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.

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.

Recently, the noise modulation scheme and active anti-jamming (AAJ) scheme have shown the potential advantages, such as high anti-jamming performance, low transmitter complexity and low power consumption. Inspired by the prior schemes, this paper proposes a novel hybrid noise and jamming modulation (NJM) scheme that enables reliable communication under diverse jamming scenarios. Specifically, this scheme encodes information bits through a noise modulated transmitter and the amplification factor of a programmable gain amplifier (PGA), achieving robust anti-jamming performance. Theoretical bit error rate (BER) is conducted to derive the optimal decoding thresholds for different jamming strategies, and the approximate optimal threshold proposed for practical implementation using training symbols. Furthermore, we formulate and solve the power allocation optimization problem to enhance BER performance by properly splitting power between the two modulation components. To comprehensively evaluate the scheme, we provide the capacity analysis for the hybrid NJM scheme under three jamming strategies. Simulation results show that the theoretical results match well with the simulated ones, which demonstrates the correctness of our BER performance analysis. Moreover, the hybrid NJM scheme can achieve the superior BER performance and the channel capacity under the jamming attacks compared with the benchmark schemes.

Unmanned Aerial Vehicles (UAVs) have achieved considerable popularity due to their maneuverability and versatility. However, UAV swarms often face challenges from external malicious jamming and internal co-channel interference when performing reconnaissance missions. Aiming to counteract malicious jamming and co-channel interference, effectively enhancing the anti-jamming transmission capability of UAV networks, this paper proposes UAV deployment schemes based on congestion game model, which adjust the transmitting power of UAV while optimizing the deployment position. By defining the utility function of the UAVs and analyzing the conditions for achieving Nash equilibrium, the optimal deployment strategy and power strategy are realized. Experimental results show that this method effectively improves the data collection efficiency and power efficiency of UAV swarms.

Recently, the noise modulation scheme and active anti-jamming (AAJ) scheme have shown the potential advantages, such as high anti-jamming performance, low transmitter complexity and low power consumption. This paper proposes a novel hybrid noise and jamming modulation (NJM) scheme, which aims to effectively resist the jamming attack in wireless communications. Specifically, the information bits are conveyed by the noise modulated transmitter and the amplification factor of the programmable-gain amplifier (PGA). Then, the optimal threshold is derived for decoding the messages in the receiver node. We formulate the power allocation problem of the hybrid NJM scheme as an optimization problem, which aims to obtain the optimal bit error rate (BER) performance by accurate transmit power splitting for two modulation components. Simulation results show that the theoretical results of BER fit well with the simulated ones, which verifies the effectiveness of the derivation. Moreover, the hybrid NJM scheme outperforms in BER performance against the jamming attack compared with other anti-jamming schemes.

High mobility feature in communication scenario encourages the emergence of the orthogonal time frequency space (OTFS) modulation. The Cyclic prefix (CP) in OTFS systems is used to combat Inter-Symbol Interference (ISI) and Intercarrier Interference (ICI), which simplifies the design of the equalizer by restoring circulant convolution relationship between symbols and the channel. However, the overhead of CP weighs the burden of spectral efficiency and latency. In this paper, an improved CP-free OTFS framework is designed combined with a dual-component Weighted Fractional Fourier transform (DCWFRFT) precoder and an enhanced detector. DCWFRFT precoder enables more evenly distributed signal energy to achieve better performance. To ensure the reliability of CP-free OTFS, the enhanced detector firstly truncates the ISI contaminated portion and then reconstructs these symbols from the reliable one. Finally, the reliable portion and reconstructed portion are combined to be detected and decoded. Furthermore, the normalized mean square error (NMSE) and bit error rate (BER) performance of the proposed detector and CP-free OTFS system are simulated. Results show that the proposed OTFS framework improves spectral efficiency by discarding CP while maintaining satisfactory BER performance compared with CP-OTFS.

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.

Massive devices have become a bottleneck restricting the improvement of computing networks. In this paper, we investigate massive multiple access (MA) schemes in low earth orbit satellite-unmanned aerial vehicle (UAV) integrated multi-tier computing networks to achieve higher connectivity. A game-theoretic MA approach is proposed for sum data minimization at UAVs. This approach is divided into two parts: data node (DN)-UAV assignment and UAV-satellite matching. In the first part, the DNs’ orthogonal MA process is formulated as a coalition formation game (CFG) with the derived optimal bandwidth allocation. The CFG with the proposed Max-Satisfaction order is proofed as an exact potential game, which can maximize the satisfaction between the collected data and UAVs’ real computing capacities. Besides, the overlapping CFG (OCFG) is utilized for higher satisfaction. In UAV-satellite matching, non-orthogonal MA-based sum rate maximization with imperfect successive interference cancellation is obtained by transmit power optimization firstly. The UAV-satellite matching process is formulated as a many-to-one matching game (MTOMG). A swap-based MTOMG algorithm is proposed for UAVs grouping. Finally, the simulation results show the proposed schemes have better performance than other schemes, and the proposed approach remains the lowest data at UAVs compared to other approaches.

Satellite-terrestrial integrated networks (STINs) are troubled with the serious jamming threats in the counterwork environment. Non-orthogonal multiple access (NOMA) approach can not only improve the resource utilization by resource sharing, but also has the potential advantages to be used for anti-jamming. In this paper, under the threat of smart jammer with adaptive jamming policies, we investigate the NOMA-based anti-jamming problem in multi-cell STINs by jointly considering the NOMA-based user grouping in each cell and the beam allocation among multiple cells. Specifically, for each cell, the users can enhance anti-jamming performance and improve the sum rate by NOMA-based users grouping, which is formulated as the anti-jamming Stackelberg game and grouping game to obtain the equilibrium solutions. Then, an adaptive beam allocation algorithm with a low complexity is proposed to avoid allocation conflicts and achieve fairness among multiple cells. Finally, simulation results prove the performance of the proposed scheme.

Satellite-enabled Internet of Things (IoT) (SatIoT) has drawn increasing attentions due to the ubiquitous coverage, high capacity and massive connectivity. The inherent openness and broadcast nature of the SatIoT are vulnerable to security threats, particularly the jamming attacks for interrupting transmissions. Nonorthogonal multiple access (NOMA) scheme has the potential to be applied in anti-jamming communication for SatIoT due to the characteristic of resource sharing. The severely jammed users can get more allocated power by forming NOMA groups with other users, and both parties can improve the spectrum efficiency by frequency sharing. In this article, we aim to improve the performance of sum rate for SatIoT under the jamming environments. An anti-jamming transmission scheme is developed by jointly considering the NOMA-based user grouping and the power allocation (PA) for each NOMA group. Specifically, the users can enhance anti-jamming performance and improve the sum rate by NOMA-based users grouping, which is formulated as an anti-jamming coalition formation game, and the equilibrium solution is proved by the exact potential game theory. Moreover, in order to further improve NOMA performance, we derive the PA solution for multiuser NOMA by considering the imperfect successive interference cancellation. Finally, simulation results briefly highlight some details of the proposed approaches.

As an emerging technology, mobile edge computing (MEC) network paradigm provides great computing potential for edge services, which has been widely applied in friendly city environment. However, there are still many challenges to deploy MEC technology in harsh tactical communication environment due to poor communication conditions, limited computational resources, and hostile malicious interference. Thus, this article investigates the computational resource pricing and task offloading strategy in tactical MEC ad-hoc network, which consists of multiple tactical edge nodes, ground MEC servers, unmanned aerial vehicle-MEC (UAV-MEC) servers and a low-Earth orbit-MEC (LEO-MEC) satellite server. Each edge node can offload its partial computation-intensive task to the MEC servers to reduce computational delay and energy consumption. First, a multileader and multifollower Stackelberg game (MLMF-SG) which includes leader subgame for MEC servers and follower subgame for edge nodes, is proposed to formulate the interaction between servers and edge nodes. It has been proved that there exists a Stackelberg equilibrium (SE) in the proposed MLMF-SG. In order to decrease the delay, energy consumption, and resource overhead, the follower subgame is further formulated as a multimode computation task offloading game. With the help of the exact potential game (EPG), we prove that the follower subgame can converge to the Nash equilibrium (NE). To achieve the SE, a hierarchical distributed iterative algorithm is designed to maximize the utilities of the leaders and followers. Finally, the simulation results demonstrate that the proposed scheme can achieve better performance compared with the existing schemes.

We investigate a computing task scheduling problem in space–air–ground integrated network (SAGIN) for Internet of Remote Things (IoRT). In the considered scenario, the unmanned aerial vehicles (UAVs) collect computing tasks from IoRT devices and make offloading decisions, in which the tasks can be computed at the UAVs or offloaded to the low Earth orbital (LEO) satellite. The optimization objective is to design offloading strategies for each UAV that maximum the number of tasks satisfies delay constraint and reduces the energy consumption of UAVs. We formulate this problem as a nonlinear integer optimization problem, and remodel it as a stochastic game to solve it. To copy with the dynamic and complexity environment, we propose a learning-based orbital edge offloading (LOEF) approach which can coordinate all UAVs to learn the optimal offloading strategies. This method is based on the actor–critic framework of multiagent reinforcement learning, the Actor observes the environment and output the current offloading decision, the Critic evaluates the action of the Actor and coordinate the actions of all UAV to increase the reward of system. The simulation results show that the proposed offloading strategy converges fast, increases the number of satisfying the delay constraints tasks and the energy utilization of UAVs compared with the baseline strategies.

In this article, we investigate max completion time optimization for Internet of Things (IoT) in LEO satellite-terrestrial integrated networks (STINs), in which IoT devices use non-orthogonal multiple access (NOMA) scheme to transmit data to central earth stations (CESs), and orthogonal multiple access (OMA) scheme is used for data transmission from CESs to LEO satellite. We decouple this problem into two subproblems: 1) max completion time optimization in terrestrial networks and 2) max completion time optimization among satellite beams. Different from the existing works about NOMA data transmission in terrestrial networks, we propose a cooperative NOMA scheme, and derive the closed expressions of the optimal cooperative data and the optimal transmit power of IoT devices. Based on the closed-form expressions, a joint subcarrier assignment and cooperative NOMA pairing (JSACNP) approach is proposed to minimize the max completion time in terrestrial networks by utilizing matching theory. Then, to minimize the max completion time among satellite beams, the optimal linear receiver expression is derived with fixed transmit power. Convex optimization is utilized to solve transmit power optimization, we propose an algorithm to solve it by CVX tool. An iterative algorithm is proposed for improved performance. Finally, numerical results are provided to evaluate our proposed algorithms, compared with some other proposed approaches or algorithms.

The internet of satellites (IoS), containing multiple low earth orbit (LEO) satellite constellations, can support tremendous traffic, massive connectivity, and vast coverage. But it also puts forward higher demands for resource management due to the high dynamics of the IoS networks, especially in the malicious jamming environment, where the jammers launch jamming attacks to reduce the efficiency and reliability. Thus, this paper investigates the problem of resource management in malicious jamming environment for IoS, which is divided into three sub-problems: traffic prediction problem, anti-jamming decision problem and resource matching problem. To solve these problems, we proposed a distributed resource management framework (DRMF), which consists of three sub-algorithms. Firstly, the traffic prediction algorithm (TPA) is proposed to deeply mine and accurately predict the traffic rule. Meanwhile, the dynamic anti-jamming algorithm (DAA) is developed to make anti-jamming decision autonomously. Then, based on the outputs obtained by TPA and DAA, the distributed resource matching algorithm (DRMA) is proposed for IoS, and the satellites with insufficient resource can apply for assistance from neighboring satellites with excess resource, thereby improving the safety and efficiency of the entire IoS network. Finally, experiment results and algorithm analysis verify the proposed scheme has better performance than the existing algorithms.

The anti-jamming routing for the Internet of Satellites (IoS) has drawn increasing attentions due to the unknown interrupts, unexpected congestion and smart jamming. This paper investigates anti-jamming routing scheme for heterogeneous IoS, with the aim of minimizing anti-jamming routing cost. Firstly, to tackle the smart jamming which can automatically change jamming strategies according to the jamming effect, we formulate the routing anti-jamming problem as a hierarchical anti-jamming Stackelberg game. Secondly, we propose a deep reinforcement learning based routing algorithm (DRLR) to obtain an available routing path subset. Furthermore, based on this set, a fast response anti-jamming algorithm (FRA) is proposed to achieve fast and reliable antijamming routing. Finally, the simulations have shown that the proposed algorithm have lower routing cost and better antijamming performance than existing approaches.

Satellite-enabled army Internet of Things (SaIoT) has drawn increasing attention due to the wide-coverage and large-capacity transmission. However, the smart jamming based on artificial intelligence technologies has seriously degraded SaIoT performance. Thus, this article investigates the distributed dynamic anti-jamming scheme for SaIoT to decrease energy consumption in the jamming environment. First, a hierarchical anti-jamming Stackelberg game (HASG), which consists of the leader subgame for jammers and the follower subgame for SaIoT devices, is proposed to formulate the confrontation interaction between jammers and SaIoT devices. It has been proved that there exists a Stackelberg equilibrium in the proposed HASG. Then, an anti-jamming coalition formation game (CFG) is proposed for the follower subgame to decrease the energy consumption in the jamming environment, and the modified coalition preference order and coalition change principle are put forward to enhance the performance of the proposed anti-jamming CFG. Furthermore, with the help of the exact potential game, we have demonstrated that the proposed anti-jamming CFG could converge to the stable coalition formation and it is able to achieve similar performance to the centralized optimization via a distributed approach. Finally, reinforcement-learning-based algorithms are utilized to obtain the suboptimal anti-jamming policies according to the dynamic and unknown jamming environment, and simulation results validate that the proposed approach achieves better performance than the existing approaches.