Fluid antenna (FA) technology has recently emerged as an effective means of exploiting spatial diversity through position-domain reconfigurability. This paper investigates the integration of FA into over-the-air federated learning (OTA-FL) systems with the aim of improving aggregation reliability and user participation under realistic channel conditions. By dynamically selecting antenna positions, FA-equipped users can exploit additional spatial degrees of freedom to realize more favorable channel conditions, thereby increasing the probability of successful contribution to the OTA aggregation process in each communication round. We consider an uplink OTA-FL framework consisting of a single fixed-antenna access point and multiple FAenabled users operating over spatially correlated fading channels. Unlike existing studies that primarily rely on optimization-based designs or numerical evaluations, we develop a tractable analytical framework that enables a rigorous performance characterization of FA-assisted OTA-FL. In particular, closed-form expressions are derived for the aggregation error outage probability and the expected number of participating users per round. Spatial channel correlation across FA ports is modeled using a copula-based approach, where the Clayton copula is adopted to capture lower-tail dependence relevant to worst-case fading conditions. Numerical results validate the analytical findings and demonstrate that FA-assisted OTA-FL significantly outperforms conventional fixed-antenna schemes in terms of aggregation reliability and participation efficiency, while providing insights under practical system considerations.

A model-driven deep learning framework is proposed for channel estimation in Rydberg atomic quantum receivers (RAQRs) based on the measurement of holographic snapshots. Specifically, we develop a Transformer-based unrolling architecture, termed URformer, to solve the non-linear biased phase retrieval problem, which is derived by unrolling a stabilized variant of the expectation-maximization Gerchberg-Saxton (EM-GS) algorithm. Each layer of the proposed URformer incorporates three trainable modules: 1) a learnable filter network that replaces the fixed Bessel kernel in the classic EM-GS algorithm; 2) a trainable gating mechanism that adaptively combines classic updates to ensure training stability; and 3) an efficient channel Transformer module that learns to correct residual errors by capturing non-local channel dependencies. Numerical results demonstrate that the proposed URformer significantly outperforms classic iterative algorithms and conventional black-box neural networks with less pilot overhead.

This paper proposes a robust received signal strength (RSS)-based localization framework for non-cooperative unmanned aerial vehicles. Conventional RSS methods face three fundamental obstacles: susceptibility to heavy-tailed measurement noise, intractable non-convexity, and severe accuracy degradation when target transmission power is unknown. These vulnerabilities present critical security risks to emerging low-altitude economy networks. To overcome these limitations, we propose an integrated joint-estimation architecture. First, a cascaded preprocessing pipeline, combining Gaussian outlier suppression and statistical median weighting, is developed to mitigate multipath-induced biases and minimize variance. Second, an information-theoretic base station (BS) selection mechanism is designed to identify geometrically optimal BSs, thereby exponentially reducing computational overhead in both uniform and random deployment scenarios. Third, the power-unknown problem is reformulated via semidefinite programming, absorbing the unknown parameter into a higher-dimensional convex cone to guarantee global convergence without relying on initial guesses. Extensive Monte Carlo simulations demonstrate that under uniform BS deployment, our strategy achieves sub-10-meter accuracy (approximately 5 m root mean square error) using only 5 selected BSs in typical urban conditions with a path loss exponent of 3. Consequently, this approach delivers a highly accurate and computationally efficient solution for real-time target tracking in complex environments.

High throughput coherent optical transmitters are critical elements in future optical communication systems, but their performance is constrained by components nonlinearities. We propose a bidirectional recurrent neural network (RNN) based digital pre-distortion (DPD) method, trained using both direct learning (DL) and indirect learning (IL), to effectively mitigate nonlinear distortions. We simulate a 64 Gbaud 64-quadrature amplitude modulation (QAM) coherent optical transmitter under varying levels of nonlinearity and noise. We evaluate the proposed neural network (NN)-DPD against Volterra, look-up table (LUT)-3 and linear DPD solutions. Our results show that the NN-DPD trained via DL consistently outperforms other nonlinear DPD methods, achieving up to a 4.4 dB gain in effective signal-to-noise ratio (SNR) over linear DPD.

This paper presents a novel digital pre-distortion technique leveraging deep reinforcement learning (DRL) to address memoryless nonlinearities caused by nonideal electrical components in communication systems. The proposed DRL based method is shown to outperform traditional approaches—including Volterra series, look-up tables, and machine learning-based indirect learning algorithms—in terms of bit-error rate (BER) and effective signal-to-noise ratio (SNR) improvement. Additionally, the study demonstrates the superior robustness of DRL compared to direct learning architectures (DLA) under abrupt input power variations in power amplifiers. Notably, this work is the first to incorporate transfer learning into the DRL training process, effectively stabilizing performance across varying operating conditions.

This paper investigates an over-the-air federated learning (OTA-FL) system that employs fluid antennas (FAs) at an access point. The system enhances learning performance by leveraging the additional degrees of freedom provided by antenna mobility. We analyze the convergence of the OTA-FL system and derive the optimality gap to illustrate the influence of FAs on learning performance. With these results, we formulate a nonconvex optimization problem to minimize the optimality gap by jointly optimizing the positions of the FAs, the beamforming vector, and the transmit power allocation at each user. To address the dynamic environment, we cast this optimization problem as a Markov decision process and propose the recurrent deterministic policy gradient (RDPG) algorithm. Finally, extensive simulations show that the FA-assisted OTA-FL system outperforms systems with fixed-position antennas and that the RDPG algorithm surpasses the existing methods.

This paper proposes an innovative approach to improve over-the-air federated learning (OTA-FL) systems by integrating fluid antennas (FAs) at the access point. By exploiting the mobility of FAs, we aim to increase the correlation among the users’ channels, thereby improving the learning performance. We analyze the performance of over-the-air computation and the convergence behavior of the OTA-FL system, highlighting the benefits of FAs. Since the learning performance improves as more devices participate in the FL aggregation, we formulate a non-convex optimization problem that maximizes the number of selected users by jointly optimizing FA positions and the beamforming vector, coupled with a user selection policy subject to a mean-squared error constraint. To address environmental dynamics, we describe the problem as a Markov decision process and develop a long short-term memory (LSTM)-based algorithm for efficient decision-making. Simulation results demonstrate that the proposed FA-assisted OTA-FL framework significantly outperforms conventional setups, achieving higher user selection rates and improved learning performance compared to existing benchmarks.

Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) introduces revolutionary capabilities by reaching full space coverage for wireless signals, significantly enhancing the efficiency and reliability of Internet of Things (IoT) networks compared to traditional RIS. In this article, we propose a novel framework that leverages STAR-RIS into wirelessly powered federated learning (FL) networks with a multiantenna access point, aiming to minimize system latency. A multivariable nonconvex optimization problem is formulated to optimize phase shift vectors of STAR-RIS, beamforming matrices, time, power, and computation frequency for each user in all phases of FL. Block coordinate descent (BCD) over the combination of an 1-D search algorithm and interior point method is employed to optimize time, power, computation frequency, phase shift vectors of STAR-RIS, and active beamforming matrix in the uplink transmission phase, while semi-definite relaxation via BCD addresses phase shift vectors of STAR-RIS and beamforming matrices optimization in harvesting and downlink transmission phases. On this basis, the optimized downlink transmission time and power are derived. The convergence of the proposed algorithm and the superiority of its performance compared to benchmark schemes are validated through comprehensive simulations. Our findings indicate the potential of FL, multiantenna aggregation server, and STAR-RIS in ushering in a new era of intelligent and efficient IoT networks.

This work proposes a novel privacy-preserving cyberattack detection framework for blockchain-based Internet of Things (IoT) systems. In our approach, artificial intelligence (AI)-driven detection modules are strategically deployed at blockchain nodes (BNs) to identify real-time attacks, ensuring high accuracy and minimal delay. To achieve this efficiency, the model training is conducted by a cloud service provider (CSP). Accordingly, BNs send their data to the CSP for training, but to safeguard privacy, the data is encrypted using homomorphic encryption (HE) before transmission. This encryption method allows the CSP to perform computations directly on encrypted data without the need for decryption, preserving data privacy throughout the learning process. To handle the substantial volume of encrypted data, we introduce an innovative packing algorithm in a single-instruction-multiple-data (SIMD) manner, enabling efficient training on HE-encrypted data. Building on this, we develop a novel deep neural network training algorithm optimized for encrypted data. We further propose a privacy-preserving distributed learning approach based on the FedAvg algorithm, which parallelizes the training across multiple workers, significantly improving computation time. Upon completion, the CSP distributes the trained model to the BNs, enabling them to perform real-time, privacy-preserved detection. Our simulation results demonstrate that our proposed method can not only mitigate the training time but also achieve detection accuracy that is approximately identical to the approach without encryption, with a gap of around 0.01%. Additionally, our real implementations on various blockchain consensus algorithms and hardware configurations show that our proposed framework can also be effectively adapted to real-world systems.

With the massive deployment of Internet of Things (IoT) devices in sixth-generation networks, several critical challenges have emerged, such as large communication overhead, coverage limitations, and limited battery lifespan due to high energy consumption. Federated learning (FL), wireless power transfer (WPT), multi-antenna access point (AP), and reconfigurable intelligent surfaces (RIS) can mitigate these challenges by reducing the need for large data transmissions, enabling sustainable energy harvesting, and optimizing the propagation environment. Compared to conventional RIS, simultaneously transmitting and reflecting (STAR)-RIS not only extends coverage from half-space to full-space but also improves energy saving through appropriate mode selection. Motivated by the need for sustainable, low-latency, and energy-efficient communication in large-scale IoT networks, this paper investigates the efficient STAR-RIS mode in the uplink and downlink phases of a WPT-FL multi-antenna AP network with non-orthogonal multiple access to minimize energy consumption, a joint optimization that remains largely unexplored in existing works on RIS or STAR-RIS. We formulate a non-convex energy minimization problem for different STAR-RIS modes, i.e., energy splitting (ES) and time switching (TS), in both uplink and downlink transmission phases, where STAR-RIS phase shift vectors, beamforming matrices, time and power for harvesting, uplink transmission, and downlink transmission, local processing time, and computation frequency for each user are jointly optimized. To tackle the non-convexity, the problem is decoupled into two subproblems: the first subproblem optimizes STAR-RIS phase shift vectors and beamforming matrices across all WPT-FL phases using block coordinate descent over either semi-definite programming or Rayleigh quotient problems, while the second one allocates time, power, and computation frequency via the one-dimensional search algorithms or the bisection algorithm. Simulation results demonstrate that TS STAR-RIS in both uplink and downlink transmissions achieves the lowest energy consumption, outperforming ES and conventional RIS schemes due to its flexible phase shift adaptation and lower interference levels.

Despite its advantage of preserving data privacy, federated learning (FL) could suffer from the limited computation resources of the distributed clients particularly when they are connected by wireless networks. By imitating the distributed resources effectively, digital twin (DT) shows great potential in eliminating the straggler issue in FL. In this paper, we leverage DT in the FL framework over non-orthogonal multiple access (NOMA) network, where DT deployed at the server can assist FL training process. To minimize the total latency and energy consumption in the proposed system, we formulate a Stackelberg game by considering clients and the server as the leader and the follower, respectively. Specifically, the leader aims to minimize the energy consumption via the optimization of DT mapping data ratio and resource allocation, while the objective of the follower is to minimize the total latency during FL training by optimally allocating DT computation resource. The Stackelberg equilibrium is considered to obtain the optimal solutions. We first derive the closed-form solution for the follower-level problem and include it in the leader-level problem which is then solved through the deep reinforcement learning (DRL) method. Simulation results verify the superior performance of the proposed scheme.

This paper considers the scenario where multiple Internet-of-Things (IoT) devices collaborate to train a distributed model using federated learning. Wireless power transfer (WPT) is employed to address the issue of limited battery life of IoT devices, while non-orthogonal multiple access (NOMA) is utilized to facilitate data transmission. Besides, an intelligent reflecting surface (IRS) is applied to assist both energy transfer and data transmission. On this basis, a joint resource allocation problem is formulated to minimize the total energy consumption for the considered IRS-aided FL-WPT networks with NOMA. The non-convex problem is first solved by developing a combination of semi-definite programming relaxation (SDR) with a two-dimensional search algorithm. To lower the computational complexity, SDR with a bisection algorithm is further employed by exploiting the inherent structure of the formulated problem. Numerical results not only validate the equivalence of these two algorithms in performance but also unequivocally establish the superior efficiency of the proposed method over benchmark schemes in terms of energy consumption.

Intelligent reconfigurable surface (IRS) is an innovative and promising technology to achieve intelligent reconfigurable wireless environment, and thus, enables cost-effective and energy-efficient wireless communications. Due to the broadcasting nature of the wireless signals, the reflected signal in IRS-assisted wireless communications networks might suffer from eavesdropping. Thus, it is essential to tackle the secrecy aware problems in IRS-assisted wireless communications networks. In this article, we consider an aerial IRS (AIRS) assisted wireless relay network scenario, where IRS is mounted on the aerial platform. The artificial noise is added to interrupt the eavesdropping. A secrecy rate maximization problem is formulated subject to the total transmit power and reflecting phase shift constraints. To solve this problem, we first divide the secrecy maximization problem into three subproblems, i.e., transmit power allocation, AIRS trajectory design, and reflecting phase shift optimization. These three subproblems are solved alternately until convergence to maximize the secrecy rate. Especially, for the AIRS trajectory design and reflecting phase shift optimization, we employ the successive convex approximation (SCA) and positive semidefinite relaxation (SDR) technologies to convert the nonconvex optimization problems into convex problems, respectively. The intercept probability of the proposed optimal schemes is derived and the theoretical analyses show that the intercept probability can be reduced by increasing the numbers of IRS elements. Simulation results show that the joint optimization of transmit power, AIRS trajectory and reflecting phase shift can effectively improve the secrecy rate.

The integration of radar and communication systems in 6G networks has led to a significant challenge of spectrum congestion. To address this issue, we propose a deep learning-based method for efficient waveform-based signal classification. Our method is designed to handle large and impaired radar and communication signals, and is crucial for the implementation of resource-limited cognitive radio-enabled Internet-of-Things (CR-IoT) devices. We introduce WaveNet, a cost-efficient deep convolutional neural network that can aptly learn underlying radio features from time-frequency images transformed by a smooth pseudo Wigner-Ville distribution. WaveNet incorporates several innovative modules, including cost-efficient feature awareness, which integrates two well-designed structural blocks: grouped-of-kernel-wise residual connections and dual asymmetric channel attention. These enhancements significantly reduce network size without compromising classification accuracy. Based on various simulations experimented on an impaired signal dataset containing eight radar and communication waveform types, the results demonstrate the effectiveness and robustness of WaveNet, achieving an overall classification accuracy of 92.02%. Compared to the current state-of-the-art deep models, WaveNet has the lowest architectural complexity, with a network size five times smaller, while still outperforming them by approximately 0.5 – 1.69%. Consequently, WaveNet emerges as a valuable solution for waveform classification in integrated radar-communication 6G systems.

The Internet-of-Things (IoT) has greatly facilitated our daily lives. Nevertheless, how to achieve higher spectral efficiency, large-scale device access, and lower latency for the next-generation IoT is still a challenge. Inspired by this, a non-orthogonal multiple access (NOMA) assisted cognitive radio (CR) IoT network is proposed in this paper, where the communication between the indoor secondary transmitter and secondary receivers is performed in the presence of an eavesdropper and under the constraint of secondary transmit power. In particular, we introduce simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) into the secondary network to assist the secondary transmitter to communicate with its receivers in different rooms. To characterize the reliability and security of the proposed system, we derive analytical approximate expressions for the outage probabilitys (OPs) and intercept probabilitys (IPs) by using Gaussian-Chebyshev quadrature. With the aim of providing a deeper understanding, we also explore the impacts of transmission signal-to-noise ratios (SNRs), power allocation coefficient and the number of STAR-RIS elements on the system performance. Presented numerical results show that: 1) the OPs of near and far users gradually decrease with SNRs until floors appear at high SNR, and the floors of near user is always lower than that of far user; 2) IPs increasing with SNRs and near user is always less than far user, which proves that near user has better security; 3) under appropriate parameters, the trade-off between reliability and security of the considered system can be arisen.

This paper investigates the performance of physical layer security (PLS) in wireless communication systems, where a reconfigurable intelligent surfaces (RIS) is deployed between the transmitter and legitimate receiver to enhance the communication security. The Fisher-Snedecor F distribution is adopted to model the underlying fading channels, owing to its accuracy, tractability and generality. On this basis, this paper evaluates the performance of the proposed system by deriving the average secrecy capacity (ASC) and the secrecy outage probability (SOP) under correlated Fisher-Snedecor F channel coefficients. Furthermore, the asymptotic behavior of the ASC and SOP in the high signal-to-noise ratio (SNR) regime is examined. Analyzing the correlated scenario is crucial as it provides a detailed understanding of how interdependencies among channel coefficients impact the system’s security and overall performance, offering valuable insights into real-world communication scenarios. Finally, this paper verifies the analytical results through numerical illustrations, and demonstrates the effectiveness of employing RIS.

In this paper, we investigate the energy efficiency (EE) problem under reconfigurable intelligent surface (RIS)-aided secure cell-free networks, where multiple legitimate users and eavesdroppers (Eves) exist. We formulate a max-min security EE optimization problem by jointly designing the distributed active beamforming and artificial noise at base stations as well as the passive beamforming at RISs under practical constraints. To deal with it, we first divide the original optimization problem into two sub-ones, and then propose an iterative optimization algorithm to solve each sub-problem based on the fractional programming, constrained concave-convex procedure (CCCP) and semi-definite programming (SDP) techniques. After that, these two sub-problems are alternatively solved until convergence, and the final solutions are obtained. Next, we extend to the imperfect channel state information of the Eves’ links, and investigate the robust security EE beamforming optimization problem by bringing the outage probability constraints. Based on this, we first transform the uncertain outage probability constraints into the certain ones by the Bernstein-type inequality and sphere boundary techniques, and then propose an alternatively iterative algorithm to obtain the solutions of the original problem based on the S-procedure, successive convex approximation, CCCP, and SDP techniques. Finally, the simulation results are conducted to show the effectiveness of the proposed schemes.

To achieve low-cost, low energy consumption green Internet of Things (IoT) communication and meet 360oarea full-coverage, we propose a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted cognitive radio (CR)-non-orthogonal multiple access (NOMA) network. Specifically, the secondary transmitter serves as a relay of the primary network to forward the messages, and the secondary transmitter communicates with the user with the assistance of the STAR-RIS. To evaluate the performance of the considered network, we derive the outage probability (OP) for the users under the Nakagami-m fading channels. In addition, the asymptotic behavior at high signal-to-noise ratio (SNR) regions is analyzed. The following meaningful insights are obtained from the simulation experiments: 1) The OPs of users decrease continuously with the transmit power Ps, and increasing Psat high SNR is no longer effective for system reliability; 2) The increase in the components number of STAR-RIS has a positive impact on the reliability for the STAR-RIS assisted overlay CR-NOMA network and saturates after a certain value; 3) The scheme we considered has superior reliable performance by comparing with orthogonal multiple access.

In this paper, we investigate the energy efficiency (EE) problem in downlink reconfigurable intelligent surface (RIS)-aided secure cell-free networks. First, we formulate a max-min secure EE (SEE) optimization problem via jointly optimizing the distributed beamforming at base stations and phase shifts at RISs under the constraint of each base station transmit power. To deal with it, we divide the original optimization problem into two sub-ones and propose an alternative scheme. Specifically, we develop an iterative optimization algorithm to solve each sub-one based on the fractional programming, constrained convex-convex procedure and semi-definite programming techniques. After that, these two sub-ones are alternatively solved until convergence, and then the final solutions are obtained. Finally, the simulation results show the effectiveness of the proposed algorithm.

Federated learning (FL) has been envisioned as a promising distributed learning framework for next-generation wireless communication systems. FL introduces new challenges in system design, since users need to consider the local processing optimization in addition to traditional communication resources allocation. In this paper, we aim to address this challenge by considering a wireless-powered FL network with multiple users, where non-orthogonal multiple access (NOMA) is employed for uplink transmission. A latency minimization problem is formulated, requiring to jointly optimize the power and time allocation for all FL phases together with the local processing computation frequency at each user. An one-dimensional search algorithm (ODSA) is proposed to obtain the optimal solution for the formulated non-convex problem. Presented numerical results demonstrate that the proposed scheme outperforms its orthogonal counterpart.

Mobile-edge computing (MEC) and intelligent reflecting surface (IRS) are envisioned as two promising technologies that enable massive connectivity in the future Internet of Things (IoT) networks. MEC allows IoT devices (IDs) to offload their computation intensive tasks and, thus, can prolong their lifespan. In contrast, the IRS can enhance the channel condition between IDs and the access points (APs), which are co-located with the MEC server. Wireless power transfer technique enabling energy harvesting for IDs helps realizing sustainable IoT network. This article applies IRS in a multi-ID MEC system for better latency performance. We first propose a multiple access scheme with hybrid frequency-division and nonorthogonal access technologies and then design a timing protocol for the IDs. Based on the above design, we study the latency optimization problem with the joint optimization of power allocation, the IRS phase shift matrix, and uplink and downlink beamformer under maximum power constraint for the IDs and AP. To tackle the formulated multivariable nonconvex problem, we split the target problem into several subproblems and provide a near-optimal low-complexity ID clustering scheme. Afterward, we derive optimal solutions to these subproblems, and a low-complexity fast-convergence alternating algorithm is proposed to minimize the overall latency. Presented simulation results verify the convergence of the alternating algorithm, and its superiority over the benchmarks.

Nonorthogonal multiple access (NOMA) has been viewed as a potential candidate for the upcoming generation of wireless communication systems. Comparing to traditional orthogonal multiple access (OMA), multiplexing users in the same time-frequency resource block can increase the number of served users and improve the efficiency of the systems in terms of spectral efficiency. Nevertheless, from a security viewpoint, when multiple users are utilizing the same time-frequency resource, there may be concerns regarding keeping information confidential. In this context, physical layer security (PLS) has been introduced as a supplement of protection to conventional encryption techniques by making use of the random nature of wireless transmission media for ensuring communication secrecy. The recent years have seen significant interests in PLS being applied to NOMA networks. Numerous scenarios have been investigated to assess the security of NOMA systems, including when active and passive eavesdroppers are present, as well as when these systems, are combined with relay and reconfigurable intelligent surfaces (RISs). Additionally, the security of the ambient backscatter (AmB)-NOMA systems are other issues that have lately drawn a lot of attention. In this article, a thorough analysis of the PLS-assisted NOMA systems research state-of-the-art is presented. In this regard, we begin by outlining the foundations of NOMA and PLS, respectively. Following that, we discuss the PLS performances for NOMA systems in four categories depending on the type of the eavesdropper, the existence of relay, RIS, and AmB systems in different conditions. Finally, a thorough explanation of the most recent PLS-assisted NOMA systems is given.

The Internet of Things (IoT) is a major driving force for future sixth-generation (6G) wireless systems. With the emergence of various novel IoT applications, more data should be collected and transmitted. However, IoT devices are constrained by battery, transmit power, and processing capacity. Featured by line-of-sight communication links, favorable channels, and better coverage, aerial access networks have been proposed to facilitate data transmission from IoT devices. In parallel, by shifting the computing and storage resources from the cloud to the edge of the network, edge computing [e.g., fog and mobile-edge computing (MEC)] can better support various computing-intensive and low-latency IoT applications. The integration of aerial access networks and edge computing, so-called aerial computing, is anticipated to provide not only traditional communication services but also advanced services for the IoT on a global scale.

Ambient backscatter communication (AmBC) and non-orthogonal multiple access (NOMA) are two promising technologies for the future wireless communication networks owing to their high energy and spectral efficiencies. The AmBC-aided NOMA symbiotic radio is a promising technology because of possessing advantages of AmBC and NOMA. Nonetheless, when a number of devices with limited power and computation capability access to the AmBC-based NOMA symbiotic networks, communication security becomes a critical issue. In this paper, we investigate physical-layer authentication (PLA) to identify the users and prevent illegal access and malicious activities for AmBC-based NOMA symbiotic networks. Moreover, channel estimation errors are considered when calculating the probability of false alarm (PFA) and probability of detection (PD) of the far user and near user. To enhance the authentication performance, three PLA schemes for the considered networks are designed according to the multiplexing form of the authentication tags: i) PLA with shared authentication tag (PLA-SAT); ii) PLA with space division multiplexing authentication tags; iii) PLA with time-division multiplexing authentication tags. To characterize the proposed PLA schemes, we first derive the PFA and the PD of the considered AmBC-based NOMA symbiotic networks. Then, the covertness is studied in terms of outage probability and asymptotic behavior in the high signal-to-noise ratio regime. Extensive analytical and computer simulated results show that: i) The PLA-SAT scheme has better performance than the other two authentication schemes with the same threshold; ii) The outage performance of systems employing authentication schemes is worse than those without authentication; iii) There exists a trade-off between robustness and covertness.

Internet-of-Vehicle (IoV) enabled Maritime Transportation Systems (MTS) communication is anticipated to support ultra-reliable and low latency, diverse quality-of-service (QoS) and large-scale connectivities. To meet such stringent demands, a cognitive ambient backscatter non-orthogonal multiple access (C-AmBC-NOMA) IoV-MTS network is proposed. We explore the reliable and secure performance of the proposed C-AmBC-NOMA IoV-MTS network with in-phase and quadrature phase imbalance (IQI) at radio-frequency (RF) front-ends and the existence of an eavesdropper. In particular, the analytical expressions on the outage probability (OP) and intercept probability (IP) are obtained after a series of calculations. For a deeper understanding, we discuss the asymptotic behavior of OPs in the high signal-to-noise ratio (SNR) region, the diversity orders of OPs, and IPs in the high main-to-eavesdropper ratio (MER) regime. The results of Monte-Carlo simulation and a series of corresponding theoretical analysis show that: i) As the SNR approaches infinity, the OPs tend to be fixed non-negative values, indicating that the diversity orders of the OPs have error floors; ii) When the MER approaches infinity, the IPs of legitimate users decrease continuously, while the IP of backscatter device (BD) increases; iii) Compared with the system performance under ideal condition, the system performance is less reliable under IQI condition, but the security performance is enhanced; iv) By carefully selecting the system parameters, a trade-off can be achieved between reliability and security.

In this paper, we investigate the physical layer security in the reconfigurable intelligent surface (RIS)-aided cell-free networks. A maximum weighted sum secrecy rate problem is formulated by jointly optimizing the active beamforming (BF) at the base stations and passive BF at the RISs. To handle this non-trivial problem, we adopt the alternating optimization to decouple the original problem into two sub-ones, which are solved using the semidefinite relaxation and continuous convex approximation theory. To decrease the complexity for obtaining overall channel state information (CSI), we extend the proposed framework to the case that only requires part of the RIS’ CSI. This is achieved via deliberately discarding the RIS that has a small contribution to the user’s secrecy rate. Based on this, we formulate a mixed integer non-linear programming problem, and the linear conic relaxation is used to obtained the solutions. Meanwhile, we also study the system performance under the imperfect CSI. Finally, the simulation results show that the proposed schemes can obtain a higher secrecy rate than the existing ones.

Terahertz (THz) communication has been regarded as one promising technology to enhance the transmission capacity of future Internet-of-Things (IoT) users due to its ultrawide bandwidth. Nonetheless, one major obstacle that prevents the actual deployment of THz lies in its inherent huge attenuation. Intelligent reflecting surface (IRS) and multiple-input-multiple-output (MIMO) represent two effective solutions for compensating the large path loss in THz systems. In this article, we consider an IRS-aided multiuser THz MIMO system with orthogonal frequency-division multiple (OFDM) access, where the sparse radio frequency chain antenna structure is adopted for reducing the power consumption. The objective is to maximize the weighted sum rate via jointly optimizing the hybrid analog/digital beamforming at the base station (BS) and reflection matrix at the IRS. Since the analog beamforming and reflection matrix need to cater all users and subcarriers, it is difficult to directly solve the formulated problem, and thus, an alternatively iterative optimization algorithm is proposed. Specifically, the analog beamforming is designed by solving a MIMO capacity maximization problem, while the digital beamforming and reflection matrix optimization are both tackled using semidefinite relaxation (SDR) technique. Considering that obtaining perfect channel state information (CSI) is a challenging task in IRS-based systems, we further explore the case with the imperfect CSI for the channels from the IRS to users. Under this setup, we propose a robust beamforming and reflection matrix design scheme for the originally formulated nonconvex optimization problem. Finally, simulation results are presented to demonstrate the effectiveness of the proposed algorithms.

Non-orthogonal multiple access (NOMA) and ambient backscatter communication have been envisioned as two promising technologies for the Internet-of-things due to their high spectral efficiency and energy efficiency. Motivated by this fact, we consider an ambient backscatter NOMA system in the presence of a malicious eavesdropper. Under the realistic assumptions of residual hardware impairments (RHIs), channel estimation errors (CEEs) and imperfect successive interference cancellation (ipSIC), we investigate the physical layer security (PLS) of the ambient backscatter NOMA systems with emphasis on reliability and security. In order to further improve the security of the considered system, an artificial noise scheme is proposed where the radio frequency (RF) source acts as a jammer that transmits interference signals to the legitimate receivers and eavesdropper. On this basis, the analytical expressions for the outage probability (OP) and the intercept probability (IP) are derived. To gain more insights, the asymptotic analysis and corresponding diversity orders for the OP in the high signal-to-noise ratio (SNR) regime are carried out, and the asymptotic behaviors of the IP in the high main-to-eavesdropper ratio (MER) region are explored as well. Finally, the correctness of the theoretical analysis is verified by the Monte Carlo simulation results. These results show that compared with the non-ideal conditions, the reliability of the considered system is high under ideal conditions, but the security is low.

In this article, we consider an edge cache-assisted millimeter-wave cloud radio access network (C-RAN). Each remote radio head (RRH) in the C-RAN has a local cache, which can prefetch and store the files requested by the actuators. Multiple RRHs form a cluster to cooperatively serve the actuators, which acquire their required files either from the local caches or from the central processor via multicast fronthaul links. For such a scenario, we formulate a beamforming design problem to minimize the secure transmission delay under transmit power constraint of each RRH. Due to the difficulty of directly solving the formulated problem, we divide it into two independent ones: 1) minimizing the fronthaul transmission delay by jointly optimizing the transmit and receive beamforming and 2) minimizing the maximum access transmission delay by jointly designing cooperative beamforming among RRHs. An alternatively iterative algorithm is proposed to solve the first optimization problem. For the latter, we first design the analog beamforming based on the channel state information of the actuators. Then, with the aid of successive convex approximation and $S$ -procedure techniques, a semidefinite program (SDP) is formulated, and an iterative algorithm is proposed through SDP relaxation. Finally, the simulation results are provided to verify the performance of the proposed schemes.

In this paper, we propose using artificial noise (AN) and zero-forcing (ZF) beamforming to protect massive multiple-input multiple-output (MIMO) non- orthogonal multiple access (NOMA) networks. In particular, the ergodic legitimate, eavesdropping, and secrecy rates are derived while taking imperfect channel estimation into account. From the obtained ergodic rates, the effects of ZF precoder, AN, and the number of antennas at the base station are revealed. Results show that the ZF precoder can enhance secrecy performance when compared with maximum ratio transmission precoder. Furthermore, an optimization algorithm is proposed to maximize the sum secrecy rate of the proposed network, while guaranteeing a target secrecy rate at each user equipment. Numerical results verify the correctness of the analysis and the effectiveness of the proposed algorithm.

In this paper, we consider a mobile edge computing (MEC)network, that is wirelessly powered. Each user harvests wireless energy and follows a binary computation offloading policy, i.e., it either executes the task locally or offloads it to the MEC as a whole. For the offloading users, non-orthogonal multiple access (NOMA)is adopted for information transmission. We consider rate-adaptive computational tasks and aim at maximizing the sum computation rate of all users by jointly optimizing the individual computing mode selection (local computing or offloading), the time allocations for energy transfer and for information transmission, together with the local computing speed or the transmission power level. The major difficulty of the rate maximization problem lies in the combinatorial nature of the multiuser computing mode selection and its involved coupling with the time allocation. We also study the case where the offloading users adopt time division multiple access (TDMA)as a benchmark, and derive the optimal time sharing among the users. We show that the maximum achievable rate is the same for the TDMA and the NOMA system, and in the case of NOMA it is independent from the decoding order, which can be exploited to improve system fairness. To maximize the sum computation rate, for the mode selection we propose a greedy solution based on the wireless channel gains, combined with the optimal allocation of energy transfer time. Numerical results show that the proposed solution maximizes the computation rate in homogeneous networks, and binary offloading leads to significant gains. Moreover, NOMA increases the fairness of rate distribution among the users significantly, when compared with TDMA.

In this paper, energy efficient resource allocation is considered for an uplink hybrid system, where non-orthogonal multiple access is integrated into orthogonal multiple access (OMA). To ensure the quality of service for the users, a minimum rate requirement is predefined for each user. An energy efficiency (EE) maximization problem is formulated by jointly optimizing the user clustering, channel assignment, and power allocation (PA). To address this problem, a many-to-one bipartite graph is first constructed considering the users and resource blocks (RBs) as the two sets of nodes. Based on swap matching, a joint user-RB association and PA scheme is proposed, which converges within a limited number of iterations. Moreover, for the PA under a given user-RB association, a feasibility condition is first derived. If feasible, a low-complexity algorithm is proposed, which obtains optimal EE for any successive interference cancellation (SIC) order and an arbitrary number of users. In addition, for the special case of two users per cluster, analytical solutions are provided for the two orders in which SIC can be implemented. These solutions shed light on how the power is allocated for each user to maximize the EE. Numerical results are presented, which show that the proposed joint user-RB association and PA algorithm outperforms other hybrid multiple-access-based and OMA-based schemes.

In this paper, we investigate the energy-efficient resource allocation problem in an uplink non-orthogonal multiple access (NOMA) millimeter wave system, where the fully-connected-based sparse radio frequency chain antenna structure is applied at the base station (BS). To relieve the pilot overhead for channel estimation, we propose a codebook-based analog beam design scheme, which only requires to obtain the equivalent channel gain. On this basis, users belonging to the same analog beam are served via NOMA. Meanwhile, an advanced NOMA decoding scheme is proposed by exploiting the global information available at the BS. Under predefined minimum rate and maximum transmit power constraints for each user, we formulate a max-min user energy efficiency (EE) optimization problem by jointly optimizing the detection matrix at the BS and transmit power at the users. We first transform the original fractional objective function into a subtractive one. Then, we propose a two-loop iterative algorithm to solve the reformulated problem. Specifically, the inner loop updates the detection matrix and transmit power iteratively, while the outer loop adopts the bi-section method. Meanwhile, to decrease the complexity of the inner loop, we propose a zero-forcing (ZF)-based iterative algorithm, where the detection matrix is designed via the ZF technique. Finally, simulation results show that the proposed schemes obtain a better performance in terms of spectral efficiency and EE than the conventional schemes.

In this paper, energy efficient power allocation is considered for a multiuser uplink non-orthogonal multiple access (NOMA) system under quality of service (QoS) constraints. Due to the QoS requirements, the considered energy-efficiency (EE) maximization problem may be infeasible and it is first required to determine the feasibility conditions. Then in a feasible region, the considered problem is shown to be pseudo-concave and can be solved by employing Dinkelbach's algorithm. Moreover, for the two extreme cases with very low and high maximum transmit power constraints, analytical results are provided, which shed light on how power is allocated to each user to maximize the EE. Simulation results are presented, which show that with a low maximum transmit power constraint, NOMA may perform worse than the conventional orthogonal multiple access (OMA) in terms of EE. However, under a high maximum transmit power constraint, NOMA always outperforms OMA.

In this paper we evaluate the effect of increased wireless spectral efficiency on the performance of mobile edge computing. Specifically, we study the energy minimization of computation offloading for a multicarrier non-orthogonal multiple access (NOMA) assisted mobile edge computing (MEC) system. A joint radio-and-computational resource allocation problem is formulated, in which three different resources should be appropriately allocated, including subcarriers, transmission power and computational resources. The formulated resource allocation problem belongs to mixed integer nonlinear programming (MILNP) and is NP-hard. We propose therefore a heuristic solution consisting of two steps, NOMA clustering and subcarrier allocation, and joint computational resource and power allocation. Our numerical results show that NOMA based MEC significantly outperforms its OMA counterpart, especially in scenarios with strict delay limits, where both the transmission and the computational resources become scarce.

This paper addresses the power allocation challenge for the downlink transmission in non-orthogonal multiple access (NOMA) systems applying multiple input multiple output transceivers. We consider the case when users are paired to form NOMA clusters, and share a common power budget. We provide low complexity power allocation methods within the clusters and across the clusters, that, together, maximize the sum-rate of the network, while guaranteeing a minimum quality of service for the users with weak channel condition. We show that compared to equal power allocation for the clusters, the proposed power allocation scheme improves the system fairness significantly, without decreasing the aggregate performance.

In this paper, we consider the power allocation (PA) problem in cognitive radio networks (CRNs) employing nonorthogonal multiple access (NOMA) technique. Specifically, we aim to maximize the number of admitted secondary users (SUs) and their throughput, without violating the interference tolerance threshold of the primary users (PUs). This problem is divided into a two-phase PA process: a) maximizing the number of admitted SUs; b) maximizing the minimum throughput among the admitted SUs. To address the first phase, we apply a sequential and iterative PA algorithm, which fully exploits the characteristics of the NOMA-based system. Following this, the second phase is shown to be quasiconvex and is optimally solved via the bisection method. Furthermore, we prove the existence of a unique solution for the second phase and propose another PA algorithm, which is also optimal and significantly reduces the complexity in contrast with the bisection method. Simulation results verify the effectiveness of the proposed two-phase PA scheme.

In this paper, the performance of multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) is investigated, when multiple users are grouped into a cluster. The superiority of MIMO-NOMA over MIMO-OMA in terms of both sum channel capacity and ergodic sum capacity is proved analytically. Furthermore, it is demonstrated that the more users are admitted to a cluster, the lower is the achieved sum rate, which illustrates the tradeoff between the sum rate and maximum number of admitted users. On this basis, a user admission scheme is proposed, which is optimal in terms of both sum rate and the number of admitted users when the signal-to-interference-plus-noise ratio thresholds of the users are equal. When these thresholds are different, the proposed scheme still achieves good performance in balancing both criteria. Moreover, under certain conditions, it maximizes the number of admitted users. In addition, the complexity of the proposed scheme is linear in the number of users per cluster. Simulation results verify the superiority of MIMO-NOMA over MIMO-OMA in terms of both sum rate and user fairness, as well as the effectiveness of the proposed user admission scheme.

In this paper, the power allocation (PA) problem is investigated in cognitive radio networks (CRNs), employing non-orthogonal multiple access (NOMA) technique. In such a framework, the quality of service (QoS) requirements for both the primary users (PUs) and secondary users (SUs) should be met. We propose a novel PA algorithm, which fully exploits the characteristics of NOMA-based system. The QoS requirements for PUs are guaranteed through the setup of the overall power for SUs. In addition, it provides PA in a descending order, according to SUs' channel gains. It is validated that the proposed algorithm is optimal. To show its effectiveness, it is compared with one of the most efficient PA algorithms, namely fractional transmit power control (FTPC). The superiority of the proposed algorithm is thoroughly verified by simulation results. Additionally, the computational complexity of our proposed algorithm is only linear, i.e., O(N).

In this paper, the resource allocation problem is considered in the context of spectrum underlay in cognitive radio (CR) networks. In such a framework, the quality of service (QoS) requirements for both primary users (PUs) and secondary users (SUs) need to be satisfied. Admission control based on removal algorithms is used jointly with power control such that the QoS requirements of all admitted SUs are satisfied, while no excessive interference is caused to PUs. For the first time in the literature, we introduce the min-weight load-balancing problem based on weighted bipartite graphs, while combining it with power control and user removal. Simulation results show that the combined algorithm achieves improved results when compared with merely using power control and user removal algorithms.

With the fast development of high-speed trains (HST) globally, some related technical issues of HST communications should be addressed, e.g. time- variant (TV) radio channel modeling and channel estimation. In this paper, we propose a more generic TV channel model based on Qian Liu's research. Moreover, a generalized subspace-based channel estimation algorithm with the aid of an improved basic expansion model (BEM) is proposed for orthogonal frequency-division multiplexing (OFDM) systems in high-speed mobile environments. The proposed channel model is based on the sum-of- sinusoids (SoS) method and accounts for the fast fading characteristics of HST channels due to the terminal's movement. The statistical analysis shows that the proposed model can be considered as a more general channel for TV-HST scenarios. Additionally, to accurately estimate the TV-HST channels, the improved BEM is applied to reconstruct the model by using the subspace projection method. Finally, numerical simulation illustrates that the accuracy of our channel estimation algorithm.

In this paper, we perform 3-Dimensional (3D) clustering based on the Outdoor-to-Indoor (O2I) wideband 3D multiple-input-multiple-output (MIMO) channel measurement at 3.5 GHz. Clusters are identified by KPowerMeans algorithm. Based on analysis on clustering results, we modified the definition of Multiple component distance (MCD) to split the bounding of azimuth and elevation, which can obtain larger number of clusters and the clusters are more intra- compact and inter-separated. Then, Calinski-Harabasz (CH) and Davies-Bouldin (DB) indices are used to further validate the proposed MCD. Finally, intra cluster and inter cluster statistics are both provided, which provides insights in 3D MIMO channel modeling.