Multivariate Time Series (MTS) forecasting plays a vital role in various real-world applications, such as traffic management and predictive maintenance. Existing approaches typically model MTS data in either Euclidean or Riemannian space, limiting their ability to capture the diverse geometric structures and complex Spatio-Temporal (ST) dependencies inherent in real-world data. To overcome this limitation, we propose the Hybird Symmetric Positive-Definite Manifold Graph Neural Network (HSMGNN), a novel graph neural network-based model that captures data geometry within a hybrid Euclidean–Riemannian framework. To the best of our knowledge, this is the first work to leverage hybrid geometric representations for MTS forecasting, enabling expressive and comprehensive modeling of geometric properties. Specifically, we introduce a Submanifold-Cross-Segment (SCS) embedding to project input MTS into both Euclidean and Riemannian spaces, thereby capturing ST variations across distinct geometric domains. To alleviate the high computational cost of Riemannian distance, we further design an Adaptive-Distance-Bank (ADB) layer with a trainable memory mechanism. Finally, a Fusion Graph Convolutional Network (FGCN) is devised to integrate features from the dual spaces via a learnable fusion operator for accurate prediction. Experiments on three benchmark datasets demonstrate that HSMGNN achieves up to 13.8% improvement over state-of-the-art baselines in forecasting accuracy.

A major limitation of physical layer security is the requirement for prior knowledge of the potential eavesdroppers' (Eves) channels, which makes its practical implementation challenging. Integrated sensing and communication systems can be exploited to address this issue by enabling both communication and sensing functionalities, sensing the physical environment to estimate Eves' directions and amplitudes, and further reconstructing their channel state information (CSI) to achieve sensing-assisted wiretap channel awareness, thus realizing secure communication. We analyze a more practical scenario where not only is reconstructed Eves' CSI inaccurate, but also the CSI of the legitimate user equipments (UEs) estimated by the base station is imperfect, and Eves are equipped with multiple antennas. To resolve this, we propose a robust optimization problem that jointly maximizes the sensing accuracy and the secrecy rate by co-designing the beamforming and the artificial noise matrix. For the highly challenging non-convex infinite objective function, the S-Procedure is introduced, which is then solved by an efficient algorithm based on alternating optimization and successive convex approximation. Numerical results validate the effectiveness of the proposed algorithm and demonstrate that the robust beamforming scheme can mitigate the impact of channel uncertainty (both UEs' and Eves') on the system performance.

Multi-agent reinforcement learning (MARL), as a thriving field, explores how multiple agents independently make decisions in a shared dynamic environment. Due to environmental uncertainties, policies in MARL must remain robust to tackle the sim-to-real gap. We focus on robust two-player zero-sum Markov games (TZMGs) in offline settings, specifically on tabular robust TZMGs (RTZMGs). We propose a model-based algorithm (*RTZ-VI-LCB*) for offline RTZMGs, which is optimistic robust value iteration combined with a data-driven Bernstein-style penalty term for robust value estimation. By accounting for distribution shifts in the historical dataset, the proposed algorithm establishes near-optimal sample complexity guarantees under partial coverage and environmental uncertainty. An information-theoretic lower bound is developed to confirm the tightness of our algorithm's sample complexity, which is optimal regarding both state and action spaces. To the best of our knowledge, RTZ-VI-LCB is the first to attain this optimality, sets a new benchmark for offline RTZMGs, and is validated experimentally.

To meet the increasing demand for communication and computing resources in delay-sensitive and computation-intensive applications, resource-limited users should take advantage of the extensive computing resources at the network edge. A user-centric cell-free network can fully utilize the communication and computing resources of multiple access points (APs) to provide better load balancing among users. Parallel offloading for divisible tasks is expected to offer greater flexibility and further reduce latency. When data dependencies between different tasks has to be considered, the strict temporal relationship makes the task offloading problem even more challenging, which has been ignored in existing studies. In this paper, we propose a task-dependent parallel task offloading strategy for the user-centric cell-free network. The total time latency is minimized by joint optimizing AP selection, power control, task assignment and computational resource allocation. The formulated problem is then solved by an intelligent optimization algorithm based on gray wolf optimization algorithm (GWO). Simulation results show that in user-centric cell-free networks with limited computational resources, the proposed scheme can effectively reduce user latency.

Federated learning (FL) with synchronous model aggregation suffers from the straggler issue because of heterogeneous transmission and computation delays among different agents. In mobile wireless networks, this issue is exacerbated by time-varying network topology due to agent mobility. Although asynchronous FL can alleviate straggler issues, it still faces critical challenges in terms of algorithm design and convergence analysis because of dynamic information update delay (IU-Delay) and dynamic network topology. To tackle these challenges, we propose a decentralized FL framework based on gradient descent with momentum, named decentralized momentum federated learning (DMFL). We prove that DMFL is globally convergent on convex loss functions under the bounded time-varying IU-Delay, as long as the network topology is uniformly jointly strongly connected. Moreover, DMFL does not impose any restrictions on the data distribution over agents. Extensive experiments are conducted to verify DMFL’s performance superiority over the benchmarks and to reveal the effects of diverse parameters on the performance of the proposed algorithm.

Radio-based simultaneous localization and mapping (SLAM) facilitates unmanned systems to fulfill self-localization and navigation in complex environments. However, the existing studies overlooked communication between devices, which may lead to low efficiency and high costs when performing SLAM tasks. Reconfigurable intelligent surface (RIS) can satisfy the growing demands of users and improve SLAM accuracy in dynamic and complex environments. This paper proposes a RIS enabled simultaneous radio communication, localization, and mapping (SCLAM) system, where an unmanned aerial vehicle (UAV) can perform concurrent communication and SLAM with the help of RIS by utilizing communication signals from base station (BS). The proposed method enables the UAV to simultaneously accomplish SLAM and ensure communication with the BS, while also enhancing the spectrum utilization efficiency. We derive the Bayesian Fisher information matrix (BFIM) for joint SLAM and symbol detection of the proposed system, in which the trade-off between the BFIM of SLAM and the upper bound of the ergodic mutual information is illustrated. Then, a weighted factor based BFIM is presented to further achieve a performance trade-off between SLAM and communication. We formulate an optimization problem of joint BS active beamforming and RIS passive beamforming to maximize the log determinant of weighted BFIM. Numerical results verify the superiority of the proposed SCLAM system on position error bound (PEB), mapping error bound (MEB), and spectral efficiency (SE). The performance trade-off between communication and SLAM is also discussed and explored.

Physical layer (PHY) secret key generation (SKG) has been widely studied as a promising approach to achieving One-Time-Pad security. The improvement of SKG rate is quite a huge challenge, especially in scenarios with untrusted internal helpers or eavesdroppers that aim to wiretap the negotiated secret keys between legitimate parties. In this paper, we propose a token-based SKG scheme to deal with the problem of information leakage with internal eavesdropping attacks. The basic idea is to cover random pilots with protective tokens to confuse eavesdroppers. Three scenarios including passive external eavesdropping, active internal eavesdropping with a reconfigurable intelligent surface (RIS)-assisted untrusted helper, and active internal eavesdropping with an untrusted relay are considered and analyzed to evaluate the performance of the proposed anti-eavesdropping scheme. Theoretical analysis shows that the proposed token-based SKG scheme can perfectly secure the key negotiation, achieving zero information leakage even in the untrusted relaying scenario without a direct link between Alice and Bob. Moreover, closed-form expressions for secret key capacity (SKC) are obtained. Finally, numerical results indicate that the proposed scheme outperforms the state-of-the-art methods. Using a token-generation mapping function with greater diversity in amplitude and phase, our approach achieves enhanced SKC performance across various scenarios, including those with a passive eavesdropper, a RIS-assisted untrusted helper, and an untrusted relay.

To meet future ultra-low latency and extensive computing requirements, resource-constrained users should utilize ubiquitously available computing resources at the network edge. User-centric cell-free networks can effectively utilize the resources of multiple access points (APs) to serve users. However, the computing resources of a single AP are often limited due to deployment environment or cost, which makes the resource allocation problem more challenging. In this paper, we propose a parallel offloading scheme with limited AP computing resources under user-centric cell-free networks. Maximize the total users’ uplink rate and minimum delay margin by optimizing the users’ uplink transmission power and task allocation ratio. And we solve the non-convex optimization problem based on fractional programming (FP). Simulation results show that jointly scheduling the computing and communication resources of multiple APs can effectively alleviate problems such as task offloading failure caused by limited computing resources of a single AP.

Selecting access points (APs) and power control are crucial issues in resource allocation within cell-free massive multiple-input multiple-output (MIMO) networks. The aim of these challenges is to manage inter-user interference, lessen the fronthaul load, and ensure system performance is maintained as the number of users increases. Unlike previous works that address these issues separately, this paper introduces an integrated optimization approach for both AP selection and power control in a user-centric cell-free network, constrained by users’ minimum quality of service (QoS) to ensure system fairness. The problem at hand is a mixed-integer non-convex challenge, and we address it by utilizing fractional programming techniques for its transformation and solution. Simulations were carried out for environments, both indoors and outdoors, following 3GPP standards. The results indicate that the joint optimization approach greatly enhances the overall network sum spectral efficiency (SE) compared to solving these issues independently, emphasizing the proposed method’s effectiveness.

Reconfigurable intelligent surface (RIS) facilitates the extraction of unpredictable channel features for physical layer key generation (PKG), securing communications among legitimate users with symmetric keys. Previous works have demonstrated that channel reciprocity plays a crucial role in generating symmetric keys in PKG systems, whereas, in reality, reciprocity is greatly affected by hardware interference and RIS-based jamming attacks. This motivates us to propose LoCKey, a novel approach that aims to improve channel reciprocity by mitigating interferences and attacks with a loop-back compensation scheme, thus maximizing the secrecy performance of the PKG system. Specifically, our proposed LoCKey is capable of effectively compensating for the CSI non-reciprocity by the combination of transmit-back signal value and error minimization module. Firstly, we introduce the entire flowchart of LoCKey and provide an in-depth discussion of each step. Following that, we delve into a theoretical analysis of the performance optimizations when our LoCKey is applied for CSI reciprocity enhancement. Finally, we conduct experiments to verify the effectiveness of the proposed LoCKey in improving channel reciprocity under various interferences for RIS-assisted wireless communications. The results demonstrate a significant improvement in both the rate of key generation assisted by the RIS and the consistency of the generated keys, showing great potential for the practical deployment of our LoCKey in future wireless systems.

The thriving field of multi-agent reinforcement learning (MARL) studies how a group of interacting agents make decisions autonomously in a shared dynamic environment. Existing theoretical studies in this area suffer from at least two of the following obstacles: memory inefficiency, the heavy dependence of sample complexity on the long horizon and the large state space, the high computational complexity, non-Markov policy, non-Nash policy, and high burn-in cost. In this work, we take a step towards settling this problem by designing a model-free self-play algorithm \emph{Memory-Efficient Nash Q-Learning (ME-Nash-QL)} for two-player zero-sum Markov games, which is a specific setting of MARL. We prove that ME-Nash-QL can output an $\varepsilon$-approximate Nash policy with remarkable space complexity $O(SABH)$, sample complexity $\widetilde{O}(H^4SAB/\varepsilon^2)$, and computational complexity $O(T\mathrm{poly}(AB))$, where $S$ is the number of states, $\{A, B\}$ is the number of actions for the two players, $H$ is the horizon length, and $T$ is the number of samples. Notably, our approach outperforms in terms of space complexity compared to existing algorithms for tabular cases. It achieves the lowest computational complexity while preserving Markov policies, setting a new standard. Furthermore, our algorithm outputs a Nash policy and achieves the best sample complexity compared with the existing guarantee for long horizons, i.e. when $\min \\{ A, B \\} \ll H^2$. Our algorithm also achieves the best burn-in cost $O(SAB\,\mathrm{poly}(H))$, whereas previous algorithms need at least $O(S^3 AB\,\mathrm{poly}(H))$ to attain the same level of sample complexity with ours.

Physical layer security is of paramount importance in vehicular cyber-physical systems, as it safeguards not only the privacy of sensitive data exchanged between vehicles and infrastructure but also ensures the integrity and reliability of the entire transportation network. Key generation plays a crucial role in establishing secure communication channels and facilitating the creation of unique cryptographic keys used for encryption, decryption, and authentication purposes. The secret key generation involves deriving secret bits by harnessing the inherent randomness present within the communication channels. The difficulty lies in precisely evaluating the randomness of the channel to achieve unanimous agreement on secure key generation within an unpredictable environment. In this line, we propose a combinatorial approach involving A2C and LSTM to decrease the key disagreement rate. A2C employs policy and value-based strategies to choose quantization levels predicated on the randomness of wireless channels and LSTM includes the partially observable radio channels and improves the environment. Based on our performance evaluation, the proposed A2C-LSTM method substantially accelerates the convergence rate by $\mathbf{5 0 - 6 0 \%}$ and reduces the Key Disagreement Rate (KDR) by $40 \%$.

Distributed learning is a promising paradigm for future UAV (unmanned aerial vehicle) networks networks and other emerging autonomous unmanned systems. Such distributed learning framework can suit the intrisic decentralized topology of UAV networks, where the UAVs can collaborate to train a global AI model by only exchanging the model parmeters via its peer-to-peer (i.e, inter-UAV) links in a distributed manner. However, with the ever-increasing AI model sizes, the challenges arise from the significant communication overhead for exchanging massive model weights via inter-UAV links in an ad-hoc manner: Previous communication-efficient techniques are mainly designed for conventional federated learning and not easily extendable to the decentralized counterpart. We propose selective link orchestration to minimize communication overhead while ensuring convergence of distributed learning, and prove that the convergence constraint is equivalent to the connectivity of the selected sub-graph. As such, we can reformulate the problem as a link selection problem in graph theory and develop a distributed optimization algorithm based on the modification of the Gallager, Humblet, and Spira’s algorithm. Experimental results on MNIST, Fashion-MNIST, and CIFAR-10 datasets demonstrate up to a $90 \%$ reduction in communication overhead without compromising model accuracy.

Integrated sensing and communication (ISAC), as a promising component in 6G, however, is faced with security challenges that the critical communication information will be leaked to the sensing targets. This paper investigates an intelligent reflecting surface (IRS) aided bi-static ISAC system. The objective is to maximize the sensing signal-to-interference-and-noise ratio (SINR) with security and other constraints. A joint beamforming, reflection and receiving filter design is proposed. Alternating optimization (AO) based iterative algorithm leveraging semi-definite relaxation (SDR), Dinkelbach's transform, and successive convex approximation (SCA) techniques is utilized to solve the non-convex problem. It is found that the proposed scheme provides significantly better sensing performance with security constraint.

Reconfigurable intelligent surfaces (RIS), as new physical dimension transmission technology, can smartly adjust its reflection coefficients to achieve passive beamforming gains for signal enhancement and interference nulling. However, the instantaneous channel state information (CSI) is generally hard to obtain and involves huge training overhead. Besides, due to the passive and noncooperative manner of eavesdroppers, neither location nor CSI of the eavesdropping links can be perfectly known. Considering these practical restrictions, we propose a robust secure transmission strategy using only statistical CSI of the RIS assisted legitimate link with imperfect knowledge about the eavesdropper link. A bounded error model is established to characterize the uncertainty of eavesdroppers channel state information based on its potential location with uncertainty. The approximated ergodic secrecy rate is first derived in closed form, and then maximized by optimizing the active beamforming of the BS, the phase shifts of the RIS and the artificial noise (AN). To tackle this problem, a block coordinate descent (BCD) algorithm is proposed, and the original problem is decoupled into multiple subproblems, which are solved iteratively until convergence. Simulation results demonstrate that our proposed scheme can achieve approximately $59.1 \%$ secrecy gain compared to the benchmark schemes.

With the rapid development of Internet of Things (IoT) technologies, increasing number of IoT devices are connected to the wireless network. Random access (RA) is the necessary initial step in establishing a connection. However, the RA process is vulnerable to various attacks due to the openness of the wireless environment. Distributed denial of service (DDoS) is one of the leading attacks significantly violating the usability of wireless networks. Traditional security methods are set up after the RA process and thus can neither detect the RA-oriented attacks nor protect RA from being attacked. In this paper, we consider a typical RA-oriented DDoS attack in which a large number of preambles are illegally occupied to prevent the normal access of legitimate users. We propose a DDoS detection method based on multiple physical layer attributes. The time advance (TA) calculated from the preamble sequences and the carrier frequency offset (CFO) extracted from the preamble signals are jointly utilized to describe the characteristics of different accessing devices. A density-based spatial clustering of applications with noise (DBSCAN) based algorithm is proposed. Simulation results show that the proposed method can detect DDoS attacks with a false alarm rate of 0.6% and a miss detection rate of 0% within 1s. Compared with different benchmarks, the method shows better time efficiency and higher identification accuracy. It can reduce the access failure rate by 40% when the attack situation is relatively mild and can reduce the access failure rate by 61% when the attack situation is relatively serious.

By exploiting the entropy of wireless channels, physical layer key generation (PLKG) has gained considerable attention in recent years. Due to the characteristic of dynamic controlling of wireless channels, reconfigurable intelligent surface (RIS) can improve the key generation rate (KGR). Simultaneous transmission and reflection reconfigurable intelligent surface (STAR- RIS) can extend the half-space coverage to full-space coverage. This paper proposes a STAR-RIS-assisted physical layer key generation (SRKG) scheme in a multi-user case. The objective is to maximize the sum KGR by jointly designing the transmitting and reflecting coefficients (TARCs). We propose an iterative algorithm based on alternating optimization (AO), utilizing successive convex approximation (SCA) and semi-definite relaxation (SDR) methods, to solve the non-convex optimization problem in the presence of correlated channels. We also propose a low-complexity algorithm based on Lagrange multipliers to jointly optimize TARCs considering independent fading. Finally, the simulation results validate that, compared with the PLKG scheme assisted by traditional RIS (T-RIS), the proposed SRKG scheme can achieve higher sum KGR in a wider range under correlated and independent channel conditions.

Reconfigurable intelligent surface (RIS) is highly envisaged as a promising technology in millimeter wave (mmWave) communication systems for its capability of restructuring the wireless communication environment and mitigating the severe signal blockage. However, passive RIS beamforming design is still a challenge due to the non-convex property of the problem. This work presents a deep learning (DL) based codebook classification beam search algorithm, comprising a convolutional neural network (CNN) based codebook searcher and a mapper. The searcher determines the optimal codeword position range index based on the channel state information (CSI), while the mapper selects the optimal codeword based on this index. The proposed network is trained both with the DeepMIMO dataset and the Saleh-Valenzuela channel model, respectively, and imperfect CSI is utilized to enhance the system robustness. Simulation results demonstrate that the proposed algorithm significantly improves the beam search efficiency.

In this paper, we study the jamming aided physical layer security of the intelligent reflecting surface (IRS) assisted communication system in the presence of an eavesdropper. We aim to prove that the optimally designed jamming strategy is beneficial for the IRS assisted secure transmission. Specifically, we first derive the successful and secret transmission probability (SSP) in the closed form. Then we maximize the SSP by optimizing the jamming power allocation. When the number of the IRS elements or the total transmit power increases, less jamming power is required. It is also found that the jamming location and its corresponding optimal jamming power follow a certain quantitative relation, especially when the number of the IRS elements or the total transmit power is large. Finally, simulations are provided to validate our analytical derivations.

Space-air-ground integrated network (SAGIN) is a prominent architecture for the future wireless communication system. Due to the long distance transmission of the satellite communication and the limited battery capacity of aircrafts, the energy cost has become one of the dominant problems of SAGIN. Mobile edge computing (MEC) offers a potential solution by offloading partial tasks to nodes with higher computational capability. In this paper, we study a hybrid offloading problem where both unmanned aerial vehicles (UAVs) and ground users have tasks to be processed, and UAVs and the satellite can provide offloading service. The aim is to minimize the system energy consumption under time delay constraint by choosing the optimal offloading objects and jointly optimizing the offloading proportion and computing resource allocation. The optimization problem is highly nonconvex and difficult to solve optimally. To tackle the problem, we first derive the closed-form solution of computation resources allocation and further propose a low-complex algorithm based on successive convex approximation (SCA). Simulation results show that the proposed hybrid of-floading scheme can significantly reduce energy consumption compared to benchmark schemes. Moreover, by increasing the transmission power of users and UAVs in a certain range, total energy consumption can be effectively reduced. And increasing the number of UAVs can improve the energy efficiency.

In this paper, reconfigurable intelligent surface (RIS) is utilized to enhance the performance of the cooperative non-orthogonal multiple access (C-NOMA). The simultaneous wireless information and power transfer (SWIPT) technology is adopted at the cell-center user to relay information to the cell-edge user. We aim to maximize the data rate of the cell-edge user by jointly optimizing the beamforming vectors, the reflecting phase shifts, the power splitting (PS) ratio and the relaying power under the energy constraint and minimum data rate constraint of the cell-center user. To solve the established non-convex problem, an iterative algorithm based on alternate optimization is proposed to decompose the original problem into three sub-problems. In addition, successive convex approximation (SCA) and semi-definite relaxation (SDR) techniques are also employed to solve the sub-problems. Simulation results illustrate that the proposed RIS-assisted cooperative SWIPT NOMA scheme can effectively improve the performance of the cell-edge user compared with the traditional scheme without RIS.

Multi-access edge computing (MEC) architecture is emerging as a promising paradigm to improve the computing quality for mobile devices. Nevertheless, the offloading process of MEC encounters privacy and security problems. Independent protection for security and privacy will lead to different offloading policies which may contradict with each other. Besides, high unpredictability of channel information and the threaten of vicious edge nodes make it very challenging to offload securely. To address the problem about privacy and security, this paper proposes a Lyapunov-based privacy-aware secure offloading scheme. The scheme minimizes average energy consumption with known and unknown channel information of the eavesdropper. Lyapunov optimization is resorted to design an efficient online task offloading algorithm which can reduce the energy consumption under the strict security and privacy requirements. When compared to other schemes in the simulation, the proposed algorithm achieves fewer energy consumption and guarantees the security and the privacy in MEC offloading process.

Unmanned aerial vehicles (UAVs) are anticipated to be a potential data collection solution for wireless sensor networks (WSNs). The main challenges of integrating UAVs in WSNs are security threats and UAV’s onboard energy limitation. To cope with these two challenges, this article examines the secrecy energy efficiency (SEE) maximization problem in UAV-enabled WSN. Specifically, a full-duplex (FD) UAV gathers confidential information from ground sensor nodes (SNs) in the uplink while sending jamming signals to confound a ground eavesdropper (Eve) in the downlink. Considering a passive eavesdropping scenario lacking Eve’s instantaneous channel state information (CSI), the resulting problem is subject to the constraints of connection outage probability (COP), secrecy outage probability (SOP), securely collected bits, and flight trajectory. To tackle the intractable nonconvex problem, we first derive the optimal codeword rate and redundancy rate in closed-form expressions and then develop a low-complexity algorithm using the block coordinate descent (BCD) approach to alternatively optimize the SN scheduling, SN transmit power, UAV transmit power, and UAV trajectory. Simulation results verify the performance gains of the proposed scheme compared with the benchmark schemes. In particular, the proposed scheme achieves nearly the same secrecy rate gains at a lower UAV’s energy consumption cost than the sum secrecy rate maximization (SSRM) baseline. Moreover, it is revealed that trajectory optimization of the proposed scheme plays a crucial role in improving SEE performance compared with the circle trajectory (CT) baseline.

With the booming growth of Internet of Things (IoT), ensuring the secure access of the IoT terminals is paramount importance. Machine learning (ML) based physical layer authentication (PLA) has been proposed as a promising solution for preventing unauthorized access of terminals due to its robust security. However, suffered from limited battery resources, IoT terminals are difficult to bear the ML tasks, which restricts the generality of ML-based PLA. In this paper, we propose a collaborative PLA scheme based on horizontal federated learning (HFL) to release computational pressure on resource-constrained IoT terminals. Particularly, we model the ML-based PLA as a training issue of the classifier, in which the training task is to obtain the weight parameter of the neural network. Then, we design a distributed authentication framework to assign the ML task of authentication to the trusted collaborators. Finally, all the local parameters trained by collaborators are aggregated at the center IoT terminal to obtain a global classifier used for PLA. Simulations show that both miss detection rate and false alarm rate are less than 1%, confirming the effectiveness of the proposed PLA scheme.

In this paper, we investigate the time minimization in the downlink (DL) wireless powered transmission network (WPCN) consisting of one base station (BS) and two energy harvesting (EH) users. The hybrid non orthogonal multiple access (H-NOMA) and hybrid EH scheme are adopted, in which each user can harvest energy from both the dedicated energy signal and the information signal for the other user. We aim to minimize the total time consumption under the data rate and energy consumption constraints. To solve the formulated non-convex problem, we propose a two-layer iterative algorithm. The problem in the inner layer is transformed to convex and the problem in the outer layer focused on the power allocation coefficient can be solved by binary search. Simulation results show that H-NOMA can achieve the best performance compared with time-division multiple access (TDMA) and NOMA, and NOMA also outperforms TDMA under the zero circuit power and little decoding power.

Current physical layer authentication (PLA) mechanisms are mostly designed for static communications, and the accuracy degrades significantly when used in dynamic scenarios, where the network environments and wireless channels change frequently. To improve the authentication performance, it is necessary to update the hypothesis test models and parameters in time, which however brings high computational complexity and authentication delay. In this paper, we propose a lightweight cross-layer authentication scheme for dynamic communication scenarios. We use multiple characteristics based PLA to guarantee the reliability and accuracy of authentication, and propose an upper layer assisted method to ensure the performance stability. Specifically, upper layer authentication (ULA) helps to update the PLA models and parameters. By properly choosing the period of triggering ULA, a balance between complexity and performance can be easily obtained. Simulation results show that our scheme can achieve pretty good authentication performance with reduced complexity.

Non-orthogonal multiple access (NOMA) has been proposed to raise the spectral efficiency, which also brought new security threats. This letter studies the channel gain feedback falsification (CGFF) attacks of malicious users in NOMA. Since a slight falsification on channel gain feedback can still seriously damage the efficiency and security of NOMA, the detection of malicious users in NOMA is an important problem that is difficult to be solved by the traditional methods. Specific to the sensitivity of the eigenvalues to the sparse and slight anomaly, this letter analyzes the empirical spectral distribution (e.s.d.) of eigenvalues of channel gains feedback with and without CGFF attacks based on random matrix theory. On this basis, two lightweight detection schemes are proposed to detect CGFF attacks. The simulations prove the effectiveness of our proposed methods.

Pilot spoofing attack happens in the physical layer during the uplink pilot training stage. A malicious adversary sends identical pilot signals as a legitimate user, which can disrupt legitimate communications and lead to severe information leakage. In this paper, we propose a novel method using location verification to detect pilot spoofing attack. Specifically, we adopt several auxiliary devices in different locations to measure received signal strength (RSS), based on which, we then detect the spoofing attack by comparing the path-loss-based location and the algorithm-estimated location. Compared to the existing methods, our scheme does not need an uplink-downlink two-stage training, or alter the pilot structure. Simulation results show that our proposed method can provide excellent detection performance.

In this paper, we propose an area oriented authentication framework. We first derive the missing detection probability and successful spoofing rate of the attackers, and the false alarm probability and successful transmission rate of the legitimate nodes in closed forms. With these results, we can easily evaluate the risk of being attacked for legitimate nodes, and define three different areas: the no-risk area where the spoofers do not attack, the danger area where the spoofers are more likely to attack and can achieve a pretty good spoofing performance, and the warning area where the legitimate nodes should not get in. Moreover, we study the fuzzy area where it is hard to distinguish the legitimate nodes and attackers, and improved security measures should be added. These results provide useful insights for network operators. Simulations are finally given to verify our analytical results.

This paper studies the secrecy energy efficiency (SEE) maximization problem in a full-duplex (FD) bidirectional system, where an FD base station (BS) communicates with an FD user equipment (UE) with the existence of an external user who harvests energy from the ambient radio frequency (RF) signal with SWIPT and acts as a potential eavesdropper. To balance the secrecy rate and the energy efficiency, an SEE maximization problem is formulated subject to the minimum secrecy rate, the maximum transmitted power and the minimum harvested energy. The formulated non-convex problem is solved with a two-layer optimization algorithm, where Dinkelbach method is employed to deal with the fractional programming of the outer problem and a convex approximation method based on Taylor expansion is applied to transfer the inner problem into a convex iterative program. Numerical results demonstrate the effectiveness of the proposed algorithm and the advantages of FD operation in improving SEE.

In this paper, we study the secrecy performance in a hybrid wiretapping wireless system, where the half-duplex (HD) or full-duplex (FD) eavesdroppers may wiretap the confidential signal and/or transmit a jamming signal. To evaluate the secrecy performance, we derive the approximate closed-form results for the secrecy outage probability and mean secrecy rate by means of the random matrix theory (RMT). The RMT method can greatly simplify the complicated mathematical analysis with high accuracy, and can provide a useful analytical framework for other researches. The Monte Carlo simulations and numerical results are provided to validate the theoretical analysis and demonstrate the impacts of the system parameters. From the perspective of BS transmission, increasing BS transmission power can greatly improve the secrecy performance in the low transmit power region, but the secrecy performance is constant in the high BS transmit power region. Moreover, in terms of adversaries, FD eavesdroppers have better wiretapping performance than HD eavesdroppers when the jamming power is relatively low; otherwise, this result is reversed.

We consider the big data problems in the area of political polarization for USA biparty public view. In order to provide a mathematical insight, we model the big data structure as a zero mean random matrix with a deterministic perturbation matrix, analyze this model using random matrix theory (RMT), and simulate the real data to confirm this mathematical model. Then, we first propose an average capacity metric to numerically evaluate the polarization of two different data sources, namely US Democratic and Republic parties.With this metric, we derive the approximated capacity using the large dimension approach and free deconvolution approach in RMT. These two approaches show the same capacity changing trend that the Democrats and Republicans are now more ideologically divided than in the past.

Artificial noise (AN) assisted secure communication is a promising technique in the area of physical layer security. In this paper, we propose a new AN method to enhance the secrecy performance of a time reverse (TR) transmission system. Based on the proposed AN model, we first derive the closed-form secrecy rate, whose accuracy is verified through simulations. Then using it as an objective function, we further study the power allocation issue between useful information signals and the AN with the aid of maximizing the secrecy rate. The optimal power allocation is first derived in closed form and then analyzed in some special cases such as Rayleigh channel. Analytical and simulation results show that the secrecy performance can be considerably improved by over 10% even with a small amount of AN. And when there are less multi-paths in the channel or when the transmit power is small, more power for AN is required.

The wiretap channel with shared key (WTC-K) refers to the classic wiretap channel with a secret key of arbitrary rate shared between the transmitter and the legitimate receiver. While the secrecy capacity of WTC-K has been obtained using random coding arguments, in this paper, we propose a low-complexity polar coding scheme achieving the secrecy capacity under the strong secrecy condition. Our construction consists of choosing the sets of information indices and using the shared key properly. Specifically, we utilize the structure of superposition coding in the proposed scheme.

We consider a hybrid eavesdropping wireless system, where the locations of the eavesdroppers are drawn from Poisson point process (PPP). The eavesdroppers work in a half-duplex mode with a certain probability to transfer from eavesdropping mode to jamming mode. Based on the stochastic geometry (SG) and the random matrix theory (RMT), we derive the analytic results for the secrecy outage probability (SOP) and mean secrecy rate (MSR), which are verified by Monte-carlo simulations. We further derive the closed-form results for a special case where some special system parameters are assumed. It is found that the secrecy outage probability increases fast with the increasing of eavesdroppers' density. To improve the secrecy performance, the transmit power should be optimally designed.

In this paper, we investigate the performance of artificial noise assisted secure communication in the presence of a full-duplex active eavesdropper who can simultaneously perform eavesdropping and jamming. An approximate closed-form expression for the secrecy rate is derived. With this result, we obtain a new result for the optimal power allocation factor maximizing the secrecy rate, and analyze the impact of self-interference coefficient and jamming power on it. We further consider a more practical scenario where the legitimate user aims to maintain a given target data rate and uses all remaining power for artificial noise (AN) to interfere with the eavesdropper. While the eavesdropper tries to compel the transmitter to reduce the AN by sending jamming signals to the legitimate receiver. To solve this conflict, we introduce a power cost parameter to describe the impact of jamming on the eavesdropper itself and then formulate a game-theoretic framework. We also derive the closed-form equilibrium solutions for both sides. These results show the optimal jamming strategy of the eavesdropper and provide insights into the low bound secrecy performance. Finally, simulation results are provided to verify our analytical results.

As the future cellular networks tend to be randomly deployed and equipped with more antennas, we study the enhanced secrecy rate of multiple-input multiple-output (MIMO) systems with a stochastic geometry approach. To avoid intractable computational complexity and to have a deep insight into the security performance of such networks, we derive a closed-form lower bound on the ergodic secrecy rate, based on which the optimal power allocation parameter is derived. Analytic and Monte-Carlo simulation results show that the lower bound is quite tight. The density of base stations (BSs) and number of antennas both greatly infects the optimal power allocation parameter, e.g., positive secrecy rate may not achieved when the BSs density is small, otherwise the optimal power allocation will increase with the increase of the BSs density.

This paper studies the performance of a Massive MIMO system with pilot contamination in the multi-cell multiuser network. RCI (Regularized Channel Inversion) precoding is considered due to its good performance. Instead of using a large system method which assumes the number of antennas and users tends to infinity with a fixed ratio, we analyze the system performance in a more general situation where the number of transmit antennas M and number of users K are finite. We first derive the closed-form expression of the optimal RCI factoramp;#945;opt,and then study the impact of pilot contamination on the system performance. Result shows that more factors will be considered in finite situation. It is shown that our analysis is more accurate especially in small values of M and K. And our derived amp;#945;opt enables much better performance than the traditional large system result.

This paper investigates the secrecy rate maximization problem for the multiple-input-single-output multiple-antenna-eavesdropper (MISOME) wiretap channel with multiple randomly located jammers. The multi-antenna base station (BS) transmits information signals along with artificial noise (AN) to disturb the eavesdropper. Moreover, the friendly jammers are properly selected to assist the legitimate link for better secure transmission with some payoffs. With this system model, we first formulate a Stackelberg game between the BS and the assisting jammers with full channel state information. Stackelberg equilibriums, including optimal fraction of transmit power for AN, optimal transmit power, and asking prices of assisting jammers, are first proved to exist and then analytically derived. A policy iterative algorithm is also proposed to obtain the optimal solutions. We then extend the Stackelberg game to the case of MISOME broadcast wiretap channel with channel distribution information of eavesdropper. Numerical results verify the accuracy of the derived results and the efficiency of the proposed algorithm. The results reveal that the proposed jammer-assisted secure transmission can greatly improve the secrecy performance and meanwhile save more energy for information signals, which is significant for future wireless communication.

This paper investigates secrecy and connection performance for uplink transmission in a two-tier heterogeneous network with non-uniformly deployed low- power small base stations (BSs). All BSs and the eavesdropper are equipped with multiple antennas. We propose an aggregate interference approximation approach to characterize the statistics of interference generated by users associated with small BSs to facilitate our analysis. Then the secrecy outage probability and successful connection probability for a randomly located macro user are derived. In addition, we characterize them for a special case where macro BSs and eavesdropper are equipped with single antenna. Numerical results validate the effectiveness and accuracy of the aggregate interference approximation approach. Besides, the theoretical results fit well with the numerical results.

We consider the physical layer security for the downlink system, where the confidential messages can be wiretapped by both the intended users (IUs) and the external eavesdroppers (EEs). Regularized channel inversion (RCI) precoding is adopted for multiuser communication because it can control the mutual information leakage among users. And, to enhance the secrecy performance, artificial noise (AN) is introduced to disturb external eavesdroppers. Considering the large-system regime, by using stochastic geometry, the secrecy outage probability (SOP) and mean secrecy rate for the nearest EE wiretap and the strongest EE wiretap scenarios are derived, respectively. Analytical and simulation results show that i) when the AN is adopted, the SOP exponentially decays with the number of transmission antennas, ii) in most of the power allocation case, per-user secrecy rate can be improved significantly, e.g., there is an almost 2.7 times of improvement for the particular transmission antennas number.

This paper studies the physical layer security in a multi-user downlink, where a single user is selected for secret transmission during each time frame. Current works usually assume a worst case where all unselected users are curious and act as eavesdroppers, and conclude that no multiuser diversity is achievable for secrecy when the number of users is pretty large. However, the worst case may happen rarely in practice. A general scenario is that several (maybe all) of the unselected users act as eavesdroppers. In this case, selecting the user with the largest SNR (i.e., signal to noise ratio) does not necessarily achieve the maximum secrecy rate. For the general case, we derive the new closed-form expression of the secrecy outage probability, which increases with the number of curious users, and tends to converge in the high-SNR and large-user-number regime. When the number of curious users is supposed to be small, the secrecy outage probability could be any value smaller than one even in the large-user-number regime. These results provide additional insights into the system performance.

Wireless communication is particularly susceptible to eavesdropping due to its broadcast nature. Security and privacy issues have become increasingly critical for wireless networks. This paper considers the problem of secret communication in the multiuser downlink eavesdropped by a passive eavesdropper (EVE). The well-known zero-forcing preceding is adopted at the transmitter to produce concurrent data streams to the users, and at the same time an artificial noise (AN) is generated to prevent EVE from intercepting the information. We first derive an analytical closed-form expression for the secrecy sum-rate in the large system limit. We then use it as the objective function to optimize the power allocation (PA) between the information signals and the AN to maximize the secrecy sum-rate. A simple analytical expression of the optimal PA is derived in the high transmit power regime, which proves to be a near-optimal and generic strategy. The large system results are quite accurate for finite-size systems and thus can provide useful insights into system analysis and design. Our analytical and simulation results show that the AN assisted strategy achieves the same multiplexing gain as in the multiuser downlink without eavesdropping. Moreover, more power should be allocated to AN when the system serves fewer users and EVE has more antennas.

Cloud radio access network (C-RAN) is a new concept of network architecture, which brings a technical revolution into the wireless communication market and leads to some kind of all new mode of the future wireless communications. In this paper the clustering algorithm based on multi-objective optimization is investigated. The proposed algorithm aims at maximizing the throughput contribution of the Remote RF Head (RRH) to the whole system and minimizing its total power consumption with guaranteed energy efficiency of RRH. Using the novel greedy dynamic clustering algorithm, the joint capacity of RRHs is improved. The throughput of each RRH is first given using the pricing mechanism and the Pascoletti and Serafini Scalarization method is then implemented to solve the multiobjective optimization problem. Finally, the performance of the algorithm is assessed by the simulation results. It is shown that the novel dynamic clustering algorithm based on multiobjective optimization in the C-RAN architecture outperforms the traditional greedy clustering approach.

In this paper, the low information-exchange and robust precoding design for distributed antenna systems is investigated, which is of great importance for the new mobile network architecture namely cloud radio access networks (C-RAN). Relying on an interesting low complexity decomposition algorithm, a distributed robust linear minimum mean-square-error (LMMSE) precoding algorithm is proposed. Exploiting the elegant properties of the decomposition algorithm, the precoder design problem can be decomposed into several subproblems. In addition, all the subproblems can be performed in parallel term. Moreover for the proposed algorithm there is no need of an extra step to calculate the Lagrange multipliers at each iteration. Thus the exchanging information of the algorithm can be significantly reduced. Finally, it is demonstrated by the simulation that the proposed algorithm enjoys both satisfying convergence properties and better performance.

In this paper, the linear minimum mean square error beamformers are designed for relay-assisted cloud radio access network (C-RAN). In C-RAN, the remote radio units are separated from the baseband units to save energy cost. To further enhance network coverage, it is a good choice to duly arrange relay nodes. Regrading the per-antenna power constraints at both the relay nodes and the remote radio heads (RRHs), the beamformer matrices at the relay nodes and RRHs are jointly optimized for the relay assisted C-RAN. Since the considered problem is a non-convex optimization problem and is with multiple variables, it is in general very hard to solve. To make the design suitable for C-RAN exploiting the problem structure, two novel decomposition algorithms are proposed. One algorithm is mainly carried out at the RRHs, another is mainly performed at the relay node. Finally in the simulations, the performance of the proposed algorithms are demonstrated.

Consider secure transmissions over the downlink of a multiuser MISO system with independent confidential messages, each of which is intended for one of the users and should keep secret from others. We are interested in a scenario where users have individual secrecy rate requirements. This scenario is practical but has drawn little attention in the literature so far. In this paper, we propose a secrecy rate balancing algorithm which tries to fulfill the secrecy rate requirements of users, and maximizes the minimum margin which is defined as the difference between the available secrecy rate and the required rate for each user. This algorithm balances out all the secrecy rate margins until an equilibrium is reached. We derive a necessary condition for the optimal solution. Finally, theoretical results are illustrated by numerical simulations.

In this paper, the authors investigate the distributed power allocation for the multicell orthogonal frequency division multiple access networks by taking both the energy efficiency and the intercell interference (ICI) mitigation into account. A performance metric termed as throughput contribution is exploited to measure how the ICI is effectively coordinated. To achieve a distributed power allocation scheme for each base station (BS), the throughput contribution of each BS to the network is first given based on a pricing mechanism. Different from the existing works, a biobjective problem is formulated based on the multiobjective optimisation theory, which aims at maximising the throughput contribution of the BS to the network and minimising its total power consumption at the same time. By using the method of the Pascoletti and Serafini scalarisation, the relationship between the varying parameters and the minimal solutions is revealed. Furthermore, to exploit the relationship an algorithm is proposed based on which all the solutions on the boundary of the efficient set can be achieved by adaptively adjusting the involved parameters. With the obtained solution set, the decision maker has more choices in the power allocation schemes in terms of both the energy consumption and the throughput. Finally, the performance of the algorithm is assessed by the simulation results.

In this study, the authors investigate the linear minimum‐mean‐square‐error beamforming design for relay‐assisted cloud radio access network (C‐RAN). A standard C‐RAN architecture separates baseband processing units and wireless radio units in order to save energy cost. To further enhance network coverage, several relay nodes (RNs) are also deployed. Regrading the per‐antenna power constraints at both of the remote radio heads (RRHs) and the RNs in the author's work the beamforming matrices at the RRHs and RNs are ‘jointly’ optimised for the considered relay assisted C‐RAN. The considered optimisation problem is a non‐convex and multiple variable optimisation problem which is in general very hard to solve. In order to make the design suitable for large scale networks exploiting to the problem structure a novel two stage decomposition algorithms are proposed. Finally, a detailed mean‐square‐error performance comparison is given by the simulations.