Satellite networks are essential for supporting maritime Internet of Remote Things (IoRT) in regions lacking terrestrial coverage. By integrating remote sensing capabilities with the low latency of Low Earth Orbit (LEO) and the wide coverage of Geostationary Earth Orbit (GEO) satellites, this hierarchical framework enables reliable and delay-sensitive maritime communications. To maintain seamless connectivity under highly dynamic maritime conditions, this paper proposes a two-stage dynamic access and handover scheme for the LEO/GEO networks based on Multi-Attribute Decision-Making (MADM). In the first stage, the appropriate LEO or GEO satellite is selected using jointly formulated delay–availability utility functions. In the second stage, the optimal LEO satellite is identified through an Adaptive Entropy (AE)-based Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Additionally, the Non-Orthogonal Multiple Access (NOMA) technique is integrated to improve uplink spectral efficiency. Power allocation in NOMA is optimized using Karush-Kuhn-Tucker (KKT) conditions for small user clusters and an interior-point algorithm for larger clusters. Simulation results show that our NOMA-enabled MADM scheme significantly outperforms the state-of-the-art in robustness and suitability, achieving 61.5% higher throughput, 40% lower latency, and 0.94 connectivity.

Highlights What are the main findings? This paper proposes an iterative optimization scheme for joint channel allocation and power control in solar-powered UAV-assisted vehicle platooning, which reduces total latency by 22.99% compared to state-of-the-art works. An analytical framework is developed to derive the probability that NOMA outperforms OMA in air-to-ground integrated VANETs, using Gaussian probability integrals and considering SIC decoding thresholds and imperfect CSI. What are the implications of the main findings? The proposed latency optimization scheme provides a practical solution for enhancing uplink transmission performance in UAV-supported vehicle platooning, especially in mountainous or remote areas where ground infrastructure is limited. The analytical framework offers provable performance gains and guides parameter settings (e.g., transmitted power, channel allocation, vehicle speed) for deploying NOMA-enabled VANETs, facilitating reliable and low-latency vehicular communications.Highlights What are the main findings? This paper proposes an iterative optimization scheme for joint channel allocation and power control in solar-powered UAV-assisted vehicle platooning, which reduces total latency by 22.99% compared to state-of-the-art works. An analytical framework is developed to derive the probability that NOMA outperforms OMA in air-to-ground integrated VANETs, using Gaussian probability integrals and considering SIC decoding thresholds and imperfect CSI. What are the implications of the main findings? The proposed latency optimization scheme provides a practical solution for enhancing uplink transmission performance in UAV-supported vehicle platooning, especially in mountainous or remote areas where ground infrastructure is limited. The analytical framework offers provable performance gains and guides parameter settings (e.g., transmitted power, channel allocation, vehicle speed) for deploying NOMA-enabled VANETs, facilitating reliable and low-latency vehicular communications.Abstract In vehicular ad hoc networks (VANETs), using vehicle platooning can improve traffic efficiency, reduce driving energy consumption, and ease traffic congestion. However, since land-based stations have limited coverage (about 7% of the Earth's surface), ensuring low-latency communication is challenging. To address this issue, the introduction of solar-powered unmanned aerial vehicles (UAVs) as aerial base stations provides flexible and extensive communication support for vehicle platooning. Additionally, intelligent connected vehicles (ICVs) adopt non-orthogonal multiple access (NOMA) techniques for uplink transmission to further enhance transmission performance. Motivated by the above, this paper investigates the latency optimization problem of UAV-supported vehicle platooning by jointly considering multi-dimensional resource allocation and imperfect channel state information (CSI) affected by mobility. To solve this problem, we propose an iterative optimization approach with polynomial complexity, where the transmitted power and channel allocation are tackled in turn. Then, an analytical framework is developed to analyze the probability that NOMA is superior to OMA, guiding parameter settings for UAV-supported vehicle platooning. Finally, the simulation results show that the proposed latency optimization scheme can achieve lower total and average latencies on the uplink compared to state-of-the-art works and the benchmark scheme using OMA. Moreover, this paper elucidates the convergence, performance gap, and computational complexity associated with the proposed iterative optimization approach. Furthermore, the probability of NOMA outperforming OMA is quantified through Monte Carlo experiments, which validates the correctness of the developed analytical framework.

The severe Doppler effects in high-mobility scenarios pose critical challenges for integrated sensing and communications (ISAC), motivating the development of next-evolution waveforms (NEW). Therefore, this paper investigates orthogonal chirp division multiplexing (OCDM) waveform for ISAC, leveraging its inherent robustness derived from the time–frequency duality of chirp modulation. First, a compact Fresnel-transform representation is adopted to establish an analytical signal and channel model for OCDM over multipath Doppler channels. Based on this model, we characterize the communication performance under Doppler-induced interference and develop a range–velocity estimation method via FFT-based processing. Furthermore, closed-form expressions for the achievable rate and the Cramér–Rao bounds (CRBs) are derived for adjustable pilot density and placement. These expressions quantify the OCDM-specific resource trade-off and reveal how pilot arrangement determines the performance boundary. Simulation results validate that the proposed OCDM scheme with optimized pilot arrangements achieves a lower bit error rate and higher sensing accuracy in high-mobility environments.

This paper investigates the application of cooperative relaying systems with non-orthogonal multiple access (NOMA) in low-altitude intelligent networking-enabled medical Internet of Things (IoT) and analyzes their transmission performance. First, to enhance the communication quality of remote base stations, we deploy a relaying unmanned aerial vehicle (UAV). A two-slot NOMA cooperative transmission mechanism is proposed accordingly. Next, for the NOMA-enhanced UAV-relayed smart healthcare system under Rician fading channels, an exact closed-form expression for the achievable rate is derived using the incomplete Gamma function. Then, to improve computational efficiency, a low-complexity approximation method based on Gauss–Chebyshev quadrature is designed, overcoming the high complexity of the exact expression. Finally, the simulation results validate a close match between the proposed approximation and the exact values (average approximation error below 6.17%), and demonstrate superior achievable rate performance compared to three state-of-the-art schemes.

To address the challenges of information leakage, low energy efficiency, and the Doppler effect in mobile Internet of Vehicles (IoV), this paper proposes an enhanced IoV cooperation framework, where privacy information is forwarded by the untrusted relay assisted by uncrewed aerial vehicles (UAVs) and reconfigurable intelligent surface (RIS), which can improve security and energy efficiency. To meet the requirements of green communication, we formulate a secrecy energy efficiency maximization problem by jointly optimizing the transmit power allocation, the relay's amplification factor, the two-hop RIS phase shift matrices, and the UAV trajectory. Given the non-convex nature of this problem, we introduce an iterative algorithm based on the convex-concave procedure and Dinkelbach's method to optimize the transmit power and amplification factor. Additionally, we conceive the majorization-minimization (MM) algorithm to optimize the two-hop RIS phase shift matrices, and a designed firefly algorithm-deep deterministic policy gradient (FA-DDPG) algorithm is proposed to obtain the UAV trajectory. Simulation results demonstrate the effectiveness of the proposed scheme in enhancing secrecy energy efficiency. Specifically, compared to the DDPG-only and FA-based schemes, the proposed scheme achieves an improvement of 33.3% and 64.2%, respectively, in secrecy energy efficiency.

Post-disaster emergency communication networks often suffer from coverage degradation and limited network observability, which makes it difficult to maintain reliable connectivity for evacuees. Existing UAV-assisted communication methods usually rely on network-side visible metrics for deployment decisions. As a result, they may overlook evacuees whose communication demands are hidden in coverage blind zones or observation blind zones along predefined evacuation corridors. To address this problem, this paper proposes a sensing-assisted UAV-BS recovery method for invisible evacuee demand. The method constructs an invisible-demand map by combining sensed evacuee states, ground coverage conditions, network observation states, and evacuation urgency. It further introduces an evacuation-flow demand map to describe continuous communication demand along evacuation corridors. These two maps are combined to guide temporary UAV-BS access recovery. The simulation results show that the proposed method achieves the best overall balance among invisible-demand recovery, evacuation-path coverage, and edge evacuee rate. Compared with the blind-zone-only baseline, it improves DCR (demand coverage ratio) from 0.365 to 0.373, DW-EPC (demand-weighted evacuation-path coverage) from 0.286 to 0.316, and the fifth-percentile evacuee rate from 1.559 to 1.672 bps/Hz. The proposed method also shows more stable performance under sensing-output uncertainty and constrained UAV response radius.

In emergency rescue scenarios, air-to-ground (A2G) integrated mobile ad hoc networks (MANETs) face challenges such as dynamic topology, limited resources, and unstable communication opportunities. To address these issues, we propose a position-probability-resource cooperative routing (PP-RCR) algorithm. First, Kalman filtering is applied to predict the future positions of nodes and accurately identify effective communication opportunities, which can mitigate link disruptions caused by topology dynamics. Then, a multi-dimensional relay selection criterion is designed by considering the transmission reliability, node resources, and message attributes, enabling adaptive relay selection among heterogeneous nodes. Based on this, a dynamic message replication and deletion strategy is introduced according to the urgency of messages, and the transmission path is optimized. Finally, the simulation results show that, in typical rescue scenarios, the proposed PP-RCR algorithm achieves higher delivery ratio and lower average delivery delay compared with state-of-the-art algorithms.

Federated learning (FL)-based vehicular edge computing networks (VECNs) are emerging as a key enabler of intelligent transportation systems, as their privacy-preserving and distributed architecture can safeguard vehicle data while reducing latency and energy consumption. However, conventional roadside units face processing bottlenecks in dense traffic and at the network edge, motivating the adoption of unmanned aerial vehicle (UAV)-assisted VECNs. To address this challenge, this paper proposes a UAV-assisted VECN framework with FL, aiming to improve model accuracy while minimizing latency and energy consumption during computation and transmission. Specifically, a reputation-based client selection mechanism is introduced to enhance the accuracy and reliability of federated aggregation. Furthermore, to address the channel dynamics induced by high vehicle mobility, we design a robust reinforcement learning-based resource allocation scheme. In particular, an asynchronous parallel deep deterministic policy gradient (APDDPG) algorithm is developed to adaptively allocate computation and communication resources in response to real-time channel states and task demands. To ensure consistency with real vehicular communication environments, field experiments were conducted and the obtained measurements were used as simulation parameters to analyze the proposed algorithm. Compared with state-of-the-art algorithms, the developed APDDPG algorithm achieves 20% faster convergence, 9% lower energy consumption, a FL accuracy of 95.8%, and the most robust standard deviation under varying channel conditions.

This letter investigates a dynamic sensing and covert communication network enabled by a reconfigurable intelligent surface (RIS), where a base station continuously senses an illegal autonomous aerial vehicle (AAV) and utilizes the sensing signals to achieve covert transmission for legitimate ground users. To address the time-varying target states induced by AAV motion, this letter employs an extended Kalman filter (EKF) to perform real-time estimation of the AAV’s 3D position. Then, a covert rate maximization problem is formulated with sensing performance, transmit power, and covertness constraints. To tackle this non-convex problem, a dynamic online resource allocation scheme based on a graph neural network (GNN) is proposed. By leveraging heterogeneous graph features and a constraint-aware loss function, the proposed GNN scheme optimizes the communication and sensing beamforming vectors and the RIS phase shifts. Simulation results show the superiority of the proposed scheme in terms of covert rate. Compared with the alternating optimization scheme, the proposed scheme achieves a 22% improvement in covert rate.

This paper targets the air-to-ground (A2G) data backhaul scenario of UAVs and proposes a communication system based on coherent optical zero-padding orthogonal frequency division multiplexing (CO-ZP-OFDM), which unifies atmospheric turbulence scintillation, pointing errors, and Doppler frequency shift into a composite channel model. The system employs the Gamma-Gamma (GG) distribution to describe turbulence-induced intensity fluctuations, a Gaussian beam truncation model to characterize pointing errors, and a dual-pilot method to estimate and compensate the Doppler frequency offset. Furthermore, on a polarization-time-frequency (PTF) three-dimensional orthogonal grid pilot structure, we derive theoretical mean square error (MSE) expressions for the zero-forcing (ZF) and minimum mean square error (MMSE) estimators, and analyze their MSE characteristics under the proposed pilot model. Simulation results show that, under moderate turbulence, the shrinkage factor of the MMSE estimator yields only about 0.4 dB MSE reduction over ZF at SNR=10 dB, whereas the full receiver pipeline that combines coherence-bandwidth pilot averaging with the MMSE and maximum ratio combining (MRC) equalizer reduces the empirical MSE by approximately 15 dB. The bit error rate (BER) performance tests indicate that, under turbulence-free conditions with ideal channel estimation, the system can reduce the BER below 10-4 at an SNR of approximately 12 dB. Under strong turbulence conditions with MMSE channel estimation, the SNR cost required to achieve a BER of 10-3 is approximately 18 dB, which corresponds to a 3 to 5 dB BER gain over the ZF baseline at the same SNR. Further simulation analysis shows that the average pointing loss is highly sensitive to the angular jitter at the 1 km link distance: an angular jitter of 1 mrad incurs about 18 dB of loss, and a sub-mrad pointing stability (i.e., σjit<0.062 mrad) is required to keep the average pointing loss below 1 dB.

This article investigates the key challenges of trajectory planning and resource allocation for uncrewed aerial vehicles (UAVs) providing secure communication services in a low-altitude intelligent network with mixed obstacles. By utilizing physical layer security (PLS) techniques, the goal is to maximize the long-term sum secrecy rate for ground users, while strictly ensuring both UAV flight safety and communication security. To achieve this, we formulate a joint optimization problem that incorporates realistic constraints, including 3-D no-fly zones, ground obstacle areas, and limited onboard energy. Due to the high dimensionality, nonconvexity, and coupling characteristics of the problem, we propose a solution based on the multiagent deep deterministic policy gradient (MADDPG) framework. Specifically, we design local observation and action spaces for the UAVs and adopt a centralized training with decentralized execution (CTDE) mechanism to enable distributed decision-making. In addition, we analyze the computational complexity of the proposed algorithm and demonstrate its scalability. Extensive simulation results confirm the superiority of our proposed scheme. Compared with four state-of-the-art schemes, it significantly improves the sum secrecy rate. Furthermore, we explore the impact of key network parameters on secure communication performance, providing practical insights for real-world deployment of low-altitude intelligent networks.

Spread spectrum communication plays a vital role in safeguarding the Internet of Things systems, due to its inherently low probability of interception and anti-jamming capability. However, conventional spread spectrum systems based on pseudorandom sequences are disadvantageous in limited sequence length and deterministic periodicity, making them vulnerable to brute-force attacks. To address these limitations and enhance the randomness of the spread spectrum sequences, a novel wireless channel randomness integrated spread spectrum sequence generation method is proposed in this work. Taking advantage of the intrinsic randomness, temporal variations, and unpredictability of wireless channel fadings, the proposed approach converts the extracted channel features into ordered sequences, which are then used to control the selection of irreducible generating polynomials for spread spectrum sequence generation. The proposed method improves the randomness and secrecy of the integrated spread spectrum sequence. Theoretical analysis and simulation results demonstrate that the proposed sequences not only achieve higher randomness entropy compared to the traditional $m$ -sequence, but also pass the National Institute of Standards and Technology (NIST) randomness tests. Furthermore, performance evaluations under various signal-to-interference ratio (SIR) conditions show improved autocorrelation properties and largely lower bit error rates (BERs), validating the effectiveness of the proposed method in improving the anti-jamming capability.

Traditional vehicular network communication infrastructure is limited by road obstructions and insufficient deployment density, making it difficult to efficiently ensure communication performance in complex traffic scenarios. To address this issue, this paper proposes a space-air-ground integrated vehicular network architecture, which constructs a three-dimensional network by integrating low earth orbit (LEO) satellites, low-altitude platforms (LAPs), and ground vehicles. Additionally, non-orthogonal multiple access (NOMA) and simultaneous wireless information and power transfer (SWIPT) technologies are combined to enable the efficient cooperative transmission of information and energy. However, the multi-tier and multi-node network architecture increases interference between links, making it difficult to directly quantify the downlink outage probability. To overcome this challenge, this paper first establishes a system model based on NOMA transmission and a hybrid time-switching/power-splitting (TS/PS) protocol, and proposes an antenna selection criterion with the objective of maximizing energy harvesting at LAP nodes. Based on this, a dynamic power factor allocation mechanism is designed, and the downlink transmission performance is analyzed. Furthermore, this paper derives a closed-form expression for the downlink outage probability based on the Rayleigh fading and co-channel interference channel models of the satellite-LAP-vehicle link. By applying the central limit theorem, a Gaussian approximation solution is proposed. Simulation results show that the approximation solution matches well with the closed-form expression, with an average error of 6.2 %. In addition, the proposed scheme outperforms state-of-the-art schemes in terms of the downlink outage probability under various network conditions.

The integration of uncrewed aerial vehicles (UAVs) into vehicular networks (VNets) holds great potential for extending communication coverage and enhancing network capacity. However, the high mobility of UAVs and intelligent connected vehicles (ICVs) leads to fast time-varying channels, while ensuring communication security and energy efficiency in such dynamic environments remains a challenging task. To address these issues, this paper aims to jointly maximize the sum secrecy rate of low-altitude intelligent VNets and minimize the total energy consumption of the UAV. Given the NP-hard nature of the formulated problem, we decompose it into three subproblems: UAV trajectory optimization, base station (BS) transmitted power control, and UAV transmitted power allocation. An alternating optimization (AO) framework is developed to solve them iteratively, leveraging the Newton method, model predictive control, and the shooting method, respectively. We further provide a theoretical analysis of the computational complexity and convergence property of the proposed AO framework. Simulation results demonstrate that our scheme outperforms state-of-the-art schemes in terms of sum secrecy rate and energy efficiency under various network conditions.

The realization of space-air-ground integrated networks (SAGIN) is currently impeded by significant spectrum scarcity and complex cross-layer interference. To tackle these challenges, we propose a novel cognitive hierarchical spectrum sharing framework, wherein the satellite network operates as the primary network and high-altitude platforms (HAPs) and autonomous aerial vehicles (AAVs) collaboratively function as the secondary network. Based on this architecture, we propose a two-layer rate-splitting multiple access scheme that orchestrates the synergy between the wide-area coverage of HAPs and the flexible mobility of AAVs. This scheme robustly manages both intra-layer and inter-layer interference, thereby satisfying the heterogeneous quality-of-service requirements of terrestrial users. To maximize the system sum rate, a joint optimization problem is formulated for transmit beamforming and AAV trajectories, constrained by strict satellite interference temperature limits. To address this non-convex problem, we propose a deep iterative beamforming algorithm that leverages successive convex approximation and deep deterministic policy gradient techniques. Simulation results indicate that the proposed scheme achieves up to 25% higher throughput than conventional zero-forcing and non-orthogonal multiple access schemes for SAGIN.

Reconfigurable intelligent surface (RIS)-enabled integrated sensing and communication (ISAC) is emerging as a key 6G technology for improving spectral efficiency and enabling high-resolution sensing. However,sensing targets within the communication coverage may act as potential eavesdroppers and intercept confidential data. To address this challenge, this paper proposes a sensing-assisted secure beamforming framework to enhance physical-layer security (PLS). First, we design a closed-loop architecture that sequentially performs RIS cascaded CSI estimation, target direction-of-arrival (DoA) estimation, and a Cramer-Rao bound (CRB)-based sensing accuracy evaluation. We then introduce an angular-domain information leakage (ADIL) metric to characterize leakage within the target's angular uncertainty region. Building on this metric, we formulate a weighted-sum utility to jointly optimize the communication rate and sensing CRB under an ADIL-suppression constraint. To solve the resulting non-convex problem, we develop a penalty dual decomposition (PDD)-augmented alternating optimization (AO) algorithm that iteratively updates the BS beamforming, RIS phase shifts, and sensing time allocation. Convergence and complexity analyses further demonstrate that PDD accelerates AO convergence and mitigates zig-zag updates caused by coupled variables. Simulation results verify that the proposed sensing-assisted secure beamforming scheme effectively suppresses ADIL at eavesdropper angles and enhances PLS. Moreover, the PDD-augmented AO achieves up to a 21.3% improvement in communication rate and a 5.2% reduction in CRB compared with conventional schemes.

To address the dual threats of eavesdropping and active detection posed by an illegal autonomous aerial vehicle (AAV), this paper proposes a novel reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) secure cooperation scheme. By leveraging the wide-area coverage of the high-altitude platform (HAP) and the channel reconstruction of RIS, the proposed scheme enables covert downlink command transmission from the HAP to the ground gateway while ensuring secure uplink data transmission for ground users. Specifically, the HAP, equipped with both communication and sensing capabilities, interacts with the ground gateway with the assistance of RIS, while continuously sensing and estimating the positions of an AAV using reflected echoes. For the mobile AAV, a factor graph optimization (FGO) method is proposed to achieve accurate AAV state estimation by exploiting the temporal correlation of continuous observation data. Based on the estimated AAV coordinates, we obtain the channel state and further analyze the detection performance of AAV in coherent and non-coherent detection scenarios, deriving closed-form solutions for the detection error probability (DEP) and the optimal detection threshold. Building on the above analysis, an optimization problem is formulated to maximize the effective covert rate (ECR), subject to constraints of sensing accuracy and covertness. For this high-dimensional and non-convex problem, a channel-aware graph neural network (CA-GNN) algorithm is proposed to jointly optimize sensing and communication beamforming as well as RIS phase shifts. Simulation results demonstrate the superiority of the proposed scheme in improving system security and covert performance. Compared to the genetic algorithm-based scheme and the deep neural network (DNN) scheme, the proposed scheme improves the ECR by 34.5% and 21.5%, respectively.

Ensuring reliable and secure emergency communications in post-disaster scenarios is challenging, particularly when mobile communication infrastructures are damaged. In response to challenges in post-disaster emergency communications (PDEComs), this article proposes a cooperative relaying system (CRS) enhanced with Internet of Things (IoT) technology. The system features an airship equipped with buffers that serves as an aerial relay, designed to improve data transmission performance and communication security. To achieve this, it incorporates physical layer security techniques. Additionally, we introduce a hybrid mechanism that combines nonorthogonal multiple access (NOMA) and orthogonal multiple access (OMA), facilitating flexible resource allocation in IoT-enhanced CRSs. To fully leverage the advantages of the proposed airship-and-buffer aided CRS, we formulate a weighted secure sum rate (WSSR) maximization problem, jointly considering the power control, mode selection, and information security. Initially, we address the formulated WSSR maximization problem using Lyapunov optimization. Subsequently, the primal problem is divided into four cases, from which optimal power control and mode selection policies can be derived. This process is constrained by the stability of buffer queues and privacy transmission requirements. Finally, the simulation results show that the proposed scheme outperforms state-of-the-art schemes in terms of the WSSR. By adopting the airship and the hybrid NOMA/OMA mechanism, the WSSR can be increased by 29.9% and 96.4%, respectively. Moreover, we explore the impact of network parameters (e.g., the distance between the eavesdropper and airship, and decoding thresholds) on information security.

Autonomous aerial vehicle (AAV)-enabled Internet of Things (IoT) exhibits great application potential with its wide coverage, flexible network topology, and diversified services. However, ensuring communication security and efficient spectrum resource utilization in multiuser access scenarios is challenging, given the open nature of AAV channels and the proliferation of communication devices in IoT. To address the above challenges, this article proposes a novel reconfigurable intelligent surface (RIS)-aided AAV collaborative communication framework, where RIS-equipped AAV flexibly serves multiple users. In this work, a rate splitting multiple access (RSMA)-based secure transmission scheme is proposed, where the split public information serves both as useful signals and noise to disrupt eavesdropping. For the proposed scheme, a sum secrecy rate maximization problem is formulated and solved by optimally deploying the AAV's location, designing the RIS's phase shift, and power allocation. For this nonconvex problem with a couple of variables, we decompose it and form three separate subissues. Specifically, leveraging the successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques, we first exploit an iterative algorithm for optimizing beamforming vectors and phase-shift matrix of RIS, and the optimal position of the AAV is obtained according to the deep deterministic policy gradient (DDPG). Then, we design an alternating optimization (AO) framework for joint solving. Finally, simulation results validate the efficacy of the proposed scheme in enhancing security, e.g., relative to the nonorthogonal multiple access (NOMA) scheme and benchmark scheme, the secrecy rate of the proposed scheme increased by 29.7% and 71.9%, respectively.

This paper presents a mixed traffic control policy designed to optimize traffic efficiency across diverse road topologies, addressing issues of congestion prevalent in urban environments. A model-free reinforcement learning (RL) approach is developed to manage large-scale traffic flow, using data collected by autonomous vehicles to influence human-driven vehicles. A real-world mixed traffic control benchmark is also released, which includes 444 scenarios from 20 countries, representing a wide geographic distribution and covering a variety of scenarios and road topologies. This benchmark serves as a foundation for future research, providing a realistic simulation environment for the development of effective policies. Comprehensive experiments demonstrate the effectiveness and adaptability of the proposed method, achieving better performance than existing traffic control methods in both intersection and roundabout scenarios. To the best of our knowledge, this is the first project to introduce a real-world complex scenarios mixed traffic control benchmark. Videos and code of our work are available at https://sites.google.com/berkeley.edu/mixedtrafficplus/home