o address the critical need for stable communication and energy efficiency in dynamic unmanned aerial vehicle (UAV) scenarios,o address the critical need for stable communication and energy efficiency in dynamic unmanned aerial vehicle (UAV) scenarios,T a pinching-antenna system (PASS)-enabled UAV delivery framework is proposed, which exploits the capability of PASS to establish a strong line-of-sight link and reduce the free-space pathloss. Aiming at achieving a balance between communication performance and energy efficiency, we define an effective utility function, construct a utility maximization problem, and develop an iterative joint optimization algorithm for pinching antenna (PA) activation vector and UAV delivery sequencing (IJO-PADS). More specifically, to solve the highly coupled mixed-integer nonlinear programming problem of PA activation vector optimization, we propose a pair of algorithms: 1) Branch-and-Bound (BnB) algorithm for finding global optimum; 2) incremental search and local refinement (ISLR) algorithm for reducing computational complexity. With the optimized PA activation vector, we define the path weight between a pair of nodes, which accounts for communication rate reward and energy consumption penalty. To maximize sum pate weight, we propose a genetic algorithm and dynamic programming (GA-DP) hybrid optimization method to tackle the NP-hard problem of delivery sequence planning, where a GA performs global exploration to generate candidate solutions, while a DP performs local refinement to obtain elite solutions. Simulation results indicate that: i) the proposed IJO-PADS framework converges within a moderate number of iterations; ii) the proposed algorithms (BnB, ISLR, GA-DP) outperform several benchmarks, demonstrating the effectiveness of our designs for PA activation and delivery sequence planning; iii) PASS is superior to conventional MIMO systems, due to PASS’s flexible PA activation and low-attenuation waveguide transmission.

This article investigates the beam training design for pinching-antenna systems (PASS) in the near-field communication region, where single-waveguide-single-user (SWSU), single-waveguide-multi-user (SWMU) and multi-waveguide-multi-user (MWMU) scenarios are considered. For SWSU-PASS, we design a scalable codebook, based on which we propose a three-stage beam training (3SBT) scheme. Specifically, 1) firstly, the 3SBT scheme utilizes one activated pinching antenna to obtain a coarse one-dimensional location at the first stage; 2) secondly, it achieves further phase matching with an increased number of activated antennas at the second stage; 3) finally, it realizes precise beam alignment through an exhaustive search at the third stage. For SWMU-PASS, based on the scalable codebook design, we propose an improved 3SBT scheme to support non-orthogonal multiple access (NOMA) transmission. For MWMU-PASS, we first present a generalized expression of the received signal based on the partially-connected hybrid beamforming structure. Furthermore, we introduce an increased-dimensional scalable codebook design, based on which an increased-dimensional 3SBT scheme is proposed. Numerical results reveal that: i) the proposed beam training schemes can significantly reduce the training overhead compared to the two-dimensional exhaustive search, while maintaining the same training accuracy; ii) PASS yields better flexibility and improved performance compared to several benchmark schemes.

To facilitate low-latency and secure transmission in Internet of Everything, a secure short-packet transmission framework is proposed in an uplink non-orthogonal multiple access (NOMA)-based integrated sensing and communication (ISAC) system. A triple-functional base station is utilized, which simultaneously carries out the tasks of receiving short-packet messages, detecting potential eavesdroppers, and transmitting active jamming signals. The achievable secure short-packet transmission rate is developed to measure the security performance, where the practical cases of imperfect inter-functional and inter-device interference elimination are considered. To optimize the security performance by effectively leveraging sensing capabilities, a problem is formulated aiming at maximizing the secure short-packet transmission rate, while guaranteeing the sensing quality. A key challenge to solve this problem lies in the performance loss terms associated with decoding error and information leakage in short-packet transmission, which makes the optimization problem highly coupled and strictly non-convex. To tackle this challenge, an alternating optimization (AO)-based approach is devised to solve the formulated problem iteratively, where an approximation method is proposed to convert the secure short-packet transmission rate into a manageable form. The convergence and effectiveness of the proposed design are validated by simulation results, which reveal that i) the devised AO-based algorithm converges within a modest number of iteration times; ii) the proposed NOMA-ISAC-based security design outperforms other benchmark schemes, demonstrating the benefit of utilizing sensing capabilities to enhance security.

As a typical scenario for the 6th Generation mobile communication systems (6G), Hyper Reliable Low Latency Communication (HRLLC) is expected to ensure extremely low delay and high reliability, while supporting wireless transmission of large-scale massive data. However, existing communication networks face the dual challenges of inadequate performance metrics and limited network resources. Therefore, this paper proposes the Flexible Bit and Semantic on-demand Transmission (FBST) framework, including three key technologies: adaptive transmission mode decision, flexible transmission time interval scheduling, adjustable semantic compression ratio. The FBST framework could satisfy the strict QoS requirements of users and provide on-demand services for users. Based on the Stochastic Network Calculus (SNC) modeling method, we conduct precise delay analysis and provided a general expression for the delay violation probability of the α - κ - μ channel, which could be extended to various complex channels. In addition, the Knowledge-base Parameterized Deep Q-Network (KP-DQN) algorithm is proposed to solve the resource allocation issue, which is a mixed action space problem with complex calculations caused by SNC. Finally, the simulation results show that FBST framework could satisfy extremely strict delay and reliability requirements of users, and the KP-DQN algorithm improving operational efficiency by over 76.8%.

Semantic communication (SemCom) is regarded as a promising and revolutionary technology in 6G, aiming to transcend the constraints of “Shannon’s trap” by filtering out redundant information and extracting the core of effective data. Compared to traditional communication paradigms, SemCom offers several notable advantages, such as reducing the burden on data transmission, enhancing network management efficiency, and optimizing resource allocation. Numerous researchers have extensively explored SemCom from various perspectives, including network architecture, theoretical analysis, potential technologies, and future applications. However, as SemCom continues to evolve, a multitude of security and privacy concerns have arisen, posing threats to the confidentiality, integrity, and availability of SemCom systems. This paper presents a comprehensive survey of the technologies that can be utilized to secure SemCom. Firstly, we elaborate on the entire life cycle of SemCom, which includes the model training, model transfer, and semantic information transmission phases. Then, we identify the security and privacy issues that emerge during these three stages. Furthermore, we summarize the techniques available to mitigate these security and privacy threats, including data cleaning, robust learning, defensive strategies against backdoor attacks, adversarial training, differential privacy, cryptography, blockchain technology, model compression, and physical-layer security. Lastly, this paper outlines future research directions to guide researchers in related fields.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted uplink non-orthogonal multiple access (NOMA) framework for finite blocklength (FBL) transmission is proposed. Considering the different communication requirements of Internet of Things devices (IoTDs), a novel design to achieve high-rate and low-error is proposed. Two operating protocols for STAR-RIS are considered, namely energy splitting (ES) and mode switching (MS). 1) For STAR-RIS with ES, an alternating optimization (AO) algorithm is proposed to handle the highly-coupled mixed integer programming problem. More particularly, a low-complexity received-signal-strength-based device pairing scheme is proposed. Based on the given device pair, the closed-form solutions for the power allocation problem are obtained. The transmitting and reflecting coefficient optimization problem is solved by exploiting the successive convex approximation and semidefinite relaxation methods. 2) For STAR-RIS with MS, a double-layer penalty-based (DLPB) algorithm is proposed to tackle the newly introduced binary amplitude constraints. Numerical results reveal that: i) the proposed AO and DLPB algorithms can converge within a few iteration times; ii) the FBL transmission performance can be improved by employing the proposed STAR-RIS framework compared with conventional transmitting/reflecting-only RISs; iii) NOMA is capable of enhancing FBL rate while guaranteeing the reliability constraints compared with orthogonal multiple access.

Downlink reconfigurable intelligent surface (RIS)-assisted multi-input-multi-output (MIMO) systems are considered with far-field, near-field, and hybrid-far-near-field channels. According to the angular or distance information contained in the received signals, 1) a distance-based codebook is designed for near-field MIMO channels, based on which a hierarchical beam training scheme is proposed to reduce the training overhead; 2) a combined angular-distance codebook is designed for hybrid-far-near-field MIMO channels, based on which a two-stage beam training scheme is proposed to achieve alignment in the angular and distance domains separately. For maximizing the achievable rate while reducing the complexity, an alternating optimization algorithm is proposed to carry out the joint optimization iteratively. Specifically, the RIS coefficient matrix is optimized through the beam training process, the optimal combining matrix is obtained from the closed-form solution for the mean square error (MSE) minimization problem, and the active beamforming matrix is optimized by exploiting the relationship between the achievable rate and MSE. Numerical results reveal that: 1) the proposed beam training schemes achieve near-optimal performance with a significantly decreased training overhead; 2) compared to the angular-only far-field channel model, taking the additional distance information into consideration will effectively improve the achievable rate when carrying out beam design for near-field communications.

The integration of unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) is a promising solution to provide flexibility in deploying the networks while reconstructing the wireless propagation environment proactively and cost effectively. We propose a UAV-RIS-assisted finite blocklength transmission framework for machine-type communications (MTCs), where downlink nonorthogonal multiple access (NOMA)-based coordinated multipoint (CoMP) is considered to mitigate intercell-interference and improve cell-edge transmission performance. Considering the cell-edge performance, we aim to maximize the minimum achievable rate of cell-edge devices (CEDs) by jointly optimizing the base stations’ transmission power allocation ratio, subchannel-device matching scheme, RIS reflecting coefficient, and UAV trajectory. To solve the highly coupled nonconvex optimization problem, we propose a double-layer alternating optimization algorithm for maximizing the minimum rate (DLAO-MM) in an iterative manner. Specifically, in theinner layer, we first derive the closed-form solution of power allocation, the propose a low-complexity priority-based subchannel-device matching scheme, and finally solve the RIS phase optimization subproblem. In theouter layer, we propose a successive convex approximation (SCA)-based optimization algorithm for the UAV trajectory planning subproblem. The convergence and effectiveness of the proposed DLAO-MM scheme for UAV-RIS-aided CoMP transmission are evaluated by simulations, which show that: 1) the proposed DLAO-MM scheme is capable of improving the cell-edge performance compared to the benchmark schemes; 2) the combination of UAV and RIS improves the cell-edge performance compared with RIS deployed in a fixed location; and 3) adopting the NOMA scheme in the UAV-RIS-aided CoMP system achieves a higher minimum CED rate than orthogonal multiple access.

In this paper, we propose a reconfigurable intelligent surface (RIS) assisted secure finite blocklength transmission framework in machine-type communications (MTC) networks, where the integration of millimeter-wave (mmWave) communication and non-orthogonal multiple access (NOMA) technology is considered to alleviate the problem of insufficient spectrum resources caused by massive MTC devices (MTCDs). For improving the ability of anti-eavesdropping, we aim to maximize the achievable sum secrecy capacity (SC) by jointly optimize the MTCDs’ transmission power, RIS phase coefficient and receive beamforming design. To handle the nonconvexity of the proposed optimization problem, we decouple it into three sub-problems, where the first two are solved by successive convex approximation (SCA) method. A minimum mean squared error successive interference cancellation (MMSE-SIC) scheme is proposed to tackle the receive beamforming problem for uplink NOMA networks. Furthermore, an alternating optimization based joint power, phase, and beamforming allocation (AO-JPPBA) algorithm is developed to implement joint optimization. Simulation results show that: 1) the security performance of the proposed AO-JPPBA is improved by 612.26% than the baseline scheme; 2) the proposed MMSE-SIC beamforming scheme is more effective in improving sum-SC of uplink NOMA networks; 3) RIS’s location has an obvious impact on sum-SC when considering eavesdroppers with strong wiretapping ability.

Machine-type communication (MTC) is one of the main enabling technologies to support various applications with diverse quality of service (QoS) requirements. Finite blocklength transmission has great potential in meeting the strict delay requirements of delay-sensitive MTC devices (MTCDs), while also causing loss of network capacity due to the decoding error probability. Aiming at this problem, we introduce uplink non-orthogonal multiple access (NOMA) and buffer-aided relaying to assist the finite blocklength transmission with delay requirements for improving the achievable effective capacity (EC), which is defined as the maximum short-packet constant arrival rate under specific statistical QoS constraints. To solve the EC maximization problem, we derive the closed-form expression of time allocation coefficient. Then we establish a concave lower bound of EC using successive convex approximation (SCA) for power allocation of MTCDs, and formulate a non-cooperative game based distributed power allocation algorithm for relay. Furthermore, a joint time and power allocation (JTPA) algorithm is proposed to implement joint resource allocation. Simulation results show that under finite blocklength and statistical QoS constraints, adopting buffer-aided relaying can improve EC by 41.82% compared with no-buffer relaying. Moreover, the achievable EC of JTPA algorithm is only 3.12% lower than that of exhaustive search while reducing complexity.

Industrial 4.0 will be supported by Internet of Things (IIoT), which will bring profound revolutions to the industrial manufacturing. The fifth generation wireless communication system (5G) will be one of the key technologies to support IIoT. However, the connectivity-massive, computation-intensive and time-critical features of IIoT pose great challenges to the spectrum and computation resource in 5G IIoT networks. Non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) are regarded as promising paradigms to tackle these problems, called NOMA-based MEC. To enhance computing performance of MEC system, we consider that devices can also offload their computation tasks to some idle devices with rich computation resources through machine-to-machine (M2M) communication, called M2M-assisted NOMA-based MEC scheme. We formulate an optimization problem under tasks delay constraints to minimize the system energy consumption through sleep-scheduling and joint computation-communication resource allocation. Specifically, we propose a deep reinforcement learning (DRL) based sleep-scheduling scheme to arrange some idle devices to work at sleep-mode for saving energy while satisfies the system computation requirements. Furthermore, we design an iterative algorithm for the joint computation-communication resource allocation problem. Numerical results demonstrate our proposed scheme and algorithm achieve significantly reduction of system energy consumption, while satisfying network computation requirements.