Integrated sensing and communication (ISAC) has emerged as a key paradigm for next-generation wireless systems, which allows wireless resources to be used for data transmission and target sensing simultaneously. In this paper, multi-user collaborative target detection in the uplink ISAC system is investigated. To incorporate the target sensing functionality, the system relies on the reuse of uplink signals from the communication users. Specifically, we analyze an uplink multi-user single-input multiple-output (MU-SIMO) communication system with bistatic sensing. Using the channel statistics, we formulate the problem of joint optimal pilot and data power allocation to maximize the uplink ergodic sum rate while meeting communication and sensing quality-of-service (QoS) requirements. To address this non-convex problem, we propose an alternating optimization (AO)-based iterative framework, where the joint power allocation problem is decomposed into two sub-problems. Specifically, the pilot power allocation is optimized using a penalty dual decomposition (PDD)-based gradient ascent algorithm, while the data power allocation is solved via successive convex approximation (SCA). Once the long-term power allocation is determined, the base station (BS) estimates the instantaneous channels using a minimum mean-squared error (MMSE) estimator. Subsequently, based on the estimated instantaneous channel state information (CSI), the receive beamforming for communication users is optimized via another SCA-based method to maximize the sum rate. Meanwhile, the optimal receive beamforming for the target is obtained in closed-form through eigenvalue decomposition (EVD). We provide comprehensive simulation results to analyze the performance of the proposed iterative algorithm and to demonstrate its dependence on different design parameters. Our results also confirm the superiority of the proposed resource allocation approach over conventional benchmark schemes.

Rate-splitting multiple access (RSMA) is a key technology for next-generation multiple access systems due to its robustness against imperfect channel state information (CSI). This makes RSMA particularly suitable for high-mobility autonomous driving, where ultra-reliable and low-latency communication (URLLC) is essential. To address the stringent requirements, this study enables RSMA finite blocklength (FBL) transmissions and explicitly evaluates the ergodic performance. We derive the closed-form lower bound for the ergodic sum-rate of RSMA, considering vital factors such as the vehicle velocities, vehicle positions, power allocation of each stream, blocklengths, and block error rates (BLERs). To further enhance the ergodic sum-rate while complying with quality of service (QoS) rate constraints, we jointly optimize the global power coefficient, private power distribution, and common rate splitting. Guided by gradient descent, we first adjust the global power coefficient based on its sum-rate solution. This parameter regulates the power state of the common stream, allowing for dynamic activation or deactivation: if active, we optimize the private power distribution and adjust the common rate splitting to meet minimum transmission constraints; if inactive, we use the sequential quadratic programming for private power distribution optimization. Simulation results confirm that our RSMA scheme significantly improves the ergodic performance, reduces blocklength and BLER, surpassing the RSMA counterpart with average private power and space division multiple access (SDMA). Furthermore, our approach is validated to guarantee the rates for users with the poorest channel conditions, thereby enhancing fairness across the network.

Radio Frequency Fingerprinting Identification (RFFI) is a lightweight physical layer identity authentication technique. It identifies the radio frequency device by analyzing the signal feature differences caused by the inevitable minor hardware impairments. However, existing RFFI methods based on closed set recognition struggle to detect unknown unauthorized devices in open environments. Moreover, the feature interference among legitimate devices can further compromise identification accuracy. In this paper, we propose a joint radio frequency fingerprint prediction and siamese comparison (JRFFP-SC) framework for open set recognition. Specifically, we first employ a radio frequency fingerprint prediction network to predict the most probable category result. Then a detailed comparison among the test sample's features with registered samples is performed in a siamese network. The proposed JRFFP-SC framework eliminates inter-class interference and effectively addresses the challenges associated with open set identification. The simulation results show that our proposed JRFFP-SC framework can achieve excellent rogue device detection and generalization capability for classifying devices.

The importance of group communication in the context of the Internet of Things (IoT) is growing, yet the security and stability of this communication are facing significant challenges. The prevailing distributed group key management (GKM) schemes are ill-suited to resource-constrained devices. Furthermore, those that rely on servers are vulnerable to single-point failures and Byzantine risks. The distributed, immutable, and automatic execution of smart contracts on blockchains may offer a potential solution to these problems. This article puts forth a multilayer blockchain-based IoT GKM scheme with Byzantine fault tolerance (BFT). The scheme oversees the management of IoT device subgroups through the deployment of blockchain and smart contracts on edge servers while overseeing the entire device group in a hierarchical structure. A redundant selection mechanism based on hash mapping has been designed to guarantee reliable communication between disparate blockchains and devices. Concurrently, the scheme incorporates a server detection mechanism for Byzantine behavior, thereby ensuring the stability of the blockchain. The results of the experimental analysis demonstrate that the scheme exhibits enhanced security and fault tolerance.

This paper investigates the physical layer security (PLS) issue of unmanned aerial vehicle (UAV) aided multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) networks with randomly distributed passive eavesdroppers (Eves). Considering Nakagami-m fading, we propose a novel secure communication protocol that integrates transmit antenna selection (TAS) and maximum ratio combining (MRC) diversity technology. Specifically, to tackle the challenges of low spectrum efficiency and PLS performance, we propose two TAS solutions: TAS-max UN and TAS-max UF, the first one aims to enhance the performance of UN and the other one focuses on UF. To mitigate the impact of passive eavesdropping, a secure protected zone is established around the UAV to limit the Eve’s ability. Accordingly, we derive the closed-form expressions for the ergodic secrecy rate to evaluate the impact of spatial randomness. Then, the accuracy of the derived expressions is verified through Monte-Carlo simulations.To further support the theoretical analysis, asymptotic expressions under the high-SNR regime are derived, which offer valuable insights into secrecy rate trends and model convergence. Moreover, we propose a three-dimensional UAV deployment optimization framework that adopts a hybrid approach combining grid-based evaluation and Genetic Algorithm refinement, which improves ESR performance while significantly reducing computational complexity. In addition, a Simulated Annealing based power allocation scheme is introduced to optimize the power coefficient aF, achieving enhanced secrecy rate with improved search efficiency and adaptability. Extensive simulation results confirm that the proposed TAS/MRC framework, together with the secure protected zone, consistently outperforms conventional OMA and MRT schemes in terms of secrecy rate and robustness. The impact of key system parameters, including power allocation, UAV altitude, antenna configuration, and Eve density, is also thoroughly analyzed.

In this work, we investigate the physical layer security (PLS) of ambient backscatter communication non-orthogonal multiple access (AmBC-NOMA) networks where non-colluding eavesdroppers (Eves) are randomly distributed. In the proposed system, a base station (BS) transmits a superimposed signal to a typical NOMA user pair, while a backscatter device (BD) simultaneously transmits its unique signal by reflecting and modulating the BS’s signal. Meanwhile, Eves passively attempt to wiretap the ongoing transmissions. Notably, the number and locations of Eves are unknown, posing a substantial security threat to the system. To address this challenge, the BS injects artificial noise (AN) to mislead the Eves, and a protected zone is employed to create an Eve-exclusion area around the BS. Theoretical expressions for outage probability (OP) and intercept probability (IP) are provided to evaluate the system’s reliability-security trade-off. Asymptotic behavior at high signal-to-noise ratio (SNR) is further explored, including the derivation of diversity orders for the OP. Numerical results validate the analytical findings through extensive simulations, demonstrating that both the AN injection and protected zone can effectively enhance PLS. Furthermore, analysis and insights of different key parameters, including transmit SNR, reflection efficiency at the BD, power allocation coefficient, power fraction allocated to desired signal, Eve-exclusion area radius, Eve distribution density, and backscattered AN cancellation efficiency, on OP and IP are also provided.

The ever-increasing requests of users in emergency communication scenarios lead to high data traffic and transmission delay, posing challenges for resource-limited space-air-ground integrated networks (SAGINs). To address this issue, this paper proposes a joint caching optimization and user selection (JCOUS) problem that leverages unmanned aerial vehicle (UAV) caching to maximize the residual energy of the satellite, considering the limited resources of UAVs. To address the complex time-coupling optimization problem with discrete variables, we propose a primal decomposition method to decouple the problem, and design an energy-efficient user selection algorithm with dynamic caching. Furthermore, to reduce computational complexity and cost, we consider a statistical scenario and maximize the statistical residual energy in the JCOUS problem. Simulation results verify that the proposed scheme can achieve a higher residual energy and fast optimization, thus realizing energy saving and quick decision making especially in large-scale computation-intensive SAGINs.

In this paper, we investigate a full-duplex (FD) cell-free massive multiple-input multiple-output (mMIMO) system. To cope with the cross-link interference, especially the interaccess point (inter-AP) interference, we formulate a sum spectral efficiency maximization problem by jointly optimizing the digital and analog beamforming at FD AP and digital precoder at the uplink user equipment. To solve it, we present a fully digital design first by utilizing successive convex approximations and majorization-minimization to obtain a closed-form solution in each iteration. After that, we exploit the Riemannian manifold to get a hybrid design, which is shown to approximate the fully digital one well. Simulation results show that the proposed design enhances the sum spectral efficiency as well as uplink performance effectively when compared to other benchmarks.

Edge intelligence is expected to revolutionize the industrial Internet of Things (IoT) by providing proximal intelligent services to massive low-cost IoT devices. However, integration of the two paradigms needs to simultaneously maximize the edge quality of training (QoT) and IoT quality of service (QoS) under time-varying co-channel interference, for which the existing edge or IoT resource allocation algorithms become ineffective, as they ignore the contradiction between learning performance and communication requirements. This paper proposes an edge intelligence industrial IoT (EI3) framework, which jointly maximizes QoT and QoS through a newly derived learning-communication balancing power allocation (LCBPA) formulation. An efficient algorithm is proposed to solve the non-convex and non-smooth LCBPA problem. Simulation results demonstrate that the proposed LCBPA scheme achieves superior performance compared to several benchmarks in terms of the desired learning accuracy and qualified transmission rate. It is also shown that EI3can adapt to new scenarios by flexibly adjusting the importance factor between learning and communication.

Full duplex (FD) radios have attracted extensive attention due to the co-time and co-frequency transceiving capability. However, the potential gain brought by FD radios is closely related to the management of self-interference (SI), which imposes high or even stringent requirements on SI cancellation (SIC) techniques. When the FD deployment evolves into next-generation mobile networking, the SI problem becomes more complicated, significantly limiting its potential gains. In this paper, we conceive a multi-cell FD networking scheme by deploying a reconfigurable intelligent surface (RIS) at the cell boundary to configure the radio environment proactively. To achieve the full potential of the system, we aim to maximize the sum rate (SR) of multiple cells by jointly optimizing the transmit precoding (TPC) matrices at FD base stations (BSs) and users, as well as the phase shift matrix at the RIS. Since the original problem is non-convex, we reformulate and decouple it into a pair of subproblems by utilizing the relationship between the SR and minimum mean square error (MMSE). The optimal solutions of TPC matrices are obtained in closed form, while both complex circle manifold (CCM) and successive convex approximation (SCA) based algorithms are developed to resolve the phase shift matrix suboptimally. Our simulation results show that introducing an RIS into an FD networking system not only improves the overall SR significantly but also enhances the cell edge performance prominently. More importantly, we validate that the RIS deployment with optimized phase shifts can reduce the requirement for SIC and the number of BS antennas, which further reduces the hardware cost and power consumption, especially with a sufficient number of reflecting elements. As a result, the utilization of an RIS enables the originally cumbersome FD networking system to become efficient and practical.

In this paper, we investigate the application of rate-splitting multiple access (RSMA) in a two-tier wireless backhaul heterogeneous network (HetNet), where a macro base station (MBS) simultaneously transmits wireless access signals to multiple macro-cell users (MCUs) and wireless backhaul signals to small base stations (SBSs) by leveraging RSMA. Furthermore, to explore the potential advantage of common streams in RSMA systems, we develop a “Hybrid RSMA” scheme in which the MBS only employs RSMA to encode the backhaul messages while each MCU’s message is directly encoded without rate-splitting. We formulate an optimization problem to maximize the system’s spectral efficiency (SE) by jointly considering transmit precoding and rate allocation at the MBS and SBSs. To solve the formulated non-convex problem, we first propose an iterative centralized algorithm based on successive convex approximation (SCA). Then, we further develop an efficient decentralized algorithm that can be executed in parallel at the MBS and each SBS based on the local channel state information with fewer signaling exchanges. Simulation results show that the application of RSMA can achieve higher SE over conventional non-orthogonal multiple access (NOMA) and space division multiple access (SDMA) under different network loads. Particularly, “Hybrid RSMA” has a greater performance improvement than “RSMA” in the underloaded system.

In this paper, we propose a multi-cell full-duplex (FD) networking scheme by deploying a reconfigurable intelligent surface (RIS) at the cell boundary to configure the radio environment proactively. We aim to maximize the sum rate (SR) of multiple cells by jointly optimizing the transmit precoding (TPC) matrices at FD base stations (BSs) and the phase shift matrix at RIS. Since the original problem is non-convex, we reformulate and decouple it into a pair of subproblems by utilizing the relationship between SR and minimum mean square error. The optimal solutions of TPC matrices are obtained in closed form, while a successive convex approximation-based algorithm is developed to resolve the phase shift matrix suboptimally. Simulation results show that introducing an RIS into the FD networking system can improve the overall SR significantly. More importantly, we validate that the RIS deployment with optimized phase shifts can reduce the requirement for self-interference cancellation (SIC) and the number of BS antennas effectively, especially with enough reflecting elements. As a result, the utilization of RIS enables the originally cumbersome FD networking system to become efficient and practical.

In this paper, we investigate the application of rate-splitting multiple access (RSMA) in a two-tier wireless backhaul heterogeneous network (HetNet), where a macro base station (MBS) simultaneously transmits wireless access signals to macro-cell users (MCUs) and wireless backhaul signals to small base stations (SBSs) by leveraging RSMA. In order to improve the system spectral efficiency (SE) while guaranteeing the quality-of-service (QoS) of each user, we formulate an optimization problem to maximize the sum SE by jointly considering transmit precoding and rate allocation at the MBS and SBSs. To solve the formulated non-convex problem, we propose an iterative algorithm based on successive convex approximation (SCA). Simulation results show that the application of RSMA can achieve higher SE over conventional non-orthogonal multiple access (NOMA) and space division multiple access (SDMA) under different network loads. In addition, RSMA has better flexibility than other benchmark schemes in meeting the increasing QoS requirements of users.

Terrestrial robots, i.e., unmanned ground vehicles (UGVs), and aerial robots, i.e., unmanned aerial vehicles (UAVs), operate in separate spaces. To exploit their complementary features (e.g., fields of views, communication links, computing capabilities), a promising paradigm termed integrated robotics network therefore emerges, which provides communications for cooperative UAVs-UGVs applications. However, how to efficiently deploy UAVs and schedule the UAVs-UGVs connections according to different UGV tasks become challenging. In this paper, we consider the sum-rate maximization problem, where UGVs plan their trajectories autonomously and are dynamically associated with UAVs according to their planned trajectories. Although this problem is a NP-hard mixed integer program, a fast polynomial time algorithm using alternating gradient descent and penalty-based binary relaxation, is devised. Simulation results demonstrate the effectiveness of the proposed algorithm.

To cope with the explosive traffic growth expected in next-generation wireless networks, it is necessary to design next-generation multiple access techniques that can provide higher spectral efficiency as well as larger-scale connectivity. As a promising candidate, power-domain non-orthogonal multiple access (NOMA) has been widely studied. In conventional power-domain NOMA, multiple users are multiplexed in the same time and frequency band with differentpresetpower levels, which, however, may limit the spectral efficiency under practical finite alphabet inputs. Inspired by the concept of spatial modulation, we propose to solve this problem by encoding extra information bits into the power levels, and exploiting different signal constellations to help the receiver distinguish between them. To convey this idea, termed power selection (PS)-NOMA, clearly, we consider a simple downlink two-user NOMA system with finite input constellations. Assuming maximum-likelihood detection, we derive closed-form approximate bit error rate (BER) expressions for both users. Moreover, the two-user achievable rate region is also characterized. Simulation results verify the analysis and show that the proposed PS-NOMA can outperform conventional NOMA in terms of BER and achievable rate.

In this paper, a multi-vehicle multi-task non-orthogonal multiple access (NOMA) assisted mobile edge computing (MEC) system with passive eavesdropping vehicles is investigated. To heighten the performances of edge vehicles, we propose a vehicle grouping pairing method, which utilizes vehicles near the MEC to assist edge vehicles as full-duplex relays. For promoting transmission security, we employ artificial noise to interrupt eavesdropping vehicles. Furthermore, we derive the approximate expression of the secrecy outage probability of the system. The combined optimization of vehicle task division, power allocation, and transmit beamforming is formulated to minimize the total delay of task completion for edge vehicles. Then, we design a power allocation and task scheduling algorithm based on the genetic algorithm to solve the mixed-integer non-linear programming problem. Numerical results demonstrate the superiority of our proposed scheme in terms of system security and transmission delay.

In this paper, we investigate a multi-vehicle multi-task non-orthogonal multiple access (NOMA) based mobile edge computing (MEC) system with passive eavesdropping vehicles. To enhance the performance of edge vehicles, we propose a vehicle grouping and pairing method (GPM) to employ the vehicle near to MEC acting as a full-duplex (FD) relay to assist edge vehicles. In order to improve the transmission security, artificial noise (AN) is used to interfere with eavesdropping vehicles. The approximate expression of secrecy outage probability of the system (SOPS) is derived in closed form. This paper aims to minimize the total delay of task completion of edge vehicles by jointly optimizing vehicle task division, power allocation and transmit beamforming. We design a power allocation and task scheduling algorithm relying on genetic algorithm (GA-PATS) to solve our formulated mixed-integer non-linear programming (MINP) problem. Numerical results demonstrate the superiority of our proposed scheme in the aspect of system security and transmission delay.

Nowadays, intelligent transportation industry is becoming a hot spot in Internet of vehicles (IoV). However, owing to the existence of numerous intelligent terminals, communication security becomes a pressing problem. On the other hand, pervasive edge computing (PEC), as a pivotal technology, can significantly improve the performance of the system compared to the traditional cloud computing. In this article, we propose a nonorthogonal multiple access (NOMA)-based PEC power allocation framework in IoV, aiming at minimizing the system latency in the presence of eavesdroppers. Besides, queuing models, imperfect channel state information, and vehicles' speeds are all considered. Since the formulated problem is complicated, we consider its lower bound and derive the suboptimal closed-form expressions of the power allocation coefficients. Furthermore, a Frank-and-Wold algorithm is proposed to achieve the optimum total power. Simulation results illustrate the superior performance of the proposed NOMA scheme.

Edge caching can effectively reduce backhaul burden at core network and increase quality-of-service at wireless edge nodes. However, the beneficial role of edge caching cannot be fully realized when the offloading link is in deep fade. Fortunately, the impairments induced by wireless propagation environments could be renovated by a reconfigurable intelligent surface (RIS). In this paper, a new RIS-aided edge caching system is proposed, where a network cost minimization problem is formulated to optimize content placement at cache units, active beamforming at base station and passive phase shifting at RIS. After decoupling the content placement subproblem with the hybrid beamforming design, we propose an alternating optimization algorithm to tackle the active beamforming and passive phase shifting. For active beamforming, we transfer the problem into a semidefinite programming (SDP) and prove that the optimal solution of SDP is always rank-one. For passive phase shifting, we introduce the block coordinate descent method to alternately optimize the auxiliary variables and the RIS phase shifts. Further, a conjugate gradient algorithm based on manifold optimization is proposed to deal with the non-convex unit-modulus constraints. Numerical results show that our RIS-aided edge caching design can effectively decrease the network cost by improving the quality of offloading links.

A probabilistically robust transmit beamforming problem is referred, when the wireless downlink (DL) communication is supported by a robust non-orthogonal transmission (NOT)-aided design. Realistic imperfect channel state information (CSI) is considered in the face of rapidly fluctuating vehicular wireless channels, when the road side unit (RSU) communicates with multiple vehicles. Our design objective is to keep the probability of each vehicle's signal-to-interference-plus-noise ratio (SINR) outage below a given threshold. Minimizing the outage probability presents a significant analytical and computational challenge, since it does not lend itself to tractable closed-form expressions. Assuming a Gaussian CSI uncertainty distribution, we provide an approximation method by resorting to the semidefinite relaxation (SDR) and then apply a convex restriction to the original SINR outage constraints. Furthermore, the infinite constraints are reformulated into linear matrix inequalities (LMIs) by exploiting the popular S-procedure. As a benefit, the reformulated program can be solved efficiently using off-the-shelf solvers. Computer simulations are performed for benchmarking our convex method both against the non-robust non-orthogonal as well as the classical orthogonal designs. The results show that our robust beamforming design offers excellent high-mobility performance.

This paper studies the application of non-orthogonal multiple access (NOMA) to a cooperative multicast system (termed CM-NOMA). In particular, the multicast subscribers are served as secondary users underlaying a primary user. To enhance the secondary user fairness and compensate for the primary user, a two-stage cooperative strategy is proposed by selecting a certain secondary user to perform NOMA transmission distributedly. The maximum ratio combining (MRC) is further employed to harvest spatial diversity. We explicitly formulate the primary outage probability and secondary ergodic throughput in closed form. Moreover, we derive the asymptotic expressions and accordingly investigate the power allocation optimization with a primary outage constraint to maximize the secondary throughput. Numerical results validate that the proposed scheme achieves superior secondary user fairness and primary outage performance as well as higher energy efficiency.

At the time of writing, vehicle-to-vehicle (V2V) communication is enjoying substantial research attention as a benefit of its compelling applications. However, the ever-increasing tele-traffic is expected to result in overcrowding of the available band. As a first resort, multiple input multiple output (MIMO) can be utilized to enhance the attainable bandwidth efficiency or link reliability. However, in hostile V2V wireless propagation environments, the achievable multiple-antenna gain is eroded by the channel correlation. As a promising MIMO technique, spatial modulation (SM) only activates a single transmit antenna (TA) in any symbol interval and, hence, completely avoids the inter-antenna interference, hence showing robustness against channel correlation. As a further powerful solution, non-orthogonal multiple access (NOMA) has been proposed for improving the bandwidth efficiency. Inspired by the robustness of SM against channel correlation and the benefits of NOMA, we intrinsically amalgamate them into NOMA-SM in order to deal with the deleterious effects of wireless V2V environments as well as to support improved bandwidth efficiency. Moreover, the bandwidth efficiency of NOMA-SM is further boosted with the aid of a massive TA configuration. Specifically, a spatio-temporally correlated Rician channel is considered for a V2V scenario. We investigate the bit error ratio performance of NOMA-SM via Monte Carlo simulations, where the impact of the Rician K-factor, spatial correlation of the antenna array, time-varying effect of the V2V channel, and the power allocation factor is discussed. Furthermore, we also analyze the capacity of NOMA-SM. By analyzing the capacity and deriving closed-form upper bounds on the capacity, a pair of power allocation optimization schemes are formulated. The optimal solutions are demonstrated to be achievable with the aid of our proposed algorithm. Again, instead of simply invoking a pair of popular techniques, we intrinsically amalgamate SM and NOMA to conceive a new system component exhibiting distinct benefits in the V2V scenarios considered.