International audience

International audience

We study the feasibility of black-box radio frequency fingerprint (RFF) spoofing, where an adversary lacks access to the target receiver’s data or model. We present a surrogate-guided generative perturbation framework that jointly trains a generator with feedback from multiple surrogate receivers to synthesize low-power, fingerprint-level perturbations. Using real RF fingerprint datasets, we evaluate the spoofing effectiveness of forged perturbations on unseen receivers. Our results show that, even without any feedback from the target, an attacker can successfully impersonate a selected transmitter at an unseen receiver. However, the success remains limited and varies across devices, reflecting both the feasibility and the boundary of black-box RF fingerprint spoofing. These findings provide the first empirical evidence that multi-surrogate training can partially narrow the black-box gap in RF fingerprint spoofing.

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.

Graph-based Network Intrusion Detection Systems (GIDS) are increasingly used to model complex communication patterns and detect sophisticated enterprise threats, yet the reproducibility and replicability of GIDS research remain underexplored, limiting the reliability and generalizability of published results. We present a rigorous reproduction and replication of five state-of-the-art GIDS across four public datasets and a new large-scale enterprise dataset. Even with original code and configurations, reproducing claimed performance is difficult; detection metrics vary by up to 40 percent due to undocumented assumptions, preprocessing discrepancies, and hyperparameter sensitivity. Models also fail to generalize to real-world enterprise traffic, exhibiting high false positive rates and scalability issues. We identify key implementation factors: graph snapshot size and threshold-setting strategies significantly affect detection performance but are inconsistently documented, and several GIDS are vulnerable to evasion attacks. Beyond confirming known challenges (e.g., parameter sensitivity), our results expose a critical reproducibility crisis in the GIDS literature: without transparent and systematic evaluation, reported results may mislead researchers and practitioners. We provide recommendations to improve reproducibility, replicability, and robustness, and urge the community to adopt rigorous standards for empirical evaluation.

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.

This paper presents a novel approach for detecting fraudulent tokens in blockchain transactions using an enhanced Graphs of Graphs model. By leveraging blockchain’s inherent transparency and immutability, our method enables precise tracking of transactional patterns, enhancing fraud detection capabilities and market forecast within decentralized ecosystems. We introduce a self-attention mechanism, Transaction-To-Token Attention (T2T-Attention), which captures both wallet-level and token-level information by maintaining detailed node and cluster interactions. Unlike traditional graph coarsening, T2T-Attention enables a fine-grained analysis of token transfer activities, preserving transaction-level details while also modeling inter-token relationships at a cluster level. To address computational complexity, we use kernelized softmax for efficient processing. Our model outperforms existing methods in detecting fraud and classifying token-related risks, offering a scalable solution to enhance blockchain security and DeFi risk management.

Zero-trust has emerged as a promising security paradigm for next-generation networks (NGN). However, conventional cryptographic schemes struggle with continuous and dynamic authentication due to their coarse granularity and cumbersome processes. Radio frequency fingerprint identification (RFFI), as a prospective solution, enables physical-layer user-transparent identity authentication. Whereas, facing the dynamic topology and device mobility of NGN, such as Internet of Vehicles (IoV), Drone networks, etc., there exists a current deficiency in addressing the significant performance degradation across different receivers. In this paper, we propose a novel RFFI scheme for zero-trust continuous authentication in dynamic NGN environments, enabling unified high-performance cross-receiver identification. A two-stage unsupervised domain adaptation model is designed to extract receiver-independent transmitter-specific features. The receiver-side impact on RFFI, modeled as domain shift, is addressed through adversarial training for global alignment and local maximum mean discrepancy (LMMD)-based subdomain adaptation for eliminating subdomain confusion. Moreover, we further optimize RFFI through data augmentation to enhance robustness, multi-sample fusion inference to handle dynamic uncertainties, and an adaptive few-sample selection strategy for efficient fine-tuning. Extensive experiments on public datasets demonstrate the excellent performance of our proposed scheme in cross-receiver zero-trust wireless networks.

This letter focuses on greedy approaches to select the most informative$k$sensors from$N$candidates to form a measurement submatrix that minimizes the estimation error. It is a submatrix selection problem. We refine conventional greedy sensor selection algorithms based on the square maximum-volume (SMV) submatrices finding method, particularly at their$n$th step, with$n$being the problem dimension. Our main idea is to increase the volume of the square measurement submatrix associated with the$n$sensors by iteratively swapping the selected and unselected sensors based on the dominant property of the maximum-volume submatrix. This simple refinement method ensures a square measurement matrix with increased volume, facilitating the subsequent greedy steps. It can be easily applied to existing greedy algorithms for performance improvement without increasing their complexity order. Numerical results demonstrate the effectiveness of the proposed refinement method in improving several popular greedy algorithms.

Aggregating metadata in the ciphertext field is an attractive property brought by homomorphic encryption (HE) for privacy-sensitive computing tasks, therefore, research on the next-generation wireless networks has treated it as one of the promising cryptographic techniques for various scenarios. However, existing schemes are far from being deployed in various computing scenarios due to their high computational complexity and ciphertext expansion, especially for bandwidth-limited and latency-sensitive wireless scenarios. In this paper, we propose the AirHE scheme to achieve homomorphic evaluation via the over-the-air computation in the physical layer. Moreover, we propose a new encryption scheme that can be integrated with the physical layer procedure. A new error control mechanism for ciphertext is further proposed to solve the error accumulation problem. The novelty of the AirHE scheme is to take advantage of the intrinsic superposition characteristic of the wireless channel, such that the communication and computation cost is greatly reduced by achieving homomorphic evaluation and error control of ciphertext in the physical layer. We implement the AirHE scheme based on the LTE system and validate its feasibility. Simulation results are also presented to show the performance of the AirHE scheme under different channel conditions.

Blockchain has been widely applied in many industries to provide secure and reliable services, in which the activities of the participating nodes are recorded as transactions. Although the original design assumes nodes disseminate the transactions voluntarily, they may be reluctant to provide transactions for others due to the lack of cooperative incentives. To fill the gap, we study the transaction collecting process in the blockchain system under the leader-based consensus protocol. Specifically, we design an incentive scheme to reward the followers if they provide unique transactions to the leader. Considering the selfish nature of different nodes, we model the transaction trading process between nodes as an Iterated Prisoner’s Dilemma (IPD), and a modified zero-determinant (ZD) strategy is proposed such that the follower could correlate the leader’s payoff with the leader’s cooperation probability. We theoretically prove the effectiveness of our proposed algorithm. Simulation results show the leader’s payoff changes under the follower’s different control functions. The proposed scheme can regulate the behavior of blockchain nodes during the transaction trading process.

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.

Semantic communication systems (SCS) have gained extensive attention with the advancement of Artificial Intelligence (AI), which transmits the data feature instead of the raw bits, whereby the communication efficiency can be substantially enhanced, e.g., via a neural network or encoder to convert from the massive user data to corresponding light-weight feature map. However, SCS can be vulnerable to adversarial noise when transmitting the feature data, which may mislead the downstream tasks at the receiver side, e.g., leading to misclassification due to the disturbed receiving information. In this paper, we investigate the overshadowing-based attacks by perturbing the physical signal with artificial adversarial noise during the semantic feature transmission. Specifically, we directly attack the waveform after the modulation of the feature bits, and conduct both the white-box and black-box attacks to evaluate the vulnerability. In our attack methods, we use the local transfer model to acquire the gradient details and provide the gradient-based strategy for generating the perturbation. The experiment results demonstrate that both white-box and black-box attacks can be a critical threat for SCS and significantly degrade the performance of downstream tasks.

The frame-based precoding oriented from the frame structure under the DVB-S2 standard for satellite communications leads to the multicast transmission in each user frame. This paper investigates the multicast-aware user framing/grouping problem to facilitate the frame-based precoding that demands users of high channel similarity in each group. We propose two alternative approaches to increase the intra-group channel similarity by taking into account the channels of all users already in the group when selecting the parallel users for it. One approach extracts the first principle component vector from the channel matrix constituted by current group members and uses it to measure the similarity to the ungrouped users for the selection of the next group member. The other one adds up the projections of the ungrouped user's channel to the channels of the current group members to measure the similarity. Numerical results demonstrate that the proposed two algorithms outperform a benchmark algorithm in various scenarios, verifying the effectiveness of exploiting the channel information of all group members to constitute multicast groups with high intra-group similarity.

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.

The technology development of wireless communication has brought about the rapid growth of various wireless devices, but also brings in many security threats. This paper focuses on the security and privacy problems in wireless authentication and proposes a novel authentication scheme based on the design of reusable fuzzy extractor (RFE) for device's radio frequency (RF) fingerprinting. Firstly, unlike the traditional authentication protocol, our scheme can accomplish the mutual authentication without the storage of long-term secret key, thus tackles with the key-compromise threats. Furthermore, although the scheme authenticates devices based on their RF fingerprint, it does not store RF fingerprinting information explicitly to safeguard it from eavesdroppers who may use it to impersonate the identity of valid users. Finally, our designed protocol relies on the correspondent peer to measure the fingerprint, rather than the device itself, thus is more secure against various adversaries. The security analysis shows the resiliency against theft of secret keys, wireless channel attacks and privacy disclosure. And the performance evaluation demonstrates that the design of RFE for device's RF fingernrinting is efficient in terms of recognition accuracy.

Blockchain-based identity brings in great evolution due to its decentralized deployment, transparent and tamper-free ledger. Specification groups of B5G/6G are exploring into integrate the technology to future network systems, e.g., Internet of Things, vehicular network, industrial communications. However, devices in these systems often have storage constraints and unstable channels, which necessitates lightweight node deployment. The security issue arises: revoked identity can forge a legitimate authentication, since the lightweight verifier does not maintain the revocation transactions. This paper hence proposes AcBF, a novel revocable identity management scheme, that enables extremely low authentication latency by allowing the lightweight node to query the certificate's status locally. To realize this feature trustfully, we design a revocation transaction based on accumulator-assisted Bloom filter to minimize the storage of certificate status structure. Secondly, we construct the blockchain protocol to ensure that no revocation event slips on any lightweight ledger, even in an insecure or unstable communication environment. In addition, different from other revocation mechanisms, AcBF minimizes the impact on valid users during the revocation process. Through security and performance analysis, AcBF has shown strong security and advantageous efficiency on both lightweight verifiers and certificate owners, thus suits identity management systems with low-latency constraints.

We consider a$\mathrm{K}$user interference network with general connectivity, described by a matrix N, and general message flows, described by a matrix M. Previous studies have demonstrated that the standard interference alignment (IA) scheme might not be optimal for networks with sparse connectivity. In this paper, we formalize a general IA coding scheme and an intuitive number-filling puzzle for given M and N in a way that the score of the solution to the puzzle determines the optimum sum degrees that can be achieved by the IA scheme. A solution to the puzzle is proposed for a general class of symmetric channels, and it is shown that this solution leads to enhanced Sum-DoF compared to the standard IA scheme.

Distributed denial-of-service (DDoS) attacks can cause significant damage to network applications. A crucial step in combating these attacks lies in promptly and accurately detecting DDoS attack traffic. However, due to data insufficiency (imbalance) and contamination, existing solutions fail to yield satisfactory results for DDoS detection. Furthermore, current methods typically require access to raw data for training, posing a significant privacy risk. To tackle these challenges, we propose HFL-AD, a hierarchical federated learning framework specifically designed for detecting DDoS attack traffic. In our approach, a federation of lower layer clients train local anomaly detection models using diverse raw data. A selected few clients, possessing a small supplementary dataset, serve as upper layer clients, responsible for excluding model updates trained on contaminated datasets. Experimental results demonstrate that HFL-AD outperforms baseline solutions in DDoS detection, particularly when some training datasets are contaminated.

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.

Machine learning models are widely applied for vehicular applications, which are essential to future intelligent transportation system (ITS). Traditional model training methods commonly employ a client-server architecture to perform local training and global iterative aggregations, which can consume significant bandwidth resources that are often absent in vehicular networks, especially in high vehicle density scenarios. Modern vehicle users naturally can collaboratively train machine learning models as they are the data owner and have strong local computing power from the onboard units (OBU). In this paper, we propose a novel collaborative learning scheme for mobile vehicles that can utilize the opportunistic vehicle-to-roadside (V2R) communication to exploit the common priors of vehicular data without interaction with a centralized coordinator. Specifically, vehicles perform local training during the driving journey, and simply upload its local model to roadside unit (RSU) encountered on the way. RSU's model will be updated accordingly and sent back to the vehicle via the V2R communication. We have theoretically shown that RSUs' models can eventually converge without a backhaul connection. Extensive experiments upon various road configurations demonstrate that the proposed scheme can efficiently train models among vehicles without dedicated Internet access and scale well with both the road range and vehicle density.

This paper investigates an intelligent reflecting surfaces (IRS) aided anti-jamming communication strategy in the integrated terrestrial-satellite network (ITSN), where the IRS is exploited to mitigate jamming interference and enhance the integrated system communication performance. In such a network, the terrestrial network and satellite network are co-existing with a spectrum-sharing scheme in the presence of a multi-antenna jammer. We aim at maximizing the weighted sum rate (WSR) of all users by jointly optimizing the terrestrial beamformers and IRS phase shifts while considering the signal-to-interference-plus-noise ratio (SINR) requirements of legitimate users. Different from the non-convex optimization techniques utilized in the IRS-related problem, a novel optimization-driven deep reinforcement learning (DRL) algorithm is proposed, which leverages both the robustness of model-free learning approaches and the efficiency of model-based optimization methods. In the optimization module of the proposed algorithm, we analyze the smart jammer under the unknown jamming model and derive a lower bound of the anti-jamming uncertainty, such that the IRS-aided anti-jamming problem can be solved by alteration method with second-order cone programming (SOCP) algorithm and semidefinite relaxation (SDR) technique. Simulation results demonstrate that the IRS can enhance the anti-jamming performance efficiently, and the proposed optimization-driven DRL algorithm can improve both the learning rate and the system performance compared with existing solutions.

Over-the-air computation (AirComp) is a novel technology that utilizes the superposition characteristic of the wireless multiple-access channel to accomplish communication and computation tasks simultaneously, which can be used to achieve efficient data fusion in wireless networks. However, the performance of AirComp can be compromised due to non-ideal conditions in practical systems, such as limited transmitting power budget, receiving noise, etc. In this paper, we first propose a joint transmitting-receiving power control scheme for over-the-air computation in the OFDM-based multicarrier wireless system, which can minimize the mean square error (MSE) of the received signal by taking into account of limited transmitting power budget and receiving noise. Based on the special structure of the problem, which depends on the set of users that either use up their power budget or not, we decompose the problem into two sub-problems, one deals with the power allocation at the transmitters, the other deals with the power scaling at the receiver. The optimal results are obtained by searching the set of users with used up power budget and solving these two sub-problems accordingly. We then propose an efficient channel state information (CSI) acquisition and feedback scheme for the AirComp power control scheme, and the effect of imperfect CSI is also considered accordingly. We provide extensive simulation results to demonstrate the performance of the proposed scheme under different network conditions.

Radio frequency fingerprint identification (RFFI) has been widely studied as a physical layer security scheme for device identification and authentication in wireless scenarios, such as Internet of Things (IoTs), industrial wireless networks, Internet of Vehicles (IoV), etc. Typical RFFI approaches train a model at the receiver to extract hardware defects of the transmitter RF front-end using a deep learning-based method and achieve classification. However, few works have taken into account its shortage in multiple-receiver scenarios, where the identification accuracy significantly decreases when migrating a model trained on the known receivers to the new ones, directly. In this paper, we propose a novel cross-receiver RFFI scheme to improve the performance and the generalization of the fingerprinting classification tasks on new receivers. This scheme tackles the shortage by two means: 1) we extract receiver- independent features using global domain adaptation based on adversarial training and relevant subdomain adaptation based on local maximum mean discrepancy (LMMD); 2) The performance is further improved by fine-tuning on few labeled samples when domain adaptation is not effective. The second mechanism brings in significant performance advantage, without a large amount of labeled data on new receivers. Experimental results on public datasets show the outstanding performance of the proposed scheme in cross-receiver scenarios.

Machine learning mechanisms are applied to detect the unique characteristics of the wireless interface or signaler that can distinguish one device's signal pattern from the others, which has been widely researched as the fingerprint for user identification. However, such fingerprinting can also be used for malicious purposes, i.e., identification tracking, undesired positioning, etc., as the unique features of the radio signal from a device is determined at the manufacturing stage and often cannot be easily removed afterward. To prevent privacy leakage from such radio frequency (RF) fingerprinting, in this paper, we propose an adversarial mechanism to hide the fingerprint whereby the device's identification cannot be induced by machine learning models from the preamble. Specially, we apply the adversarial attack method to attack the fingerprinting model by adding optimized adversarial perturbation to the preamble that can mislead the model classification results. To alleviate the adversarial sample's impact on communications and ensure the execution of packet detection at receivers, we improve the identification protection strategy with sparse perturbed features. In order to prevent further fingerprinting of re-training over the perturbed RF feature, we extend our method with the time-varying perturbations to further hide the device's identity. Extensive experiments are conducted, and we show that the proposed method can effectively hide the device identification from both the dedicated fingerprint model and the re-trained one from perturbed signals without disturbing the preamble functionality, which provides a gratifying confirmation of the proposed method.

The integrated terrestrial satellite networks (ITSNs) have been deemed as a promising solution to ubiquitous Internet access anytime and anywhere. In this paper, we investigate the spectrum sharing problem in ITSN, in particular focusing on the downlink transmission of the satellite network, which adopts the framing structure for the satellite users (SUs). We model the interference from both terrestrial downlink and uplink transmissions to SUs according to the beamforming techniques. For the terrestrial downlink transmission, we assume that terrestrial users (TUs) are served cooperatively by multiple small base stations (SBSs) via joint beamforming, while the virtual multiple access channel (VMAC) scheme is adopted for the terrestrial uplink transmission. We propose the optimization framework by jointly considering the terrestrial beamformer design and satellite user scheduling to maximize the sum rate of all users. The optimization problems are decomposed into three sub-problems: satellite user scheduling, terrestrial beamformer design, and time slot allocation, which are solved by deep clustering, second-order cone programming (SOCP) (or fractional programming (FP)), and linear programming, respectively. Then, an alternating iterative algorithm is designed to obtain the optimal solution. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm in multiple cases.

Intelligent reflecting surface (IRS) is a novel technology to manipulate wireless propagation channels via smart and controllable signal reflection. In this paper, we investigate an IRS-aided integrated terrestrial-satellite network (ITSN) system, where the IRS is deployed to assist the co-existing transmissions of the terrestrial small base stations (SBSs) and the satellite. Because of the spectrum sharing in the ITSN, the interference between the two systems should be carefully mitigated. Our objective is to maximize the weighted sum rate (WSR) of all users by jointly optimizing the frame-based coordinated transmit beamforming vectors at the SBSs, the phase shift matrix at the IRS, and the frame user scheduling, subject to SBSs’ individual power constraints and unit modulus constraints of phase shifters. To this end, we first adopt the agglomerative hierarchical clustering (AHC) method to schedule the satellite users to different frames. Then the block coordinate descent (BCD) algorithm is proposed, which alternately optimizes the transmit beamforming vectors and the reflective phase shift matrix. In particular, the optimal transmit beamforming vectors are obtained via the fractional programming (FP) technique. Meanwhile, two efficient algorithms, i.e., the Riemannian manifold (RM) and the successive convex approximation (SCA), are proposed for the phase shift optimization. Finally, simulation results are provided to demonstrate the performance gain of our schemes over other benchmark schemes.

Over-the-air computation (AirComp) is a novel technology that utilizes the superposition characteristic of the wireless multiple-access channel to accomplish the communication and computation tasks simultaneously, which can achieve efficient data fusion in large-scale wireless networks. However, in practice, the performance of AirComp is distorted by some non-ideal factors, including limited transmit power budget, receiving noise, and imperfect channel estimation. In this paper, we propose an optimal transmitting-receiving power control scheme for over-the-air computation in the multicarrier system with these non-ideal factors. We optimize the over-the-air computation system by minimizing the mean square error (MSE) of receiving signal. The results show that when a user needs to use up all power budget, the optimal power allocation policy among sub-carriers is a proportional fairness scheme and whether the user needs to use up all power budget depends on its channel compensation capability and receiving scaling policy. We also provide computation simulation results to demonstrate the optimum of the proposed scheme.

This paper proposes to implement the wireless blockchain in a cellular system and focuses on the communication demanding consensus processes. The consensus processes within the cellular network based on three typical consensus protocols, i.e., PoW, Raft and PBFT, are first analyzed. The delay optimal transmission problems subject to consensus traffic demands and power constraints are formulated for uplinks and downlinks respectively. The goal is to minimize the transmission airtime by joint user scheduling and power control of the multi-cell network. We adopt the column generation (CG) and fractional programming (FP) methods to improve transmission efficiency in consensus process. The original problem is first decomposed by the CG method into a restricted master problem and a pricing problem. Then, the FP method is adaptively adapted to optimize the pricing problem for potential improvement of the current solution. The two sub-problems are iteratively processed until the optimal solution is obtained. Numerical results demonstrate that the proposed CGFP algorithm efficiently mitigates the inter-user interference, attributing to the joint spatial-temporal optimization, and notably improves the transmission delay performance.

A major concern of blockchain systems is to scale up their throughput. Many improvements and novel consensus protocols have been proposed to address this issue, but they are intrinsically limited by the message synchronization latency of the underlying peer-to-peer (P2P) network. Most existing implementations of blockchain systems are based on the unstructured random overlay disseminating networks, which often results in a heavy-tailed delivery latency distribution, impairing the decentralization property of the blockchain system. To overcome these constraints, this research proposes Urocissa, a structured overlay protocol to reduce the delivery latency and to improve steadiness for blockchain systems. By exploiting the unique characteristics of blockchain traffic and network heterogeneity, the protocol maintains multiple minimum latency broadcasting trees in a distributed way, whereby each node communicates with its neighbors and makes decisions sovereignly to balance relaying tasks among participants. Experiments show that the proposed protocol significantly reduces block delivery and confirmation latency compared to the conventional blockchain delivery network protocols.

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.

The problem of single-group multicast beamforming (SMBF) is well-known NP-hard. It motivates the pursuit of computationally efficient near-optimal solutions. Due to multicasting, the multicast group is bottlenecked by the user(s) with the minimum received signal-to-noise ratio (SNR). This paper provides an in-depth interpretation of the SMBF problem from the multicasting point of view and proposes to solve it in two steps: i) select the bottlenecking users by a machine learning approach based on determinantal point process (DPP), and ii) design the beamformer for the selected users. The DPP model jointly considers the magnitudes and directions of users’ channel vectors, and thus enables an efficient selection of the bottlenecking users. Moreover, for a specific channel model, the DPP model is only associated with network size and each takes a one-off training cost, thus can be used as a codebook. The proposed DPP-based subset selection is incorporated adaptively into two fast beamforming algorithms, i.e., the QR decomposition algorithm and the successive beamforming (SB) algorithm. They specifically design the beamformers for the selected users by leveraging channel orthogonalization therein. Numerical results demonstrate the superiority of the proposed QR-DPP and SB-DPP algorithms in terms of the performance-complexity compromise and their robustness to different scenarios.

This paper investigates a multiple intelligent reflecting surfaces (IRSs) aided integrated terrestrial-satellite network (ITSN), where the IRSs are deployed to cooperatively assist the low channel gain users in the co-existing transmission system. In such a network, the coordinated beamforming and frame based transmission scheme are considered for the terrestrial network and the satellite network, respectively. We aim at maximizing the weighted sum rate (WSR) of all users by jointly designing the frame based beamforming at the small base stations (SBSs) and the phase shifts at the IRSs, subject to the individual maximum SBS's transmit power constraints and the IRSs' reflection constraints. This non-convex problem is firstly decomposed via fractional programming (FP) technique in the objective function, then transmit beamforming vectors and reflective phase shifts matrix are optimized alternatingly. A block coordinate descent (BCD) method is proposed to obtain the stationary solution. Simulation results verify the effectiveness of the proposed algorithm compared with different benchmark schemes.

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.

This paper investigates an intelligent reflecting surface (IRS)-aided integrated terrestrial-satellite network (ITSN) system for the low channel gain users, where an IRS is deployed to assist the co-existing transmissions of the terrestrial small base stations (SBSs) and the satellite. Because of the spectrum sharing in the ITSN, the interference between two systems should be carefully mitigated. We aim for maximizing the weighted sum rate (WSR) of all users through jointly optimizing the frame based coordinated transmit beamforming vectors at the SBSs and the phase shift matrix at the IRS, and the frame user scheduling subject to each SBS's power and unit modulus. To this end, we propose efficient algorithms based on alternating optimization, in which the transmit beamforming vectors and reflective phase shifts matrix are optimized in an alternating manner. In particular, we develop the second-order-cone programming (SOCP) for optimizing the coordinated transmit beamforming and propose the Riemannian conjugate gradient (RCG) for updating the reflecting shifts. For frame user scheduling, we propose the chordal distance measure method to improve the intra-fame correlation. Simulation results verify the effectiveness of the proposed algorithm compared with different benchmark schemes.

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.

Traffic signal control for a large-scale traffic network is one challenging problem in intelligent transportation systems (ITS). High communication overheads are typically required to achieve the optimal control of the traffic signals in multiple road intersections. In this paper, in order to avoid these communication overheads among spatially distributed intersections, we propose to recover the global traffic state at each intersection in a real-time fashion by only utilizing the traffic state observed at the local intersection. Specifically, a generative adversarial network (GAN) based traffic information recovery method is presented for each intersection controller to recover the global traffic state. We also exploit a few statistics from other intersections during the training of the proposed GAN to improve the traffic state recovery accuracy. Comprehensive numerical results demonstrate the effectiveness of the proposed scheme in recovering the global traffic state.

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.

Due to the ever-increasing time-sensitive industrial applications, critical-machine type communication (C-MTC) is a promising technique for timely delivery services in industrial wireless networks (IWNs), where vicinal devices can benefit from device-to-device (D2D) communication for low power consumption and latency. For real-time applications, Age of Information (AoI) is an essential metric that represents the freshness of data from the perspective of destinations. Thus, an AoI orchestration agent for link scheduling in D2D-enabled IWNs is needed. Most existing works on AoI deal with this scheduling in a centralized manner, which cannot afford timely packet delivery requirements for numerous D2D devices. Different from the existing works, a learning-based autonomous AoI and power orchestration agent, namely, L-AoI, is proposed for D2D-enabled IWNs in this article, where D2D devices adaptively compete for wireless resources in a distributed manner. As a result, the global channel state information as well as the actions of other D2D devices are unknown. Hence, D2D devices deal with this uncertainty under the guidance of L-AoI so that AoI constraints can be respected. By leveraging from the belief-based Bayesian reinforcement learning, L-AoI learns the scheduling action profile with strategies of other D2D devices considered so that the spectrum sharing coalitions can be intelligently formed. Both theoretical analysis and simulation are provided to validate the performance of L-AoI in terms of AoI stability and violation ratio.

To promote the revolution of Industrial Internet of Things, the next generation communication system is expected to provide latency critical services in industry. However, for the traditional downlink-centric cellular systems, the timely delivery of packets cannot be guaranteed by the default dynamic access scheme due to complex signaling procedure. A promising solution to low-latency access is the resource pre-allocation scheme based on the semi-persistent scheduling (SPS) technique, however at the expense of low spectrum utilization. Aiming to make those pre-allocated resources more rewarding, a so-called DPre, a predictive pre-allocation scheme based on learning for low-latency uplink access in industrial wireless networks, is proposed in this paper. It intelligently explores the correlation of devices' access behavior and device utility diversity through sequential learning. Thus, flexible and judicious per-allocation decisions in both time and frequency domains can be made in an on-demand manner. Moreover, with the proposed temporal-spatial utility metric, DPre is guaranteed to reserve for more informative devices. Both theoretical analysis and simulation validate its high spectrum utilization through accurate prediction and the potential to pre-allocate for valuable packets.

As a candidate radio access technique for 5G, Non- Orthogonal Multiple Access (NOMA) has become an important research topic. Radio Frequency (RF) jamming attack can reduce the communication efficiency in NOMA system. Moreover, the jammer equipped Reinforcement Learning (RL) algorithm will be more destructive. On the other hand, the base station (BS) can implement RL to counter the jamming attack. Thus, the whole system evolves to a multi-agent RL system. The interaction between agents results in a highly dynamic environment and the equilibrium state of the system cannot be intuitively predicted. In the past few years, based on Evolutionary Game Theory (EGT), numbers of researchers have developed useful tools to study the multi-agent RL system in detail. The EGT tools give us insight into the equilibrium of the system and make it possible to compare the performance of different RL algorithms. In this paper, we investigate the anti-jamming problem in the NOMA system where both the base station and the jammer equip RL algorithm. We establish the two-player game and demonstrate the existence and uniqueness of equilibrium. Three RL algorithms and their learning dynamics are introduced, which are Q-learning, Lenient Frequency adjusted Q-learning and Regret Minimization. In experiments, the simulation result shows consistency to the theoretical result given by EGT. Regret Minimization outperforms the other two algorithms in term of average reward and converging rate.

The threat of radio frequency jamming attack to cognitive radio network is an issue that has been discussed for a long time. Q-learning is a widely used anti-jamming algorithm due to its model-free characteristic. However, the traditional Q-learning based anti-jamming algorithms suffer from some limitations when dealing with high-dimensional or continuous inputs. The recently proposed double Deep Q-learning Network (DQN) overcomes this weakness by approximating the table based Q function with a deep neural network. In this paper, we apply the double DQN algorithm with frequency hopping strategy against RF jamming attack in a multi-user environment. We test the performances of three types of neural networks which are the fully connected network (FCN), the convolutional neural network (CNN) and the long short term memory (LSTM). The simulation shows the effectiveness of the double DQN algorithm. Meanwhile, the FCN agent gives the best result concerning stability.

Traffic monitoring is instrumental to a number of applications such as traffic engineering, QoS routing, anomaly detection and so on. With an accurate global view, software defined networking (SDN) has the capability to offer flexible, non-intrusive flow measurement by using wildcard matching in both direct per-flow and indirect aggregated manners. As a result, the complete and fine-grained traffic matrix (TM) monitoring, which is a challenge in traditional large-scale sensor networks, becomes more accessible. However, exiting SDN monitoring solutions have a poor trade-off between the resource-hungry nature of full sampling and limited accuracy of TM inference. Thus, in this paper, we aim to address this issue by developing CFlow, a lightweight compressive flow statistics collection (FSC) scheme for SDNs. By taking advantage of the low-rank and short-term stability features of real-world TMs, CFlow selectively samples flow statistics through custom-tailored wildcard rules, with which the final TMs are recovered via the matrix completion technique. Moreover, since the accurate measurement of large flows can improve the overall TM estimation performance, CFlow successively learns these informative flows with additional observation from both per-flow statistics collection and the latest recovered TMs. Simulation results based on real TMs demonstrate that CFlow can not only provide fine-grain visibility into network traffic but also avoid considerable monitoring overhead.

The growing popularity of industrial wireless networks (IWNs) is driven by various applications with stringent timeliness requests. However, the ossification, deep-rooted in the one-application one-network architecture of traditional IWNs, impedes the evolution of IWNs toward smart factory. As a solution, the slice-based network virtualization (NV) breaks the tight coupling between applications and network infrastructure, and thus provides a more flexible and scalable IWN architecture. The application of NV relies on the algorithms that instantiate multiple virtual networks (VNs) on a substrate infrastructure, known as VN embedding (VNE). However, existing VNE algorithms are not necessarily optimal for IWNs due to the absence of QoS-compliant capacity. To this end, so called iVNE, an intelligent latency-aware VNE scheme, is proposed to provide deadline guarantee for various industrial VNs (IVNs), which involves both static embedding and dynamic forwarding. In the static stage, an anypath embedding algorithm is introduced for the new arrival of IVNs so that their resource demands and deadlines can be satisfied with coarse grain. Then, a dynamic anypath forwarding method is incorporated into iVNE to offer intelligent latency sensing via deep Q-learning, and thus forwarding adjustments can be made timely to address the dynamic changes of link quality and network workload. The simulation results are provided to demonstrate the learning efficiency as well as the ability of load-balancing through responsive forwarding under dynamic environment.

We consider the downlink transmissions in a two-tier ultra-dense wireless network, whereby data is delivered from the base station (BS) to the cache-enabled access points (APs) through wireless backhaul links using multicast beamforming technique, and then the users are served by the APs using joint beamforming technique in the access links. To reduce the transmission delay incurred in the backhaul links, the requested data may be cached in some APs in advance during the off-peak time, and thus the users can download the data from the cached APs directly. However, under some circumstances, it is beneficial to select some un-cached APs to the serving set as long as the reduced delay in the access link is more than the delay penalty in the backhaul links. We formulate this problem as a delay optimal beamformer design (DOBD) problem, which attempts to minimize the transmission delay experienced in the backhaul and access link transmission stages. This problem is nontrivial due to the non-convexity and coupling between backhaul and access links, which is transformed into a tractable form using the semi-definite relaxation (SDR) and sequential convex approximation (SCA) schemes, and thus can be solved efficiently using an iterative algorithm. Simulation results demonstrate that the proposed scheme can converge to the stationary point quickly under different network settings, and the overall transmission delay can be reduced significantly comparing with the conventional schemes.

Driven by mission-critical applications in modern industrial systems, the 5th generation (5G) communication system is expected to provide ultra-reliable low-latency communications (URLLC) services to meet the quality of service (QoS) demands of industrial applications. However, these stringent requirements cannot be guaranteed by its conventional dynamic access scheme due to the complex signaling procedure. A promising solution to reduce the access delay is the pre-allocation scheme based on the semi-persistent scheduling (SPS) technique, which however may lead to low spectrum utilization if the allocated resource blocks (RBs) are not used. In this paper, we aim to address this issue by developing DPre, a predictive pre-allocation framework for uplink access scheduling of delay-sensitive applications in industrial process automation. The basic idea of DPre is to explore and exploit the correlation of data acquisition and access behavior between nodes through static and dynamic learning mechanisms in order to make judicious resource per-allocation decisions. We evaluate the effectiveness of DPre based on several monitoring applications in a steel rolling production process. Simulation results demonstrate that DPre achieves better performance in terms of the prediction accuracy, which can effectively increase the rewards of those reserved resources.

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.

This paper presents a joint design framework of fronthaul and access links in cloud radio access networks, wherein the fronthaul data delivery between the central processor (CP) and small-cell base stations (SBSs) is carried over wireless links, which is more cost effective and flexible than the conventional wired fronthaul solutions. In this framework, the coordinated beamforming scheme is adopted by the SBSs to serve the users cooperatively, which not only reduces the bandwidth requirement over the fronthaul links, but also eliminates the inter-cell interference in the access links. To further exploit the spatial diversity over the fronthaul links, the multiuser beamforming scheme is adopted by the CP to deliver the data of all users to their serving SBSs simultaneously. This non-convex and combinatorial optimization problem is reformulated to a unified beamformer design problem using the ℓ0/ℓ1-norm framework, which is still difficult to obtain the optimal solutions. Therefore, we first propose an algorithm based on the difference of convex (DC) programming scheme to find the suboptimal solutions, whereby the formulated problem is transformed to the standard DC programming problem and solved iteratively using the convex-concave procedure algorithm. We then propose another algorithm based on the successive convex approximation and weighted minimum-mean-squarederror approaches, and solve the transformed problem using the block coordinate update scheme. The pros and cons of these two algorithms are discussed, and simulation results are provided to demonstrate the performance gain of our schemes over 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.

The evolution of industrial wireless sensor networks (IWSNs) is driven by various factory automation applications with strict demands on latency and reliability, which requires flexible network resource allocation to support diverse QoS requirements of different applications. To this end, we propose an application-driven virtual network embedding (AVNE) scheme to facilitate the QoS provisioning for different applications leveraging the network virtualization (NV) technique. AVNE employs a novel anypath link mapping approach based on the anypath routing scheme, which greatly improves the efficiency of the embedded path by exploiting the unique features of wireless channels. Our simulations demonstrate that the proposed AVNE scheme significantly improves the revenue and admission ratio while minimizing the cost in virtualized IWSNs. AVNE also achieves better load-balance and thus is less prone to cause bottleneck nodes and links.

The cloud radio access network (C-RAN) has been deemed a cost-effective architecture for exploiting the capacity benefit of densely deployed radio access points. The low-latency fronthaul data transmission from the central processor to small-cell base stations (SBSs) is a key requirement in C-RANs for which conventional wired fronthaul links will be cost-prohibitive and also inconvenient. Therefore, scalable and low-cost wireless fronthaul solutions have drawn much attention in both industry and academia. In this paper, we propose adopting the multicast beamforming strategy over fronthaul links to deliver each user's message to a cluster of SBSs selected according to the user-centric clustering scheme, which then adopts the joint beamforming technique to cooperatively transmit the signal to the target users. Some approximate techniques are applied to obtain a tractable formulation for this mixed integer nonlinear programming problem, and an iterative algorithm based on the block coordinate update method is proposed accordingly. Then, a binary search based algorithm is developed to preserve the sparsity of beamformers due to the relaxation of the discrete clustering function with the continuous exponential function. Extensive simulation results are provided to show the performance of the proposed algorithms in terms of convergence, power consumption, and weighted sum rate.

In industrial cyber-physical systems (ICPSs), state estimation provides the best possible approximation for the unmeasurable system state based on the received measurements from sensors via lossy wireless channels. As a result, the estimation performance heavily depends on the transmission reliability. In this paper, a cognitive radio assisted cooperative transmission scheme is proposed to improve the accuracy of state estimation by delivering necessary redundant measurements to the remote estimator. The relationship between the accuracy of multi- sensor state estimation and the arrival rate of measurements is explored. Based on this, an optimization problem is formulated to minimize the state estimation error by jointly allocating the harvested licensed channels and the ISM channels with power control and admission control. A sub-optimal decomposition scheme is proposed to solve this intractable problem efficiently. Numerical results demonstrate that the proposed scheme significantly outperforms existing schemes by reducing more than 73% packet loss rate and 56% estimation errors.

The cloud radio access network (Cloud-RAN) is an emerging network architecture for the next generation mobile wireless networks. However, connecting a large number of small base stations (SBSs) through wired backhaul links is inconvenient and cost-prohibitive. Therefore, wireless backhaul has become a promising solution for Cloud-RAN. In this paper, we propose to apply the multicast beamforming technique in wireless backhaul, which is efficient to share each user's message to a cluster of SBSs for collaboratively serving the user through joint beamforming. The joint optimization of multicast beamforming in backhaul links and user-centric clustering in access links is formulated as a weighted sum rate maximization problem. To tackle this intractable problem, several approximation and transformation techniques are introduced based on the semidefinite relaxation method and successive convex approximation approach. A block coordinate descent algorithm is developed to solve the approximate optimization problem iteratively by exploiting its special structure. Simulation results show that the proposed algorithm is guaranteed to converge to the stationary point, and the performance outperforms other wireless backhaul schemes under various network scenes.

Traffic management, road sensing and multimedia delivery in vehicular ad-hoc network (VANET) are application domains whose performance depend on network throughput. Rate adaptation is the key method to maximize the throughput by estimating the current channel qualities and deciding the best bitrate for the next frames. In VANET, rate adaptation is more challenging due to the rapid variation of channel qualities caused by the high speed and density of vehicles. Fortunately, vehicles are subject to certain recurring patterns particularly when vehicles communicate with the road side units (RSU). In this paper, we design and implement a location-related rate adaptation algorithm (LRRA) which combines the historical information stored in database and current channel conditions to jointly maximize the throughput. We evaluate LRRA with outdoor experiments and ns-3 simulations. The results show that LRRA is superior to most current rate adaptation algorithms.

Wireless backhaul is a promising technology to lower the cost for connecting a large number of densely deployed access points in the emerging fifth generation wireless system. In this study, the authors consider the joint optimisation of resource allocation in wireless backhaul links and user‐centric clustering in the access links. The objective is to maximise the weighted sum rate of all users under the backhaul resource constraints. To solve this intractable problem, they first adopt the ℓ 1 ‐norm approximation approach to convert the non‐convex backhaul constraints into the solvable forms. They then reformulate the objective function as a set of second‐order cone constraints based on the sequential parametric convex approximation method. An iterative algorithm is proposed to solve the transformed problem based on its special property. Simulation results show that the proposed algorithm outperforms other existing schemes under different network settings.

Multimedia streaming applications with stringent QoS requirements in 60 GHz mmWave wireless personal area networks (WPANs) demand high rate and low latency data transfer as well as little service disruption. In this paper, we consider the problem of robust relay placement in 60 GHz WPANs with directional antenna. Relays forward traffic from transmitter devices to receiver devices facilitating i) the primary communication path for non-line-of-sight (NLOS) transceiver pairs, and ii) secondary (backup) communication path for line-of-sight (LOS) or NLOS transceiver pairs. By incorporating a classic directional antenna model and characterizing the link contention, we formulate the robust minimum relay placement problem and the robust maximum utility relay placement problem with the objective to minimize the number of relays deployed and maximize the network utility, respectively. Efficient algorithms are developed to solve both problems and have been shown to incur less service disruption in presence of moving subjects that may block the LOS paths in the environment.

In this paper, we consider the problem of concurrent transmissions in a wireless network consisting of multiple radio access technologies (multi-RATs). That is, a single flow of packets is dispatched over multiple RATs so that the complementary advantages of different RATs can be exploited. One of the challenging issues arising in concurrent transmissions is the packet out-of-order problem due to diverse wireless channel states and scheduling policies of different RATs, leading to substantial performance degradation to delay sensitive applications. To address this problem, we first propose a state-independent packet dispatching (SIPD) policy, which attempts to find the traffic dispatching ratios over multiple RATs to minimize the maximum average delay across different RATs in the long run. We further propose a state-dependent packet dispatching (SDPD) policy, which achieves fine-grained packet dispatching in the short-term. We use the value function as a measure of the admittance cost for packet dispatching given the current queueing states, and formulate the SDPD problem as a convex optimization problem. We derive the closed-form solutions for both problems for the special case of two RATs, and adopt the dual decomposition technique as the solution for the general cases. Simulation results are presented to compare the performance of the proposed schemes with existing solutions.

The heterogeneity of different radio access technologies (RATs) in the tightly integrated multi-RAT networks can be exploited to support bandwidth hungry applications. However, the out-of-order problem due to concurrent transmissions over multiple RATs may compromise the performance gain if packets are not well dispatched between different RATs. In this paper, we address this problem by adopting {\it value function} as a measure of the admittance cost based on current RAT and queuing states, with which the online packet dispatching problem is formulated as a nonlinear mix- integer programming problem. We obtain the close-form solution for this problem for the special case of two RATs and design a gradient algorithm based on the dual decomposition technique for general cases. Simulation results show that the proposed scheme significantly reduces the delay variation over different RATs, which is effective in alleviating the out-of-order problem in concurrent transmissions.

In this paper, we consider the relay selection and power allocation problem in an amplify-and-forward relaying network, the objective is to minimize the outage probability given that only mean channel gain information is known. We firstly show that relay selection is important in achieving the global optimal solution in addition to the power allocation. Based on this argument, we then propose to decompose the problem into two parts: relay selection and power allocation. For the relay selection problem, a novel scheme is designed to incrementally select relays according to their ordering of mean channel gain. Then for the given set of relays, the optimal power allocation for the source and relays are obtained by exploiting the special structure of the problem. Simulation results show that the proposed scheme outperforms existing schemes without relay selection, which is also very efficient since the results obtained by this scheme are very close to the optimal results achieved by exhaustive search.

In this paper, we study the resource allocation problem for multicast in an Orthogonal Frequency Division Multiplexing (OFDM) based multicarrier wireless system. The objective is to find the subcarrier and power allocation for a set of multicast groups such that the receivers of each group can receive their intended packets. Due to frequency selective and time-varying fading wireless channels, some receivers may fail to receive their packets in the assigned subcarriers. On the other hand, these unsatisfied receivers may obtain side information about the packets of other groups by overhearing other subcarriers, which can be exploited to improve the transmission efficiency using the index coding technique. Based on this idea, we formulate the multicast resource allocation problem by incorporating with the side information. This problem can be transformed to the maximum coverage problem, and a greedy algorithm is designed that solve the problem efficiently with guaranteed approximation ratio. Simulation results show that our proposed schemes can provide substantial improvement under various conditions.

In this paper, we consider the emerging deployment of WiFi networks in sports and entertainment venues characterized by high-density, large capacity, and real-time service delivery. Due to extremely high user density, channel allocation and user association should be carefully managed so that cochannel inference can be mitigated. To this end, we propose a channel selection and user association (CSUA) solution based on the Adversarial Multi-armed Bandit (AMAB) framework, which captures not only the uncertainty of channel states, but also the selfishness of individual stations (STAs) and access points (APs). An exponentially weighted average strategy is adopted to design an online algorithm for this problem, which is guaranteed to converge to a set of correlated equilibria with vanishing regrets. Simulation results show the convergence of the proposed algorithm and its performance under different settings.

In this paper, we consider the problem of concurrent transmissions in a wireless network consisting of multiple radio access technologies (multi-RATs). That is, a single stream of packets are split to transmit over multiple RATs simultaneously so that the complementary advantages of different RATs can be exploited. One of the challenging issues is the packet out-of-order problem due to different wireless channel states and transmission scheduling policies over different RATs, leading to substantial performance degradation to delay sensitive applications. To address this problem, we adopt a M/G/1 queueing model to characterize the delay experienced by batch arrival packets in a RAT. A convex optimization problem is formulated. It attempts to find the optimal traffic splitting over multiple RATs such that maximum delay across different RATs is minimized. We derive the close-form solution for the problem under the special condition of two RATs, and the dual decomposition technique is adopted to solve the optimization problem for general cases. Numerical results are presented to show the performance of the proposed scheme and compare with existing solutions.

This study is focused on the multiple relay selection problem in cooperative communications. The objective is to select the minimum set of relays to minimise the spectrum cost while preserving the signal‐to‐noise ratio (SNR) requirement at the destination. A probabilistic constraint is adopted to characterise the SNR outage requirement, which turns out to be intractable in general. As a solution, a tractable bound is derived for the outage probability constraint, with which the multiple relay selection problem can be formulated as a mixed integer optimisation problem with the on‐and‐off power mode at the relays, which is NP‐hard. By utilising the intersections of the proposed bound for different relays, the authors propose an outage probability guaranteed relay selection algorithm which can find the optimal solution with significantly reduced complexity. A heuristic algorithm is also proposed for comparison. Extensive simulation results are provided to show the effectiveness of the proposed schemes.

In the conventional dual-hop cooperative communications, relays with imbalanced channel condition to the source or the destination may become the bottleneck of the overall cooperation. Therefore, in this paper, a Three-Stage Relaying (TSR) framework is proposed for clustered networks to extend the dual-hop cooperation to three stages by dividing relays into two groups. The long-haul communication between two groups form a virtual multi-input multi-output (MIMO) link with which the bottleneck between the relay and the source (or the destination) can be removed. We focus on the power allocation problem based on this framework, with the objective of minimizing the outage probability at the destination under the total power constraint. To address the computational complexity, the problem is decomposed into two subproblems, one deals with the power allocation of the source and the first-hop relays, the other deals with the power allocation of the second-hop relays. We design the algorithms for each subproblem by exploiting their special structure, and then develop a master procedure to handle the power allocation across these subproblems. The performance of the proposed scheme is evaluated through simulation study, which shows that the TSR framework achieves significant improvement on the outage probability compared with the dual-hop cooperation scheme, and the power consumption is more fairly distributed across relays.

Index coding is a new variant of source coding scheme that attempts to exploit the side information at different receivers to minimize the number transmissions to be broadcasted by the source, so that each receiver can derive its intended data with its local side information. However, existing relevant research are focused on the capacity analysis and algorithm design of index coding, while the transmission of index codes in the practical wireless system with erasure channel has not been considered. In this paper, we consider the problem of scheduling the transmissions of index code in multicarrier wireless systems, which is subject to frequency selective fading and frequency dependent attenuation across different receivers. This problem is nontrivial since different index codes can be constructed given a side information graph, which is further complicated by the diverse channel states at different receivers. To this end, we propose the minrk2and clique-based index coding schemes, and design the corresponding subcarrier assignment algorithm according to the property of the index codes. Simulation results are provided to show that the proposed schemes can provide significant performance gain under different conditions.

In the conventional dual-hop cooperative communications, the relays with imbalanced channels to the source and the destination become the bottleneck of the overall cooperation. In this paper, a Three-Stage Relaying (TSR) scheme is presented to extend the dual-hop cooperation to three stages by dividing relays into two groups. The relay-to-relay cooperation is introduced with which the bottleneck link between the relay and the source (or the destination) can be efficiently broken. We focus on the power allocation based on this framework, with the objective of minimizing the outage probability at the destination under the total power constraint. To address the computational complexity, the problem is decomposed into two subproblems, one deals with the power allocation of the source and the first-hop relays, the other deals with the power allocation of the second-hop relays. We design the algorithms for each subproblems by exploiting the special structure of the problems, and develop a master procedure to handle the power allocation across these subproblems. The performance of the proposed scheme is evaluated through simulation study, which shows that the TSR framework achieves significant improvement on the outage probability compared with the dual-hop protocol, and the power consumption is more balanced across relays.

Cooperative multicast is an effective solution for the bottleneck problem of single-hop broadcast in wireless networks. By incorporating with the random linear network coding technique, the previously proposed schemes can reduce the number of retransmissions significantly. However, these schemes may incur a large decoding delay at the receivers, In addition, the centralized scheduling methods of these schemes depend on the explicit feedback mechanism, which is not practical for a large size network. In this paper, we address the decoding delay and feedback storm problems in cooperative multicast. A cooperative multicast protocol named MWNCast is proposed based on a novel moving window network coding technique. Theoretical models are developed for analyzing the performance of the proposed scheme. Simulation results show that MWNCast outperforms the existing schemes by achieving better tradeoff between the throughput and decoding delay, meanwhile keeping the packet loss probability and decoding complexity at a very low level without explicit feedback.

In this paper, we study the relay selection problem for cooperative communications in aircraft approach. We propose to incorporate the airborne trace prediction scheme with the Rician fading channel model to provide a good estimation of channel condition for aircrafts with high speed mobility. Based on this model, the relay selection problem is formulated and transformed to a nonlinear integer programming (NLIP) problem, and two heuristic algorithms are proposed since this problem is NP-hard. We provide simulation results to show that the proposed scheme can guarantee the reliability of transmission with high probability.

In this paper, we study the relay selection problem in cooperative communications in aircraft approach, whereby aircrafts in the holding stack can be selected as relays to enhance the ground-to-air communications between the control tower and the approaching aircraft. We derive the upper bound of the outage probability in cooperative communications under three different channel conditions: Rayleigh, Rician and Nakagami-m. A relay selection algorithm OPRS is proposed accordingly that attempts to find the minimum number of relays to meet the outage probability requirement. We then consider the case that the bound of the channel condition is given but the distribution is unknown. In this case, the relay selection problem is formulated as a robust optimization problem. We provide simulation results to show the effectiveness of the proposed schemes.

Network coding techniques have absorbed much attention for providing reliable broadcasting services in wireless networks. However, the intrinsic tradeoff among throughput, decoding delay, and feedback overhead has obstructed the application of the previously proposed schemes in practice. In this paper, we firstly propose a rate-controlled network coding scheme (RANC), which can effectively reduce the decoding delay of the receiver suffering from a poor channel condition, without compromising the system throughput. Based on RANC, we further propose a moving window network coding scheme together with an early loss alarm mechanism (MWNC-ELA), which achieves similar decoding delay performance to RANC, but greatly simplifies its feedback mechanism. As a benchmark of MWNC-ELA, we analyze the decoding delay performance of RANC using the random walk theory. Simulation results show that the proposed schemes outperform the existing solutions in terms of throughput, decoding delay, and feedback overhead.

Topology engineering concerns with the problem of automatic determination of physical layer parameters to form a network with desired properties. In this paper, we investigate the joint power control, channel assignment, and radio interface selection for robust provisioning of link bandwidth in infrastructure multiradio multichannel wireless networks in presence of channel variability and external interference. To characterize the logical relationship between spatial contention constraints and transmit power, we formulate the joint power control and radio-channel assignment as a generalized disjunctive programming problem. The generalized Benders decomposition technique is applied for decomposing the radio-channel assignment (combinatorial constraints) and network resource allocation (continuous constraints) so that the problem can be solved efficiently. The proposed algorithm is guaranteed to converge to the optimal solution within a finite number of iterations. We have evaluated our scheme using traces collected from two wireless testbeds and simulation studies in Qualnet. Experiments show that the proposed algorithm is superior to existing schemes in providing larger interference margin, and reducing outage and packet loss probabilities.

In densely deployed IEEE 802.11 wireless networks, the transmission delay experienced by a user depends not only on the traffic load of the associated AP, but also the contention level of other APs operating on the same channel. However, due to the random distribution of users and inappropriate allocation of AP channels, the traffic loads of different APs are often uneven, leading to unfair delay experience to different users. In this paper, we consider the problem of channel assignment and user association for balancing the traffic load of APs operating on different channels, which is modeled as a non-cooperative game. We prove the existence of Nash equilibrium (NE) for this game, and derive the price of anarchy and the fairness index at NE. Simulation results are provided to compare the performance of the proposed algorithm with the theoretical bounds.

In this paper, we propose a link outage probability based resource allocation scheme for multi-radio multi-channel wireless mesh networks. The objective is to optimize the link outage probability under the effect of channel variation and external interference while preserving the end-to-end traffic demand requirements. The problem is formulated as a mixed-integer nonlinear programming problem, which is solved by decomposing it into a feasibility- checking problem and an outage probability search problem so that the original problem can be solved iteratively with reduced complexity. Numerical results are provided to show that the proposed algorithm is superior to the existing scheme in reducing the outage probability.

In the multi-cell IEEE 802.11 wireless networks, the traffic loads of access points(APs) are often uneven, which leads to inefficient use of network resources and unfair service to users. To alleviate such imbalance, different user association schemes have been proposed that use different metrics for measuring the congestion level of APs. In this paper, we propose a game theoretical model for the user association problem using the airtime cost as the congestion metric. The centralized and localized algorithms are designed for achieving the airtime- balancing Nash equilibrium. Simulation results show that the proposed algorithms outperform the existing scheme in terms of fairness and load balance.

In this paper, we consider the robust resource provisioning problem targeting for end-to-end rate guarantee under moderate channel variations and external interference. We formulate the robustness optimization problem that explicitly takes into account practical radio switching, co-channel contention, and multi-path routing and traffic splitting constraints. By exploiting the special property of the problem, we propose a scheme to decompose the problem into a feasibility-checking problem and an interference margin search problem, which is guaranteed to converge to the optimal value with reduced complexity. Using traces collected from an indoor wireless network testbed, we evaluate the performance of the proposed algorithm, and show that the algorithm is superior to existing scheme in providing larger interference margin and reducing packet loss probability.

In this paper, we investigate the problem of link-level resource provision in multi-radio multi-channel (MR-MC) wireless networks. To quantify robustness of resource provision schemes, we propose the novel concept of interference margin. Using the notion of interference margin, a robust radio and channel assignment problem is formulated that explicitly takes into consideration link-level traffic demands. The key advantage of the proposed formulation is its robustness to channel variability and co-existence of external interference sources. We utilize the generalized Benders decomposition techniques to decouple the radio and channel assignment (combinatorial constraints) and network resource allocation (continuous constraints) so that the problem can be solved efficiently. The proposed algorithm is guaranteed to converge to the optimal solution within a finite number of iterations. We have evaluated our scheme using traces collected from a wireless mesh testbed and simulation studies in Qualnet. Experiments show that the proposed algorithm is superior to existing schemes in providing larger interference margin, and reducing outage and packet loss probabilities.

With the growing number of spontaneously deployed WiFi hotspots and home networks, end-users often experience significant performance degradation or even starvation. However, we observe that tuning individual system parameters (channel, Tx power, carrier sense (CS) threshold, and transmit rate etc.) is insufficient and in some cases may lead to starvation. In this paper, we develop a comprehensive analytical model to characterize throughput of individual flows in dense IEEE 802.11 wireless community networks. The proposed model subsumes existing models for the IEEE 802.11 MAC in multihop wireless networks by accounting for heterogeneous transmission power levels and CS thresholds, as well as various sources of packet collisions. Based on the insight from the theoretical analysis and simulation results, we propose a simple identification mechanism that determines the sources of starvation using local measurements. Both the theoretical model and the identification algorithm are validated using ns-2 simulations.

In this paper, BFLY-a practical localized network coding protocol for wireless mesh networks-is proposed. To supplement forwarding packets in classical networks, intermediate wireless nodes code packets from different sources, so that each transmission's information content is increased. Prior work allowed intermediate nodes to code (i.e, XOR) packets such that the recipient of the coded message must decode the message before forwarding. BFLY, however, allows intermediate recipients to, in addition to XOR-ing, forward coded packets; and thus further exploits network coding opportunities in multihop wireless networks. BFLY utilizes knowledge of local topologies and source route information in packet headers. We have developed network coding modules in ns-2 that facilitate simulations with large networks. Simulation studies show that BFLY can increase overall network throughput by a factor of 1.2 - 2 and reduce end-to-end latency.

Sleeping scheduling is a common energy-conservation solution for sensor networks. For application whereby coordination of sleeping among sensors is not possible or inconvenient, random sleeping is the only option. In this article, we study the asynchronous random sleeping(ARS) scheme whereby sensors (i) do not need to synchronize with each other, and (ii) do not need to coordinate their sleeping schedules. The stationary coverage probability and the expected coverage periods for ARS are derived. For surveillance application, we derive in addition the detection probability and detection delay distribution. The correctness of our results is validated through extensive simulations. We compare ARS with other synchronous and asynchronous sleeping scheduling algorithms and show that ARS offers better performance in terms of detection delay in the lower duty-cycle regime. We also conduct simulations to demonstrate that our results can be a good approximation for clock drifting case.

Prefix-randomized query-tree (PRQT) protocol has been proposed for multiple tag identification in RFID systems. The optimal performance of PRQT can be achieved with a proper choice of the initial prefix length according to the tag set size. In this paper, we propose an initial prefix length adaptation algorithm for PRQT protocol when the tag set size is unknown before identification. The algorithm starts with the setting of a small initial prefix length l followed by the polling of all 2lprefixes. The initial prefix length is then increased repeatedly until the collision ratio satisfies a prescribed condition. We derive the optimal increment step size and the respective sequence of decision thresholds. Simulation results show that PRQT with initial prefix length adaptation can significantly reduce the expected tag read time for all range of tag set size when compared to the use of query-tree protocol.

In this paper we present a new tree search-based protocol for the anti-collision problem of RFID systems. This protocol builds a binary search tree according to the prefixes chosen randomly by tags rather than using their ID-based prefixes. Therefore, the tag identification time of the proposed protocol is no longer limited by the tag ID distribution and ID length as the conventional tree search protocol. The time complexity of the protocol is derived and shown that it can identify tags faster than the Query-Tree protocol.

The delay differentiated service was proposed as a DiffServ model to provide quality of service (QoS) guarantee on the Internet. In this model, packets are scheduled for transmission according to some specific delay metrics. waiting time priority (WTP) is one of this kind of scheduling algorithms that assign the priority to the packet according to its waiting time. WTP incurs implementation difficulty due to its computational complexity. In this paper, we propose a modified algorithm based on WTP called shifted waiting time priority (S-WTP). S-WTP reduces the computational complexity of WTP from O(n) to O(log(n)) without losing the basic functionality of WTP. Simulation results illustrate the effectiveness of S-WTP for delay differentiated service.