The simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is regarded as a promising paradigm for enhancing the connectivity and reliability of non-orthogonal multiple access (NOMA) networks. However, the transmission of STAR-RIS enhanced NOMA networks performance is severely limited due to the inter-user interference (IUI) on multi-user detections. To mitigate this drawback, we propose a generalized quadrature spatial modulation (GQSM) aided STAR-RIS in conjunction with the NOMA scheme, termed STAR-RIS-NOMA-GQSM, to improve the performance of the corresponding NGMA network. By STAR-RIS-NOMA-GQSM, the information bits for all users in transmission and reflection zones are transmitted via orthogonal signal domains to eliminate the IUI so as to greatly improve the system performance. The low-complexity detection and upper-bounded bit error rate (BER) of STAR-RIS-NOMA-GQSM are both studied to evaluate its feasibility and performance. Moreover, by further utilizing index modulation (IM), we propose an enhanced STAR-RIS-NOMA-GQSM scheme, termed E-STAR-RIS-NOMA-GQSM, to enhance the transmission rate by dynamically adjusting reflection patterns in both transmission and reflection zones. Simulation results show that the proposed original and enhanced scheme significantly outperform the conventional STAR-RIS-NOMA and also confirm the precision of the theoretical analysis of the upper-bounded BER.

With the development of wireless communication, the fifth generation mobile communication technology (5G) has emerged as a hot topic in high-speed railway communication system and has moved towards industrial application. Investigating the radio propagation characteristics in 5G high-speed train (HST) scenarios is essential for enhancing wireless coverage and overall system performance. We propose a novel 5G passive sounding scheme to extract channel impulse responses (CIRs) using channel state information reference signals (CSI-RS) from the target 5G base station (BS). Detailed procedures for time-frequency synchronization, CSI-RS detection and extraction are presented through simulations. Through the laboratory work involving absolute power calibration, phase coherence calibration and power delay profile (PDP) validation, we validate the accuracy and performance of the developed platform. Furthermore, a measurement campaign was conducted in HST scenarios encompassing both residential and undeveloped areas. The path loss (PL) model and the channel characteristics including stationarity interval (SI), multi-path components (MPCs), shadow fading (SF), Rician K-factor, root mean square (RMS) delay spread and received correlation coefficients are analyzed and fitted. The estimated channel characteristics and the statistical model presented in this paper will contribute to the research on HST radio propagation and the development of 5G railway communication systems.

With the continuous advancement of communication and unmanned aerial vehicle (UAV) technologies, the collaborative operations of diverse platforms, including UAVs and ground vehicles, have been significantly promoted. However, battlefield uncertainties, such as equipment failures and enemy attacks, can impact these collaborative operations' stability and communication efficiency. To this end, we design a highly destruction-resistant air-ground cooperative resilient networking platform that aims to enhance the robustness of network communications by integrating ground vehicle information for UAV network deployment. It then incorporates the concept of virtual guiding force, enabling the UAV swarm to adaptively configure its network layout based on ground vehicle information, thereby improving network destruction resistance. Simulation results demonstrate that the UAV swarm involved in the proposed platform exhibits balanced flight energy consumption and excellent performance in network destruction resistance.

The sixth-generation (6G) networks will consist of multiple bands such as low-frequency, mid-frequency, millimeter wave, terahertz and other bands to meet various business requirements and networking scenarios. The dynamic complementarity of multiple bands are crucial for enhancing the spectrum efficiency, reducing network energy consumption, and ensuring a consistent user experience. This paper investigates the present researches and challenges associated with deployment of multi-band integrated networks in existing infrastructures. Then, an evolutionary path for integrated networking is proposed with the consideration of maturity of emerging technologies and practical network deployment. The proposed design principles for 6G multi-band integrated networking aim to achieve on-demand networking objectives, while the architecture supports full spectrum access and collaboration between high and low frequencies. In addition, the potential key air interface technologies and intelligent technologies for integrated networking are comprehensively discussed. It will be a crucial basis for the subsequent standards promotion of 6G multi-band integrated networking technology.

An adaptive dispersion estimation (ADE) is proposed to compensate dispersion and estimate the transfer function of the fiber channel with Gerchberg-Saxton (G-S) algorithm, using the stochastic gradient descent (SGD) method in the intensity-modulation and direct-detection (IM-DD) system, improving the tolerance of the algorithm to chromatic dispersion (CD). In order to address the divergence arising from the perturbation in the amplitude of the received signal caused by the filtering effect of the non-ideal channels, a channel-compensation equalizer (CCE) derived from the back-to-back (BTB) scenario is employed at the transmitter to make the amplitude of the received signal depicting the CD effect more accurately. The simulation results demonstrate the essentiality of CCE for the convergence and performance improvement of the G-S algorithm. Results show that it supports 112 Gb/s four-level pulse amplitude modulation (PAM4) over 100 km standard single-mode fiber (SSMF) transmission under the 7% forward error correction (FEC) threshold of 3.8E-3. Besides, ADE improves the tolerance to wavelength drift from about 4 nm to 42 nm, and there is a better tolerance for fiber distance perturbation, indicating the G-S algorithm and its derived algorithms with the ADE scheme exhibit superior robustness to the perturbation in the system.

Low-carbon smart parks achieve self-balanced carbon emission and absorption through the cooperative scheduling of direct current (DC)-based distributed photovoltaic, energy storage units, and loads. Direct current power line communication (DC-PLC) enables real-time data transmission on DC power lines. With traffic adaptation, DC-PLC can be integrated with other complementary media such as 5G to reduce transmission delay and improve reliability. However, traffic adaptation for DC-PLC and 5G integration still faces the challenges such as coupling between traffic admission control and traffic partition, dimensionality curse, and the ignorance of extreme event occurrence. To address these challenges, we propose a deep reinforcement learning (DRL)-based delay sensitive and reliable traffic adaptation algorithm (DSRTA) to minimize the total queuing delay under the constraints of traffic admission control, queuing delay, and extreme events occurrence probability. DSRTA jointly optimizes traffic admission control and traffic partition, and enables learning-based intelligent traffic adaptation. The long-term constraints are incorporated into both state and bound of drift-plus-penalty to achieve delay awareness and enforce reliability guarantee. Simulation results show that DSRTA has lower queuing delay and more reliable quality of service (QoS) guarantee than other state-of-the-art algorithms.

The integrated communication and jamming (ICAJ) system recently has been proposed to enable communication and jamming (C&J) to reinforce each other in one system. By exploiting the diversity gain of multiple input multiple output (MIMO) technology, a specific implementation form of ICAJ system, called communication-aided collaborative jamming system, is designed to transmit C&J signals at the same time and frequency. Different from previous studies which overlook the jamming prior information acquisition process and assume that the prior information is perfect or with bounded error, this paper takes the non-cooperative characteristics of jamming and the consequent difficulty in prior information acquisition into consideration. To analyze the tradeoff between C&J, the integration metric is proposed and then the corresponding system design problem is formulated. However, the non-convexity of problem and the lack of jamming prior information make the optimization tricky. In this case, blind channel estimation (BCE) is introduced to obtain an approximate channel state information (CSI) without interacting with jamming targets and then the neural network embedded with system performance calculation model is developed to establish the correspondence between the estimated CSI and optimal beamforming design. Furthermore, a hybrid data-driven and model-based approach, blind channel estimation-deep learning (BCE-DL), is proposed to accomplish the beamforming design based on unsupervised learning for ICAJ system in non-cooperative scenarios. The simulation results show that the BCE-DL algorithm outperforms the conventional algorithms in the presence of CSI estimation errors and is a flexible approach which takes the best of both data-driven and model-based methods to design the ICAJ system.

Physical layer security methods based on joint relay and jammer selection (JRJS) have been widely investigated in the study of secure wireless communications. Different from current works on JRJS schemes, which assumed that the global channel state information (CSI) of the eavesdroppers (Eves) was known beforehand, then the optimal relaying and jamming relays were determined. More importantly, the time complexity of selecting optimal jamming relay is $O(N^2)$, where $N$ is the maximum number of relays/Eves. In this paper, for the scenario where the source wants to exchange the message with the destination, via relaying scheme due to longer communication distance and limited transmission power, in the presence of multiple Eves, with the assumption of Eves' perfect CSI and average CSI, we propose two kinds of JRJS methods. In particular, the time complexity of finding the optimal jammer can be reduced to $O(N)$. Furthermore, we present a novel JRJS scheme for no CSI of Eves by minimizing the difference between expected signal and interfering signal at the destination. Finally, simulations show that the designed methods are more effective than JRJS and other existing strategies in terms of security performance.

In optical metro-access networks, Access Points (APs) and Data Centers (DCs) are located on the fiber ring. In the cloud-centric solution, a large number of Internet of Things (IoT) data pose an enormous burden on DCs, so the Virtual Machines (VMs) cannot be successfully launched due to the server overload. In addition, transferring the data from the AP to the remote DC may cause an undesirable delivery delay. For this end, we propose a promising solution considering the interplay between the cloud DC and edge APs. More specifically, bringing the partial capability of computing in APs close to things can reduce the pressure of DCs while guaranteeing the expected Quality of Service (QoS). In this work, when the cloud DC resource becomes limited, especially for delay sensitive but not computing-dependent IoT applications, we degrade their VMs and migrate them to edge APs instead of the remote DC. To avoid excessive VM degradation and computing offloading, we derive appropriate VM degradation coefficients based on classic microeconomic theory. Simulation results demonstrate that our algorithms improve the service providers' utility with the ratio from 34% to 89% over traditional cloud-centric solutions.

Multi-band optical networks are a potential technology for increasing network capacity. However, the strong interference and non-uniformity between wavelengths in multi-band optical networks have become a bottleneck restricting the transmission capacity of multi-band optical networks. To overcome these challenges, it is particularly important to implement optical power optimization targeting wavelength differences. Therefore, based on the generalized Gaussian noise model, we first formulate an optimization model for the problems of routing, modulation format, wavelength, and power allocation in C+L+S multi-band optical networks. Our objective function is to maximize the average link capacity of the network while ensuring that the Optical Signal-to-Noise (OSNR) threshold of the service request is not exceeded. Next, we propose a NonLinear Interference-aware (NLI-aware) routing, modulation format, wavelength, and power allocation algorithm. Finally, we conduct simulations under different test conditions. The simulation results indicate that our algorithm can effectively reduce the blocking probability by 23.5 % and improve the average link capacity by 3.78% in C+L+S multi-band optical networks.

With the rapid development of blockchain technology, the Chinese government has proposed that the commercial use of blockchain services in China should support the national encryption standard, also known as the state secret algorithm GuoMi algorithm. The original Hyperledger Fabric only supports internationally common encryption algorithms, so it is particularly necessary to enhance support for the national encryption standard. Traditional identity authentication, access control, and security audit technologies have single-point failures, and data can be easily tampered with, leading to trust issues. To address these problems, this paper proposes an optimized and application research plan for Hyperledger Fabric. We study the optimization model of cryptographic components in Hyperledger Fabric, and based on Fabric's pluggable mechanism, we enhance the Fabric architecture with the national encryption standard. In addition, we research key technologies involved in the secure application protocol based on the blockchain. We propose a blockchain-based identity authentication protocol, detailing the design of an identity authentication scheme based on blockchain certificates and Fabric CA, and use a dual-signature method to further improve its security and reliability. Then, we propose a flexible, dynamically configurable real-time access control and security audit mechanism based on blockchain, further enhancing the security of the system.

Wireless communication systems that incorporate digital twin (DT) alongside artificial intelligence (AI) are expected to transform 6G networks by providing advanced features for predictive modeling and decision making. The key component is the creation of DT channels, which form the basis for upcoming applications. However, the existing work of channel predictive generation only considers time dimension, distribution-oriented or multi-step sliding-window prediction schemes, which is not accurate and efficient for real-time DT communication systems. Therefore, we propose the wireless channel generative adversarial network (WCGAN) to tackle the issue of generating authentic long-batch channels for DT applications. The generator based on convolutional neural networks (CNN) extracts features from both the time and frequency domains to better capture the correlation. The loss function is designed to ensure that the generated channels consistently match the physical channels over an extended period while sharing the same probability distributions. Meanwhile, the accumulating error from the slicing window has been alleviated. The simulation demonstrates that an accurate and efficient DT channel can be generated by employing our proposed WCGAN in various scenarios.

This paper presents a comprehensive framework that enables communication scene recognition through deep learning and multi-sensor fusion. This study aims to address the challenge of current communication scene recognition methods that struggle to adapt in dynamic environments, as they typically rely on post-response mechanisms that fail to detect scene changes before users experience latency. The proposed framework leverages data from multiple smartphone sensors, including acceleration sensors, gyroscopes, magnetic field sensors, and orientation sensors, to identify different communication scenes, such as walking, running, cycling, and various modes of transportation. Extensive experimental comparative analysis with existing methods on the open-source SHL-2018 dataset confirmed the superior performance of our approach in terms of F1 score and processing speed. Additionally, tests using a Microsoft Surface Pro tablet and a self-collected Beijing-2023 dataset have validated the framework's efficiency and generalization capability. The results show that our framework achieved an F1 score of 95.15% on SHL-2018 and 94.6% on Beijing-2023, highlighting its robustness across different datasets and conditions. Furthermore, the levels of computational complexity and power consumption associated with the algorithm are moderate, making it suitable for deployment on mobile devices.

Federated learning (FL) is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple parties. When combined with Fog Computing, FL offers enhanced capabilities for machine learning applications in the Internet of Things (IoT). However, implementing FL across large-scale distributed fog networks presents significant challenges in maintaining privacy, preventing collusion attacks, and ensuring robust data aggregation. To address these challenges, we propose an Efficient Privacy-preserving and Robust Federated Learning (EPRFL) scheme for fog computing scenarios. Specifically, we first propose an efficient secure aggregation strategy based on the improved threshold homomorphic encryption algorithm, which is not only resistant to model inference and collusion attacks, but also robust to fog node dropping. Then, we design a dynamic gradient filtering method based on cosine similarity to further reduce the communication overhead. To minimize training delays, we develop a dynamic task scheduling strategy based on comprehensive score. Theoretical analysis demonstrates that EPRFL offers robust security and low latency. Extensive experimental results indicate that EPRFL outperforms similar strategies in terms of privacy preserving, model performance, and resource efficiency.

With increasing demand for data circulation, ensuring data security and privacy is paramount, specifically protecting privacy while maximizing utility. Blockchain, while decentralized and transparent, faces challenges in privacy protection and data verification, especially for sensitive data. Existing schemes often suffer from inefficiency and high overhead. We propose a privacy protection scheme using BGV homomorphic encryption and Pedersen Secret Sharing. This scheme enables secure computation on encrypted data, with Pedersen sharding and verifying the private key, ensuring data consistency and immutability. The blockchain framework manages key shards, verifies secrets, and aids security auditing. This approach allows for trusted computation without revealing the underlying data. Preliminary results demonstrate the scheme's feasibility in ensuring data privacy and security, making data available but not visible. This study provides an effective solution for data sharing and privacy protection in blockchain applications.

The lifetime of a wireless sensor network (WSN) is crucial for determining the maximum duration for data collection in Internet of Things applications. To extend the WSN's lifetime, we propose deploying an unmanned ground vehicle (UGV) within the energy-hungry WSN. This allows nodes, including sensors and the UGV, to share their energy using wireless power transfer techniques. To optimize the UGV's trajectory, we have developed a tabu search-based method for global optimality, followed by a clustering-based method suitable for real-world applications. When the UGV reaches a stopping point, it functions as a regular sensor with ample battery. Accordingly, we have designed optimal data and energy allocation algorithms for both centralized and distributed deployment. Simulation results demonstrate that the UGV and energy-sharing significantly extend the WSN's lifetime. This effect is especially prominent in sparsely connected WSNs compared to highly connected ones, and energy-sharing has a more pronounced impact on network lifetime extension than UGV mobility.

In wireless networks, the prioritized transmission scheme is essential for accommodating different priority classes of users sharing a common channel. In this paper, we propose a prioritized random access scheme based on compute-and-forward, referred to as expanding window sign-compute diversity slotted ALOHA (EW-SCDSA). We improve the expanding window technique and apply it to a high-throughput random access scheme, i.e., the sign-compute diversity slotted ALOHA (SCDSA) scheme, to implement prioritized random access. We analyze the probability of user resolution in each priority class utilizing a bipartite graph and derive the corresponding lower bounds, the effectiveness of which is validated through simulation experiments. Simulation results demonstrate that the EW-SCDSA scheme can provide heterogeneous reliability performance for various user priority classes and significantly outperforms the existing advanced prioritized random access scheme.

The progress of modern industry has given rise to great requirements for network transmission latency and reliability in domains such as smart grid and intelligent driving. To address these challenges, the concept of Time-sensitive networking (TSN) is proposed by IEEE 802.1TSN working group. In order to achieve low latency, Cyclic queuing and forwarding (CQF) mechanism is introduced to schedule Time-triggered (TT) flows. In this paper, we construct a TSN model based on CQF and formulate the flow scheduling problem as an optimization problem aimed at maximizing the success rate of flow scheduling. The problem is tackled by a novel algorithm that makes full use of the characteristics and the relationship between the flows. Firstly, by K-means algorithm, the flows are initially partitioned into subsets based on their correlations. Subsequently, the flows within each subset are sorted by a new special criteria extracted from multiple features of flow. Finally, a flow offset selecting method based on load balance is used for resource mapping, so as to complete the process of flow scheduling. Experimental results demonstrate that the proposed algorithm exhibits significant advantages in terms of scheduling success rate and time efficiency.

Reconfigurable intelligent surfaces (RISs) with the capability of nearly passive beamforming, have recently sparked considerable interests. This paper presents an energy-efficient discrete phase encoding method for RIS-assisted communication systems. Firstly, the beamforming gain, power consumption and energy efficiency models for the RIS-assisted system are illustrated. On this basis, the discrete phase encoding problem is formulated for the purpose of improving the energy efficiency, under the power constraint and the quality-of-service (QoS) requirement. According to the interrelation between the phase encoding and power consumption, a three-step encoding method is proposed with the capability of customizing the beamforming gain, power consumption, and energy efficiency. Simulation results indicate that the proposed method is capable of achieving a more favorable performance in terms of satisfying the QoS demand, reducing the power consumption, and improving the energy efficiency. Furthermore, two field trials at 35 GHz evidence the superiority performance and feasibility characteristics of the proposed method in real environment. This work may provide a reference for future applications of RIS-assisted system with an energy-efficient manner.

Integrated sensing and communication (ISAC), assisted by reconfigurable intelligent surface (RIS) has emerged as a breakthrough technology to improve the capacity and reliability of 6G wireless network. However, a significant challenge in RIS-ISAC systems is the acquisition of channel state information (CSI), largely due to co-channel interference, which hinders meeting the required reliability standards. To address this issue, a minimax-concave penalty (MCP)-based CSI refinement scheme is proposed. This approach utilizes an element-grouping strategy to jointly estimate the ISAC channel and the RIS phase shift matrix. Unlike previous methods, our scheme exploits the inherent sparsity in RIS-assisted ISAC channels to reduce training overhead, and the near-optimal solution is derived for our studied RIS-ISAC scheme. The effectiveness of the element-grouping strategy is validated through simulation experiments, demonstrating superior channel estimation results when compared to existing benchmarks.

The reconfigurable intelligent surfaces (RIS) can reconfigure the wireless channel environment by manipulating the propagation of incident electromagnetic waves. Specifically, we consider using multi-RIS to improve the system throughput of limited feedback multiple input single output (MISO) system in an energy efficiency manner. The critical challenge lies in the joint design of channel acquisition and beamforming which are usually based on codebook with limited precision. To solve this, we propose a semi-definite relaxation (SDR) based beamforming design scheme while considering the effect of cascaded channel acquisition. First, a channel quantization scheme is proposed by exploiting the channel sparsity in double-RIS aided MISO system. Second, an optimization problem of maximizing the system throughput is established to derive the channel quantization vector which also serves as the beamforming vector, with the consideration of the constraints of transmission power, RISs phase-shift. Third, a SDR based iterative optimization algorithm is proposed to solve the problem with low complexity. Finally, simulation results show that our proposed algorithm can improve the system throughput efficiently.

There are all kinds of unknown and known signals in the actual electromagnetic environment, which hinders the development of practical cognitive radio applications. However, most existing signal recognition models are difficult to discover unknown signals while recognizing known ones. In this paper, a compact manifold mixup feature-based open-set recognition approach (OR-CMMF) is proposed to address the above problem. First, the proposed approach utilizes the center loss to constrain decision boundaries so that it obtains the compact latent signal feature representations and extends the low-confidence feature space. Second, the latent signal feature representations are used to construct synthetic representations as substitutes for unknown categories of signals. Then, these constructed representations can occupy the extended low-confidence space. Finally, the proposed approach applies the distillation loss to adjust the decision boundaries between the known categories signals and the constructed unknown categories substitutes so that it accurately discovers unknown signals. The OR-CMMF approach outperformed other state-of-the-art open-set recognition methods in comprehensive recognition performance and running time, as demonstrated by simulation experiments on two public datasets RML2016.10a and ORACLE.

The unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has been deemed a promising solution for energy-constrained devices to run smart applications with computation-intensive and latency-sensitive requirements, especially in some infrastructure-limited areas or some emergency scenarios. However, the multi-UAV-assisted MEC network remains largely unexplored. In this paper, the dynamic trajectory optimization and computation offloading are studied in a multi-UAV-assisted MEC system where multiple UAVs fly over a target area with different trajectories to serve ground users. By considering the dynamic channel condition and random task arrival and jointly optimizing UAVs' trajectories, user association, and subchannel assignment, the average long-term sum of the user energy consumption minimization problem is formulated. To address the problem involving both discrete and continuous variables, a hybrid decision deep reinforcement learning (DRL)-based intelligent energy-efficient resource allocation and trajectory optimization algorithm is proposed, named HDRT algorithm, where deep Q network (DQN) and deep deterministic policy gradient (DDPG) are invoked to process discrete and continuous variables, respectively. Simulation results show that the proposed HDRT algorithm converges fast and outperforms other benchmarks in the aspect of user energy consumption and latency.

With the boom in maritime activities, the need for highly reliable maritime communication is becoming urgent, which is an important component of 5G/6G communication networks. However, the bandwidth reuse characteristic of 5G/6G networks will inevitably lead to severe interference, resulting in degradation in the communication performance of maritime users. In this paper, we propose a safe deep reinforcement learning based interference coordination scheme to jointly optimize the power control and bandwidth allocation in maritime communication systems, and exploit the quality-of-service requirements of users as the risk value references to evaluate the communication policies. In particular, this scheme designs a deep neural network to select the communication policies through the evaluation network and update the parameters using the target network, which improves the communication performance and speeds up the convergence rate. Moreover, the Nash equilibrium of the interference coordination game and the computational complexity of the proposed scheme are analyzed. Simulation and experimental results verify the performance gain of the proposed scheme compared with benchmarks.