and our algorithm runs over 10 times faster than the data-dependent methods.

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.

This paper investigates a joint transmissive and reflective reconfigurable intelligent surfaces (RIS) -aided secure multiple-input multiple-output (MIMO) system, where both a RIS-assisted transmitter and a RIS-based reflector are deployed to defend against the simultaneous jamming attack and wiretapping threat. Our design focuses on maximizing the sum rate under the unknown jammer’s beamforming, joint RISs’ coupled phase shift errors (PSEs), and spatially-correlated angular channel uncertainties. Besides, we take into account the various quality-of-service (QoS) requirement constraints for guaranteeing the secure performance. Since the problem is non-convex and mathematically intractable, a new optimization framework is established to facilitate the solution development to the formulated problem. Specifically, armed with the Akaike information criterion, a novel diagonalization method is first proposed to estimate the unknown jamming covariance matrix. Then, a series of fractional-eliminated rate expressions is derived that facilitates the application of the proposed Double Deterministic Transformation (DDT) to tackle the coupled stochastic PSEs. Besides, regardless of the spatial correlation matrix, a general discretization method is proposed to convert the e spatially-correlatd angular uncertainties into a worst-case robust one. Subsequently, building upon the above transformations which transform the original problem into tractable one, a two-layer iterative Lagrange multiplier algorithm capitalizing a low-complexity dual method is proposed to obtain the globally optimal solution of the digital precoder, where the multiple QoS constraints are handled without iteration. Meanwhile, we develop a novel polyblock-based multiple penalty method to obtain the globally optimal solutions to RISs’ phase shifts which can simultaneously satisfy the multiple QoS constraints. Moreover, to address the narrow feasibility region induced by the multiple QoS constraints, a heuristic initial optimization method is proposed, which strengthens the existing result. Finally, theoretical analysis and numerical results demonstrate the optimality and the excellent performance of our proposed optimization framework.

Sparse code multiple access (SCMA) is a kind of code-domain nonorthogonal multiple access (NOMA) scheme, which can support the increasing requirements for high spectral efficiency and massive connections. Meanwhile, multiaccess edge computing (MEC) is a promising technology for providing resource-constrained users with computing resources. In this article, we propose a novel optimization scheme in the SCMA-based MEC network from the perspective of energy and latency for the Internet of Things (IoT) devices. Specifically, a system utility is first used to calculate the weighted energy consumption and task execution latency. The initial utility minimization problem is nonconvex and then can be subdivided into two tractable subproblems by fixing task offloading decisions, namely, optimal local computing via CPU frequency scheduling and optimal edge computing via the SCMA codebook assignment, subcarrier power allocation, and MEC server computing resources distribution. Primarily, a joint SCMA codebook assignment based on the bidirectional matching principle and optimal power allocation algorithm is proposed. Moreover, we come up with CPU frequency scheduling strategies utilizing convex optimization to optimize the computing resources allocation (CRA) of local devices and the MEC server. Finally, a low-complexity task offloading policy based on simulated annealing is presented. Numerical results show that our proposed joint optimization algorithm for resource allocation and task offloading can achieve a good compromise between time delay and energy consumption for IoT devices. It is demonstrated that the proposed strategy has a remarkable advantage compared to the previous SCMA-MEC schemes.

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.

In this paper, we study the issue of automatic singer identification (SID) in popular music recordings, which aims to recognize who sang a given piece of song. The main challenge for this investigation lies in the fact that a singer’s singing voice changes and intertwines with the signal of background accompaniment in time domain. To handle this challenge, we propose the KNN-Net for SID, which is a deep neural network model with the goal of learning local timbre feature representation from the mixture of singer voice and background music. Unlike other deep neural networks using the softmax layer as the output layer, we instead utilize the KNN as a more interpretable layer to output target singer labels. Moreover, attention mechanism is first introduced to highlight crucial timbre features for SID. Experiments on the existing artist20 dataset show that the proposed approach outperforms the state-of-the-art method by 4%. We also create singer32 and singer60 datasets consisting of Chinese pop music to evaluate the reliability of the proposed method. The more extensive experiments additionally indicate that our proposed model achieves a significant performance improvement compared to the state-of-the-art methods.