Satellite communications have been considered a key part of global connectivity, effectively supporting diverse applications such as the Internet of Things (IoT) and real-time communication services. However, security-sensitive devices face significant challenges due to the threat of eavesdropping satellites, which compromise data confidentiality. Existing approaches often rely on deterministic models and fail to account for the stochastic nature of eavesdropping threats and the dynamic demands of satellite networks, limiting their applicability in practical scenarios. To address these challenges, this work proposes a novel secure access strategy for satellite mobile edge computing (SatMEC) systems, integrating a stochastic risk assessment model and an evolutionary game-theoretic framework. The proposed solution leverages a probabilistic model to evaluate the spatial distribution of eavesdropping satellites, quantifies the eavesdropping risk via the concept of eavesdropping capacity, and incorporates a dynamic service selection strategy that balances secrecy capacity and queuing delay.Furthermore, a distributed algorithm is developed to enable IoT devices to select service satellites based on real-time utility optimization adaptively. Extensive simulation experiments validate the effectiveness of the proposed strategy, demonstrating its ability to improve system security, balance the network load, and enhance overall performance in large-scale and dynamic satellite network environments. The results highlight the reliability and scalability of the proposed solution, making it a practical approach for secure and efficient access in LEO satellite networks.

With the rapid development of the Internet of Everything (IoE) and its applications, modern networks face unprecedented challenges in scalability, mobility, availability, and security that cannot be effectively addressed by traditional architectures. Cybertwin networks have emerged as a promising next-generation paradigm, enabling a shift from conventional end-to-end connections to cloud-to-end models. As digital representations of human users and devices in cyberspace, Суbertwins facilitate communication, log network behavior, and manage digital assets, providing a flexible, scalable, and secure foundation for future network evolution. In this paper, we present a Programmable and Flexible Platform for Cybertwin Networks (PFPCTN), an OpenAirInterface (OAI)-based, fully software-defined 5G testbed deployed at our laboratory. Designed to explore Cybertwin-enabled wireless network architectures, PFPCTN integrates open-source, programmable components spanning the entire network stack, from the Physical (PHY) layer to the Core Network (CN). The system leverages Cybertwin technology to optimize real-time resource management, enhance security, and improve network intelligence by providing flexible interfaces for AI/ML models and other optimization algorithms in service provisioning. By incorporating multi-vendor softwaredefined radio (SDR) hardware, including four USRP X410 devices and a dedicated 5G CN, the platform demonstrates scalable, high-performance private 5G capabilities. Experimental results indicate that PFPCTN supports stable connectivity for up to four Cybertwin-assisted devices (CTs) and achieves over 100 Mbps downlink throughput on a 40 MHz carrier, confirming its potential for next-generation network deployments.

In this paper, we propose an intelligent congestion control approach for the QUIC protocol based on digital twin technology, aimed at improving adaptability and transmission efficiency in dynamic network environments. By incorporating actor-critic learning, the proposed approach adaptively adjusts congestion control parameters to meet the demands of different application scenarios, thereby optimizing QUIC’s performance under varying network conditions. Leveraging the capabilities of digital twins, the approach builds a virtual model that accurately replicates the physical network. This digital environment supports rapid data collection, real-time congestion monitoring, and comprehensive analysis of historical transmission patterns and state variations. These features enable proactive parameter tuning and timely strategy updates to maintain optimal control performance. Simulation results validate the effectiveness of the proposed approach, showing improvements in data throughput, reduced latency, faster convergence of the learning algorithm, and enhanced end-to-end reliability, particularly in 5G network settings.

Sparse vector coding (SVC) is a promising short-packet transmission method for ultra reliable low latency communication (URLLC) in next generation mobile communication systems. However, the storage burden of codebook and high decoding complexity limit its application in Internet of Things (loT) devices with constrained storage space and computational capabilities. To tackle this challenge, a semi-tensor SVC (ST-SVC)-based short-packet transmission scheme is proposed in this paper. The core idea behind ST-SVC is that it utilizes the semi-tensor product (STP) model in random spreading process, replacing the matrix multiplication model used in traditional SVC schemes. At the transmitter, a low-dimensional codebook is utilized to perform random spreading on a high-dimensional sparse vector carrying information bits. At the receiver, by exploiting the Kronecker structure induced by the STP model, a low-complexity parallel support identification algorithm is proposed for ST-SVC decoding. The proposed scheme breaks through the dimension matching condition required between the codebook matrix and high-dimensional sparse vector in traditional SVC schemes, allowing the loT devices to store an ultra-low-dimensional codebook, which significantly reduces storage overhead. Simulation results demonstrate that the proposed ST-SVC scheme can achieve a substantial reduction in both storage overhead and decoding latency compared to state-of-the-art SVC schemes, with only a slight performance loss in block error rate.

Deep learning plays increasingly important role in future wireless network management and optimization. Existing training methods such as label-based supervised learning and label-free learning have inherent limitations. The performance of supervised learning is limited by labels, while label-free training methods require extensive exploration. To address these limitations, this paper proposes a novel mixture of gradients (MoG) method, which integrates gradients from different sources within the training process in order to improve the convergence performance of neural networks (NNs). Particularly, MoG is a modular, plug-and-play solution requiring no structural modifications to existing NNs. Its implementation necessitates only minor modifications to the loss function, where the label-based supervised loss is combined with a label-free loss through weighted summation. The label-free loss can be either unsupervised loss or reinforcement learning loss. This flexibility allows seamless integration into nearly all NN-based methods, making it applicable to a wide range of wireless optimization problems with minimal implementation cost. Extensive simulations across multiple classic wireless scenarios demonstrate that MoG can significantly enhance the performance of NN decision-making, leading to higher transmission rates.

Sparse vector coding (SVC) is a promising short-packet transmission method for ultra reliable low latency communication (URLLC) in next-generation communication networks. In this paper, a block SVC (BSVC) based short-packet transmission scheme is proposed to further enhance the transmission performance of SVC. The core idea behind the proposed scheme is to transmit short-packet data information after block sparse transformation. At the transmitter, the transmitted information bits are divided into two parts: one part is mapped into the non-zero indexes of block sparse vectors, and the other part is mapped into non-zero values through quadrature amplitude modulation. After pseudo-random spreading, the block sparse vectors are mapped to time-frequency resources for transmission. At the receiver, the decoding problem is transformed into a block sparse signal recovery problem. A cyclic block matching pursuit algorithm is proposed for accurate decoding by leveraging block-structured sparse prior information. Simulation results verify that the proposed BSVC scheme outperforms the existing SVC schemes in terms of packet error rate and spectral efficiency.

The integration of directed acyclic graph (DAG) blockchain and mobile edge computing (MEC) has emerged as a promising means to enable computation-intensive, delay-sensitive, and secure task execution in Internet of Things (IoT) applications. However, off-chain task execution results are not credible even if the results have been recorded on the chain, since blockchain cannot extend the trust of on-chain data to off-chain. To make the off-chain and on-chain trust consistent, we first develop a trusted MEC (T-MEC) framework by employing a DAG blockchain-aided decentralized trust management (DAG-DTM) mechanism. Specifically, DAG-DTM evaluates the off-chain trust of edge nodes according to the quality of task execution results, and the trust can be further verified off the chain by any edge node under the same trust management rule. Moreover, the approval time for recording the execution result of the edge node on the chain is positively correlated with the verified off-chain trust, which can further promote on-chain transaction security of trusted edge node. Second, we jointly optimize the bandwidth and computation resource allocation to minimize the system latency that consists of off-chain task execution delay and on-chain transaction confirmation delay. Numerical results compare system latency and security performance between the optimized T-MEC and the benchmark schemes. In particular, the optimized T-MEC can achieve a 33.12% gain of computation delay and a 10.19% gain of system latency at an affordable cost of transaction confirmation delay (i.e., 3.21%) over T-MEC, while meeting the requirements of off-chain task execution latency and on-chain transaction security simultaneously.

Mobile edge computing is a promising technique to provide timely edge service for the Internet of Things. With a continuously expanding network scale, the number and types of devices in the IoT network are growing rapidly. Moreover, the total number of devices that need edge service actually in the network is uncertain because the devices are online or offline frequently. It is very critical to design an efficient service selection strategy for every device to access edge service providers (ESPs) in such a huge number of devices situation. To address the problem, this article first formulates the access of IoT devices as a stochastic Stackelberg game model. Then, for the edge service selection of devices, a Poisson game is utilized to model the coordinated selection of edge services, we propose a distributed iterative algorithm to solve this game and give the optimal edge service selection strategy for each device group. Next, for the noncooperative competition among ESPs with incomplete information, the service prices of other ESPs are first estimated through a Bayesian neural network (BNN). Then we model this stochastic noncooperative game as a partially observable Markov decision process (POMDP) model, and finally obtain the optimal long-term pricing strategy through Bayesian deep$Q$-learning network (BDQN) algorithm. The experimental simulation results show that the proposed strategy can significantly improve the utilities of both ESPs and devices and effectively decrease the average queuing probability of all devices.

Cybertwin is a promising technique in the 6G network to cope with an exponentially increasing demand for the mobile data traffic and growing number of required network service appliances. Multiple resources are required to fulfil various personalized services of mobile users while protecting their privacy. In this article, we propose a progressive adaptive user selection environment (PAUSE)-based combinatorial auction resource trading mechanism to allocate the resource efficiently and securely. Cybertwins can request the various resources as a bid according to its corresponding user and proceed the auction in a fully distributed manner. Not only the user’s privacy can be well protected but also its cost can be saved due to the transparency of this mechanism. Simulation results validate the effectiveness of our mechanism in comparison with the centralized benchmark.

Ultradense network (UDN) is considered as one of the key technologies for the explosive growth of mobile traffic demand on the Internet of Things (IoT). It enhances network capacity by deploying small base stations in large quantities, but it simultaneously causes great energy consumption. In this article, we use energy harvesting (EH) and energy cooperation technologies to maximize system throughput and save energy. Considering that the energy arrival process and channel information are not available a priori, we propose an optimal deep reinforcement learning (DRL) algorithm to solve this average throughput maximization problem over a finite horizon. We also propose a multiagent DRL method to solve the dimensionality problem caused by the expansion of the state and action dimensions. Finally, we compare these algorithms with two traditional algorithms, greedy algorithm and conservative algorithm. The numerical results show that the proposed algorithms are valid and effective in increasing system average throughput on the long term.

Crowdsensing indoor walking paths based on crowdsourcing traces collected from normal users has recently become an emerging topic for indoor positioning, which can reduce the labor effort of building radio maps and improve the positioning accuracy when a floor plan is unavailable. In this work, we design an indoor walking path crowdsensing system with massive noisy crowdsourcing traces. In this system, we propose a robust iterative trace merging algorithm based on WiFi access points as markers (named 'WiFi-RITA') to merge massive noisy traces. The algorithm formulates the trace merging problem as an optimization problem in which each trace is controlled to translate and rotate to minimize the limitation of distances among traces defined by WiFi access points as markers. WiFi-RITA is robust to the rotation errors and uncertain absolute locations of user traces, and can efficiently work for a large number of user traces. We further adopt a landmark matching algorithm to match the merged traces to the target building and adopt a 2-dimensional histogram approach to remove outlier traces. With such procedures, we generate walking paths of a large-scale building with a mean accuracy of 2.1m.

In this work, we propose an automatic radio map construction system based on crowdsourcing Pedestrian Dead Reckoning (PDR) traces, which does not rely on priori knowledge of floor plans and is robust to inaccurate PDR traces. In this system, we propose to process some opportunistic PDR traces, in which users walk through the building, to generate parts of road paths by translating, rotating and scaling the traces based on the opportunistic GPS locations and gate points as landmarks. Then, we further extend the coverage of road paths by processing the PDR traces entirely obtained indoor by compensating the turning errors and merging the PDR traces based on the similarity of WiFi fingerprints. With such procedures, we can accurately generate indoor road paths of a large-scale building and construct the radio map based on these road paths. Our proposed method achieves a median accuracy of 2.8m and mean accuracy of 2.9m for the constructed road paths. By fusing GPS, PDR, and WiFi fingerprinting with the crowdsourcing radio map, we achieve a median positioning accuracy of 2.9m and mean accuracy of 3.4m without site surveying, which significantly outperforms the positioning algorithm by merely fusing GPS and PDR.

The robust power allocation problem is often solved by different optimization approaches under a convex optimization model. But in this study, we use a distributed projected dynamic system (PDS) to describe this model and realize power allocation by designing a stable controller using Lyapunov function and linear matrix inequality (LMI) for the PDS. The controller can follow our defined target SINR and keeps the quality of service (QoS) required by primary user (PU) under the channel and the interference uncertainties (including feedback error). The simulation results illustrate that our proposed controller can realize power allocation with better performance compared with iterative waterfilling algorithm (IWFA).

In this paper, we study spectrum overlay access to share spectrum between primary users (PUs) and secondary users (SUs). Our goal is to maximize the average throughput by optimal power allocation within finite time duration. To do this, we formulate this optimization problem as a Markov Decision Process with continuous state. An approximate value method with pre-allocation mechanism is proposed, which can effectively protect the PUs by obtaining a continuous closed-form solution rather than a discrete one. Numerical results show that the proposed algorithm exhibits better performance than traditional methods while guaranteeing non-interference to PUs.