International audience

International audience

International audience

Interference cancellation based on spectrum sharing is a key solution to improve the network performance of multi-satellite systems. In this paper, we investigate a user-centric multi-satellite communication system where users are collaboratively served by multiple satellites. In this system, the integrated access and backhaul (IAB) networks, i.e., the satellite-to-user access network and the gateway-to-satellite backhaul network, are jointly considered. Our objective is to maximize the weighted sum rates (WSR) of all users by jointly optimizing the beamformers of multiple satellites and the gateway while considering the backhaul link constraints. To solve this non-convex optimization problem, a centralized algorithm is developed using the block coordinate update (BCU) method, assuming that global channel state information (CSI) is perfectly known. In addition, a low-complexity distributed algorithm based on multi-agent deep reinforcement learning (MA-DRL) is further proposed. This approach offers enhanced flexibility for implementation in multi-satellite systems and adaptability to dynamic environments. Simulation results demonstrate that the proposed distributed algorithm based on MA-DRL can achieve a performance almost equivalent to that of the centralized algorithm. It also reveals that the spatial diversity can be fully exploited through the joint beamforming of a multi-satellite system compared with a single-satellite system, while the growing number of satellites makes the backhaul constraint a more critical bottleneck for system capacity.

In satellite-assisted maritime communications, wave-induced rotational motions of unmanned surface vessels (USVs) cause severe beam misalignment with satellites, significantly degrading transmission performance. To overcome this challenge, we propose equipping the USV with a smart metasurface-based antenna to enable adaptive beamforming that dynamically compensates for USV rolling in harsh sea conditions. To facilitate effective beam tracking under long feedback delays, we design a transmission framework that ensures accurate channel state information (CSI) acquisition. Within this framework, a BLTNet-based model is developed to predict the instantaneous rolling angle of the USV, which is then used to infer the USV-to-satellite CSI for beamforming optimization. We further formulate a stochastic optimization problem to maximize the ergodic achievable rate of the uplink transmission and design a robust beamformer accordingly. Simulation results demonstrate the high accuracy of the proposed rolling angle prediction model under various settings and sea states, confirming that the corresponding robust beamforming design substantially enhances the USV-to-satellite transmission performance.

The Open Radio Access Network (O-RAN) paradigm has attracted considerable attention from both academic researchers and industry stakeholders, owing to its capacity to significantly enhance the adaptability and flexibility of cellular network architectures. However, the disaggregation of Generation NodeB (gNB) in O-RAN introduces increased system complexity and potential variability in performance metrics, necessitating empirical evaluation to determine whether its performance can rival or exceed that of conventional monolithic designs. In this study, we developed a standalone (SA) testbed to quantitatively assess the downlink (DL) transmission performance of two O-RAN architectures, implemented on two widely adopted open-source platforms: srsRAN and OAI. To ensure a scientifically robust and comprehensive analysis, we meticulously selected key performance metrics, implemented measures to provide solid statistical results and eliminate residual effects between test iterations. Test results show that the split enhances network flexibility by offloading certain processing tasks to the Distributed Unit (DU), thereby reducing the overall hardware usage burden and potentially improving the computational efficiency and resource allocation in future RAN networks.

In satellite-assisted maritime communications, unmanned surface vessels (USVs) suffer from intensive rotational motions due to wave fluctuations, resulting in beam misalignment with the satellite and thus deteriorating the transmission performance severely. This paper initially proposes to leverage the smart metasurface at the USV to combat the rolling motion of the USV in hostile sea environments. Specifically, we design an uplink transmission framework for beam tracking and propose a rolling angle prediction model based on triple-layer long shortterm memory (TL-LSTM) to predict the instantaneous rolling angle of the USV. Then the USV-to-satellite uplink transmission rate is maximized accordingly through the joint beamforming design of the USV and the satellite based on the predicted channel state information (CSI). Simulation results demonstrate the robust accuracy of the proposed rolling angle prediction scheme under various sea conditions, while the corresponding beamforming optimization scheme is also significantly efficient in improving the USV-to-satellite transmission performance.

Software-defined Low Earth Orbit (LEO) satellite networks enable efficient and centralized inter-satellite communication management by leveraging real-time network data, supporting high-speed, global communication services. However, due to the necessity of deploying extensive constellations in LEO systems to achieve global geographical coverage, massive data transmission and processing may lead to a shortage in communication throughput and computational resources. This paper proposes a Selective Traffic State Collection and Prediction (STS-CP) scheme to reduce ISL data collection in LEO satellite systems. We first introduce a metric to quantify ISL importance, allowing data collection to focus on the most essential links. To address data incompleteness, we propose a matrix completion module based on a GAT-based autoencoder, which summarizes local dependencies and global patterns to effectively infer uncollected traffic states. A spatio-temporal residual network is proposed for traffic prediction, capable of comprehensively capturing both spatial and temporal correlations. Experimental results validate that the proposed scheme can reduce data collection while preserving the accuracy and robustness of traffic prediction.

Wireless communications involving unmanned aerial vehicles (UAVs) are more vulnerable to the malicious jamming. As a promising solution, intelligent reflecting surface (IRS) can be equipped on the UAVs to achieve anti-jamming transmissions by leveraging the reconfigurable passive beamforming technique. In this paper, we study a wireless communication system where a UAV carries an IRS and acts as a mobile relay for a multi-antenna transmitter and receiver pair in the presence of a smart jammer that transmits jamming signals to the receiver and the IRS simultaneously. By jointly designing the transmit beamformer, IRS reflection phase, and UAV trajectory, we aim to maximize the average achievable rate over the entire flight with effective resistance to jamming attacks. To solve the non-convex problem, we adopt the alternating optimization (AO) algorithm and decompose the problem into two subproblems, i.e., joint optimization of the transmit beamformer and reflection phase for a specific time slot and optimization of the UAV trajectory. Simulation results show that the proposed joint optimization framework can well combat the jammer under various network settings such as changing the position of the jammer and the initial position of the UAV. The proposed algorithm has good convergence and achieves better performance than other benchmark schemes.

Spectrum sharing and interference cancellation are key solutions for multi-satellite systems to improve network performance. In this paper, we investigate a user-centric multi-satellite cell-free communication system where users are collabo-ratively served by multiple satellites. Our objective is to maximize the weighted sum rates (WSR) of all users by jointly optimizing the beamformers of multiple satellites. To solve this non-convex optimization problem, we first assume that the global channel state information (CSI) can be perfectly obtained and propose a centralized algorithm named per-satellite power constraints weighted minimum mean-square error (PSPC- WMMSE). To address practical implementation issues, we further propose a low-complexity distributed algorithm based on multi-agent deep reinforcement learning (MA-DRL). It is more flexible to be ap-plied to the multi-satellite system and is adaptive to the dynamic environment. Simulation results demonstrate that the proposed distributed algorithm can achieve almost similar performance to the centralized algorithm. Moreover, it is verified that the spatial diversity can be fully exploited via joint beamforming of the multi-satellite system compared to the single satellite system.

Blockchain has been deemed as a promising solution for providing security and privacy protection in the next-generation wireless networks. Large-scale concurrent access for massive wireless devices to accomplish the consensus procedure may consume prohibitive communication and computing resources, and thus may limit the application of blockchain in wireless conditions. As most existing consensus protocols are designed for wired networks, directly apply them for wireless users equipment (UEs) may exhaust their scarce spectrum and computing resources. In this paper, we propose AirCon, a byzantine fault-tolerant (BFT) consensus protocol for wireless UEs via the over-the-air computation. The novelty of AirCon is to take advantage of the intrinsic characteristic of the wireless channel and automatically achieve the consensus in the physical layer while receiving from the UEs, which greatly reduces the communication and computational cost that would be caused by traditional consensus protocols. We implement the AirCon protocol integrated into an LTE system and provide solutions to the critical issues for over-the-air consensus implementation. Experimental results are provided to show the feasibility of the proposed protocol, and simulation results to show the performance of the AirCon protocol under different wireless conditions.

One of the main difficulties to enable the future scaling of IoT networks is the issue of massive connectivity. Recently, Modern Random Access protocols have emerged as a promising solution to provide massive connections for IoT. One main protocol of this family is Irregular Repetition Slotted Aloha (IRSA), which can asymptotically reach the optimal throughput of 1 packet/slot. Despite this, the problem is not yet solved due to lower throughput in non-asymptotic cases with smaller frame sizes. In this paper, we propose a new variant of IRSA protocol named Deep-Learning and Sensing-based IRSA (DS-IRSA) to optimise the performance of IRSA in short frame IoTs, where a sensing phase is added before the transmission phase and users' actions in both phases are managed by a deep reinforcement learning (DRL) method. Our goal is to learn to interact and ultimately to learn a sensing protocol entirely through Deep Learning. In this way, active users can coordinate well with each other and the throughput of the whole system can be well improved. Simulation results show that our proposed scheme convergence quickly towards the optimal performance of almost 1 packet/slot for small frame sizes and with enough minislots and can achieve higher throughput in almost all cases.

Cooperative Intelligent Transport Systems (C-ITS) Deployment Platform is considered the newest version of vehicular communication systems, which enables the cooperation between two or more ITS sub-systems to provide enhanced services. With the expanded communication range and system complexity, ensuring the credibility of access nodes and protecting users from being monitored has become a difficult problem in network security, especially the services provided by remote servers like navigation. Transport Layer Security (TLS) is widely used for user authentication and encrypted data transmission in all networks. However, although the TLS handshake complexity is significantly reduced in TLS 1.3 the transmission of a full certificate chain during the handshake is still costly, especially for high-mobility vehicles. In this paper, we propose an optional extension named Certificate Get to reduce the TLS handshake overhead in C-ITS. Specifically, with our proposed extension, the revisiting client transmits a hash value of the certificate chain corresponding to a certain server in the ClientHello message, which can reduce the transmission payload of the certificate chain from an average of 4874 bytes to 68 bytes. Simulation results show that our proposed scheme achieves a significant performance gain by greatly reducing the certificate transmission delay by 50% for both TLS 1.3 and TLS 1.2.

The extensive popularity of Android operating system hones the increased malware attacks and threatens the Android ecosystem. Machine learning is one of the versatile tools to detect legacy and new malware with high accuracy. However, these Machine Learning (ML) models are vulnerable to adversarial attacks, which severely threaten their cybersecurity deployment. To combat the deterrence of ML models against adversarial attacks, we propose a novel randomization method as a defense for image-based detection systems. In addition to defensive randomization, the paper also introduces a novel method, called AutoE, for transforming an APK to an image by leveraging API calls only. To evaluate the effectiveness of randomization as a defense against adversarial settings, we compare our AutoE with two state-of-the-art image-based Android malware detection systems. The experimental results reveal that the randomization is a strong defensive hood for image-based Android malware detection systems against adversarial attacks. Moreover, our novel AutoE detects malware with 96% accuracy and the randomization approach makes it harder against adversarial attacks.

Transport Layer Security (TLS) 1.3 was normalised in 2018, in which an efficient 0-rtt handshake protocol was proposed. For future 5G networks, the 0-RTT handshake will be a more suitable choice for both secrecy and efficiency. However, 4 years after it was proposed, the 0-rtt handshake protocol is still not widely accepted by network service providers due to concerns about its ability to resist replay attacks. In order to address this issue, many solutions have be proposed in the past few year but all of them will increase the complexity and overhead of the 0-RTT protocol. In this paper, we focus on testing whether the 0-RTT handshake protocol is supported by service providers, and testing its performance in a real network environment to verify whether it can withstand continuous optimization in terms of security. Test results show that with 0-RTT, the server received the first application data up to 37 time faster than the 1-RTT and up to 83 time faster than 2-RTT. However, at the client side, the performance of 0-RTT protocol is virtually the same as 1-RTT, as predicted.

Transport Layer Security (TLS) is widely used for user authentication and encrypted data transmission in all kinds of networks. In its newly published version, TLS 1.3, a 0- RTT handshake protocol is proposed for session resumptions in low delay networks, which makes it possible to secure the data transmission and protect users from being monitored in wireless Internet of Things (IoTs). However, the 0-RTT TLS handshake protocol is vulnerable to the replay attack. In this paper, we propose a Time-Based One-Time Password (TOTP) empowered TLS encryption algorithm to resist replay attacks during the handshake process, in which we propose to integrate the TOTP into the encryption process of the EarlyData. It can significantly improve the forward secrecy of the 0-RTT handshake protocol and its capacity to resist the replay attack. On the other hand, we make no changes to the interaction process of the standardized 0- RTT handshake protocol to guarantee the compatibility of our proposed scheme, which makes our proposed scheme suitable for large area wireless IoTs. Simulation results show that under the premise of choosing an appropriate TOTP update rate, our proposed scheme can effectively resist replay attacks while ensuring the processing efficiency of the system.

Among the existing satellite types, Low Earth Orbit (LEO) satellites provide short round-trip delays and are becoming increasingly important. Due to its low orbital profile, the LEO satellites can provide high-speed, low-latency and no dead zone network services for ground users. However, as the number of satellites continues to increase, frequency bands as non-renewable resources will seriously restrict the future development of the Space-Earth integration network. In this paper, a flexible spectrum sharing and cooperative service method is proposed to address the co-linear interference issue caused by LEO satellites while passing through the coverage area of the GEO beam and allows the LEO satellites to provide services for multiple LEO ground users. In our proposed scheme, through continuous power allocation optimization, we ensure that the service of LEO satellites will not reduce the service quality of the GEO beam. At by taking full advantage of the cooperation between LEO satellites, the quality of their service can be significantly improved. Simulation results show that our proposed scheme converges quickly, the transmission efficiency and the stability of the system can all be guaranteed.

5G communications are expected to expand both capacity and flexibility in future vehicular networks. However, due to the wide coverage range of 5G-based networks, massive device access in the 5G era will pose great challenges in access control and terminal management. In order to address the scalability issue in large-scale 5G-based vehicular networks, we propose in this paper the use of two heuristic sharding schemes which are based on the Determinantal Point Process (DPP) with different complexities. Specifically, in the proposed algorithms, both location and wireless channel condition of a base station (BS) are jointly considered respectively as diversity and quality parameters in the DPP. Both of them can effectively control the size of each shard, ensure the shards are evenly distributed and allow in-shard cooperation among the BSs. The communication robustness is then greatly improved due to the efficient in-shard cooperation and the system guarantees stable throughput even in scenarios where transactions volume changes dynamically. While compared to benchmark schemes, the simulation results of the proposed protocol and algorithms show significant performance gains in terms of coverage and load balancing.

In this paper, we investigate the downlink transmission in the integrated terrestrial satellite networks, whereby the same spectrum is shared between two systems, and thus interference to each other should be carefully mitigated. We address this challenging issue by unifying the terrestrial beamformer design and satellite user scheduling into the same optimization framework. This nontrivial problem is decomposed into two subproblems, one deals with the terrestrial beamformer design to control the interference from the terrestrial base stations to the satellite users, the other tries to optimize the scheduling of the satellite users following the framing structure the DVB-S2X standards for satellite communication systems. A deep clustering user scheduling scheme is developed to group suitable satellite users to the same frame using the channel state information as the input feature. Finally, a joint iterative algorithm is designed to maximize the sum rate of all users in the integrated systems. We conduct extensive simulation results to show the effectiveness of the proposed scheme.

Low Earth Orbit (LEO) satellites are becoming increasingly important among the existing satellite communication systems. Due to its low orbital profile, the LEO satellites can provide high-speed, low-latency, and ubiquitous services for ground users. However, as the number of satellites continues to increase, frequency bands as non-renewable resources will seriously restrict the future evolution of the LEO networks. In this paper, a flexible spectrum sharing and cooperative service method is proposed to address the collinear interference issue caused by LEO satellites while passing through the coverage area of the GEO beam. By using the continuous power allocation optimization, our scheme ensures that the service of the LEO satellites will not lead to the degradation of the service quality of the GEO beam. Meanwhile, by taking full advantage of the cooperation between LEO satellites, the quality of their service can be significantly improved. Simulation results show that our proposed algorithm converges quickly. The transmission efficiency and the stability of the system can all be guaranteed.

A salient feature of the vehicular networks is the high mobility of the vehicles, which makes it a challenging issue to provide seamless handover using the conventional dedicated short range communication (DSRC) or cellular network technologies (e.g., 3G/4G). In this paper, we consider the adoption of the dual connectivity (DC) architecture in the vehicular network, which allows the user equipment (UE) to connect simultaneously to a master eNB(MeNB) and a secondary eNB(SeNB), and thus simplifies the signaling and provides enhanced mobility support. To further improve the performance, we propose a raptor codes based dual connectivity (RCDC) scheme, which can effectively address the out-of-order packet delivery problem in the DC scheme, and the coordination between the MeNB and SeNB is significantly reduced. We develop queueing models to characterize the delay performance of the DC and the RCDC schemes by taking into account the handover events in vehicular networks. Simulation results are provided to illustrate the performance of these two schemes under different vehicular network settings, which can prove that the RCDC scheme is more adaptable for the vehicle network with handover events.

In this study, the authors adopt the non‐orthogonal multiple access (NOMA) technique to improve the spectrum efficiency in the wireless backhaul networks, whereby the downlink and uplink NOMA techniques are applied for the backhaul and access links, respectively. Due to the coupling between the backhaul and access transmission stages, the transmission power should be carefully allocated in both stages so that the overall throughput can be maximised. They start with the single user equipment (UE) case and consider different scenarios and analyse the tradeoff between the access and backhaul links, and the optimal power allocation solutions are obtained accordingly. They then extend the analysis to the multi‐UE case and formulate the optimal power allocation problem, which is solved using the Lagrangian dual decomposition algorithm. Simulation results demonstrate that the proposed schemes are effective in improving the throughput and outperforms the conventional orthogonal multiple access technique under different network settings.

In vehicular networks, radio-frequency (RF) jamming attacks are considered a major threat to the availability of control channel (CCH). In particular, vehicles may not be able to receive control messages from roadside units (RSUs) due to persistent interference in the CCH, which may claim human lives and result in significant economic losses. In this article, a cooperative anti-jamming beamforming scheme is proposed to address the CCH jamming problems in vehicular networks. This scheme utilizes spatial diversity provided by the multiantenna RSU and relay vehicles to improve the transmission reliability of downlink control messages. In addition, to address the additive effects of the jamming signals and the intergroup interference, the relay selection problem and the beamformer design problem are jointly considered, which is modeled as a mixed-integer nonlinear programming (MINLP) problem. Then, we address this challenging problem by relaxing it into a series of convex subproblems via the semi-definite relaxation (SDR) and convex-concave process (CCP) methods, and then propose to solve these convex subproblems iteratively. The simulation results show that our proposed method convergences rapidly, and compared to the benchmark schemes, significant performance gains can be observed.

Radio Frequency (RF) jamming attacks constitute a major threat to the availability of control channel communications in the vehicular networks. In particular, the victim vehicles may fail to receive the safety related messages from the Road Side Unit (RSU) due to persistent jamming attacks, which can possibly cause tremendous economic loss and claim human lives. In this paper, we propose a cooperative anti-jamming beamforming scheme for the control channel jamming problem in vehicular networks, which takes advantage of the multi-antenna and spatial diversity provided by the RSU and relay vehicles to improve the transmission reliability of the victim vehicles. The anti-jamming beamformer design problem is formulated as a Mixed-integer Nonlinear Programming (MINLP) problem, which is intractable in general. We address this challenging problem by reformulating it as a sequence of convex sub-problems using the semi-definite relaxation (SDR) and convex-concave procedure (CCP) methods. Simulation results are provided to investigate the convergence of the proposed scheme, and significant performance gain can be observed comparing with other benchmark schemes.

Radio Frequency (RF) jamming attacks represent a major threat to the availability of services in vehicular networks. In particular, if the control channel is under persistent jamming attacks, the vehicles within the jamming area cannot receive the safety related messages from the road side unit (RSU), which can possibly cause tremendous economic loss and claim human lives. In this paper, we propose to adopt the cooperative relaying technique to address this problem, whereby the neighbouring vehicles outside of the jamming area serve as the relay nodes to forward the received control channel signal to the victim vehicles through the jamming- free service channel. To investigate the performance of this cooperative relaying scheme, we analyse the outage probability at the victims under different jamming scenarios based on Poisson point process (PPP) model. Simulation results are provided to validate the theoretical results and show the effectiveness of the cooperative anti-jamming relay scheme under different conditions.

Vehicular networks have been drawing special atten- tion in recent years, due to its importance in enhancing driving experience and improving road safety in future smart city. In past few years, several security services, based on cryptography, PKI and pseudonymous, have been standardized by IEEE and ETSI. However, vehicular networks are still vulnerable to various attacks, especially Sybil attack. In this paper, a Support Vector Machine (SVM) based Sybil attack detection method is proposed. We present three SVM kernel functions based classifiers to distinguish the malicious nodes from benign ones via evaluating the variance in their Driving Pattern Matrices (DPMs). The effectiveness of our proposed solution is evaluated through extensive simulations based on SUMO simulator and MATLAB. The results show that the proposed detection method can achieve a high detection rate with low error rate even under a dynamic traffic environment.