Recognizing the coverage limitations of traditional reconfigurable intelligent surface (RIS), the simultaneously transmitting and reflecting RIS (STAR-RIS) with the ability to provide coverage on both sides of the surface, has been regarded as a promising technology especially for the integrated indooroutdoor communication environment. In this paper, we investigate a STAR-RIS assisted unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) system in the presence of imperfect channel state information (CSI). Specifically, the UAV serves as an aerial base station to provide communication services to outdoor users, as well as positioning for indoor users. To maximize the communication sum-rate while satisfying the sensing beampattern gain requirement, the UAV’s trajectory, beamforming, and the STAR-RIS transmission/reflection coefficients are jointly optimized. Meanwhile, the UAV’s flight safety, the maximum flight duration, and the minimum communication rate for each user are also considered. An online decision-making framework that utilizes deep reinforcement learning (DRL) is proposed to address the sum-rate maximization problem with imperfect CSI. Furthermore, in order to decouple the continuous optimization variables and improve the applicability of the system, we introduce a twin-twin-delayed deep deterministic policy gradient algorithm based on self-attention mechanism (SA-TTD3), which utilizes dual agents to decouple and optimize variables, and incorporates attention mechanisms to improve applicability. The numerical results indicate that the proposed SA-TTD3 algorithm significantly improves the performance of the ISAC system compared with the baseline schemes.

Unmanned aerial vehicle ad hoc networks (UANET) possess several advantages, such as distributed operation, self-organisation and long-range communication, leading to an exten-sive application in the field of Internet of Things (IoT). However, in practical UANET assisted IoT network scenarios, high degree of dynamism in UAV network topology, coupled with the complex and harsh conditions, adversely affects communication quality. Cluster networking is a well-established and highly effective technique for both organizing and managing UANET. The primary goal of cluster networking is to enhance the networks connectivity and stability by dividing nodes into clusters. This paper presents a novel weighted cluster networking mechanism, which aims to improve the performance of networks. The proposed mechanism involves two primary stages. In the initial networking stage, lowest identification number algorithm is employed to group the nodes. Subsequently, a weighted networking stage utilizes four metrics to enhance the clustering process. The optimal number of clusters is then determined by solving an optimization problem. Simulation results show that our proposed mechanism improves throughput, reduces delays and has better stability and scalability than traditional algorithms.

With the development of internet of things (IoT) technology and its wide application in urban traffic, the next-generation vehicle-to-everything (V2X) communication network should support high-capacity, ultra-reliable, and low-latency massive information exchange to provide unprecedentedly diverse user experiences. The development of the sixth-generation (6G) mobile communication technology will pave the way for realizing this vision. Reconfigurable intelligent surfaces (RISs), a critical 6G technology, is expected to make a big difference in V2X communications when used in conjunction with unmanned aerial vehicles (UAVs), allowing for extremely increased communication capacity and reduced latency. We propose a UAV-enhanced RIS-assisted V2X communication architecture (UR-V2X) suitable for urban three-dimensional (3D) IoT traffic and design an adapted MAC protocol UR-V2X-MAC to accomplish communication resource allocation and scheduling. The UAVs are used as access points and resource allocation centers, while the RISs are used as passive relays to assist V2X communication in proposed architecture. To improve the performance of UR-V2X-MAC, we use a distributed optimization algorithm in the message report phase of the protocol to maximize the system capacity by allocating the transmit power and alternately optimizing the RIS phase shift matrix. We analyze the delay and system capacity characteristics under different parameter settings through theoretical derivation and protocol performance simulation. Analysis and simulation results are presented to demonstrate that UR-V2X-MAC achieves a reduction in communication delay and a significant increase in system capacity through detailed design and alternate optimization compared to the existing V2X MAC protocol and no-RIS case.

Due to high flexible deployment and enhanced transmission capabilities, unmanned aerial vehicle (UAV) has attracted significant attention in recent years. UAV can serve as base station to provide communication coverage for emerging internet of things (IoT) in hot-spot areas. Stable topology plays an important role in improving connection and efficiency of UAV-IoT networks. However, when UAV-IoT nodes fail, network topology is destoryed and reconstruction becomes a formidable challenge, particularly in extremely harsh scenarios. Traditional topology reconstruction schemes predominantly rely on node movement to restore network connectivity, which neglect the performances of UAV-IoT networks. To remain stability and promote performances simultaneously, this paper proposes a distributed resource scheduling topology reconstruction (DRSTR) scheme, where both connectivity and throuphput of UAV nodes are jointly considered. Then, an optimization problem combining topology reconstruction and resource allocation is presented under the constraints of UAV-IoT nodes’ quality of service (QoS) requirements. Since the difficulty in solving the problem, the original problem is divided into three sub-optimal issues and an iterative approach is introduced to approximate the global optimal solution. Numerical results show that the proposed shceme achieves higher performances in UAV-IoT networks, compared to the traditional topology reconstruction shcemes.

Industrial Internet of Things (IIoT), regarded as an important technology for Industry 4.0, has the capability to connect massive IoT devices anywhere and at anytime in manufacturing industry. Enabling such a huge network requires message delivering among sensors, actuators, controllers, and the remote control to be seamless and reliable. However, IIoT wireless environment typically faces challenges such as blockage caused by IoT obstacles. To tackle the above issue, the unmanned aerial vehicle (UAV) and the reconfigurable intelligent surface (RIS) are exploited in this paper, which can provide favorable air-to-ground links and further rebuild the wireless channels. Moreover, the device-to-device (D2D) communication technique is introduced to enable direct information exchange between IoT devices. Specifically, we consider both the communication between the UAV and the cellular users (e.g., fixed IoT infrastructures) as well as the communication between D2D users (e.g., mobile IoT devices). Instead of only considering throughput, we focus on energy efficiency optimization for D2D users while guaranteeing the quality of service for cellular users, since energy-efficient transmission or green communication is important for IIoT scenarios. The transmit power, channel allocation parameters, and RIS’s reflection coefficients are jointly optimized to maximize energy efficiency for D2D users. To solve the formulated optimization problem, both centralized and distributed optimization algorithms based on deep neural networks are provided. Simulation results show that the introduction of RIS can significantly improve system performance. Moreover, the proposed algorithms can approximate the optimal solutions without the need of exhaustive search.

Heterogeneous cellular structure is a potential scheme in the sixth generation (6G) network, where macro and micro base stations (BSs) are collaborated to improve quality of service (QoS) for users. For the conventional heterogeneous cellular networks, data of users is transmitted at one frequency band and perfect channel state information (CSI) is required to beamforming. However, adjacent frequency interference and imperfect CSI decrease the spectrum efficiency (SE) of cellular network and QoS of users. In order to deal with performance loss of SE and QoS, this paper proposes a cognitive radio (CR)-based heterogeneous cellular networks, where opportunistic scheme is applied to improve QoS of users with imperfect CSI. Then, an optimization problem is established, considering both resource allocation and user selection under the constraints of users' QoS requirements. For the convenience of solving, we divide the optimization problem into two suboptimal issues and use an iterative method to approximate the global optimal solution. Numerical results reveal that the proposed scheme can achieve higher SE than the conventional scheme in heterogeneous cellular networks.

Opportunistic beamforming (OBF) is a promising multiple-input–multiple-output (MIMO) downlink precoding technique, where multiuser diversity gain is achieved to deal with the performance loss caused by low-complexity channel estimation. Due to high-performance and low-complexity channel estimation, OBF is appropriate for Internet of Things (IoT) networks. However, the conventional OBF cannot achieve satisfactory performance in post-disaster communication, and the low signal-to-noise ratio (SNR) makes it hard to establish communication between the base station (BS) and users. Therefore, we propose an unmanned aerial vehicle (UAV)-assisted OBF IoT network (UON) to satisfy the Quality-of-Service (QoS) requirements of users. Then, an optimization problem is formulated to maximize the weighted energy and spectrum efficiencies, while the optimization problem is nonconvex and hard to solve. To obtain the solution, we divide the optimization problem into three suboptimal issues, and then a joint iterative algorithm is applied. According to numerical results, the proposed scheme achieves higher system performances compared with the conventional OBF scheme in both Rayleigh and Rician channels. Moreover, we discuss the objective functions with different weights of spectrum efficiency (SE) and energy efficiency (EE) to adapt the QoS requirements of different scenarios. It is indicated that the proposed scheme can achieve high performance with low computational complexity, regardless of the weighted energy and SE.

In this paper, we investigate the covert communications in uplink nonorthogonal multiple access (NOMA) systems with the random power allocation and multi-carrier modulation scheme to confront a two-phase detector. In the NOMA system, a covert user (CU) and a reliable user (RU) transmit messages to Bob, in the presence of a warden (Willie) who tries to detect CU's transmission behavior. A two-phase detector, i.e., energy detection and similarity detection phases, is designed to improve the detection performance. In addition, we propose a random power allocation and multi-carrier modulation scheme to cover CU's transmissions. For the proposed scheme, the expected minimum detection error probability (EMDEP) and connection outage probabilities (COPs) at RU and CU are derived in closed-form expressions. The maximum expected covert rate (ECR) is analyzed under covertness and reliability constraints. Numerical results show that the proposed two-phase detector has a lower EMDEP, and the multi-carrier modulation scheme improves the covertness performance.

Stability and security are critical to modern wireless communication systems, which have attracted more and more attention in recent years. In this paper, physical layer security (PLS) between the transmitter and the legitimate user (LU) is greatly enhanced by inventing a robust directional modulation (RDM) scheme based on a relaxation phase constraint. Unlike the conventional perfect channel state information (CSI) assumption, the estimation error of LUs' azimuths is assumed, which corresponds to a more practical scenario. Moreover, the influence of the imperfect CSI on the design of artificial noise (AN) vector is investigated. Accordingly, a specific strategy to introduce AN at the transmitter is proposed with imperfect CSI. Numerical simulations suggested that the RDM scheme based on the relaxation phase constraint possesses great potential for the enhancement of security of wireless communications.

In recent years, the development of satellite communication is rapid, but the security problems become more significant. In this paper, from the perspective of improving the physical layer security, in the context of secure multicast transmission, we apply the sparse array directional modulation technology to improve the physical layer security. However, the inherent problem of sparse array is the existence of high sidelobe, which threatens the security of information transmission. Simulated annealing algorithm is used to optimize the selection of antenna subset, and some antenna combinations with high sidelobe are removed to obtain the optimized antenna subset codebook. When the distance between each ideal receiver and transmitter is different, the weighted simulated annealing algorithm method is introduced to allocate different transmission power to each receiver with different distance to improve the reception quality of each receiver. Finally, the simulation results verify the correctness of the above theory.

Full duplex radio acts as an important role in the future wireless networks since it can transmit and receive data simultaneously over the same channel. However, there are just a few medium access control (MAC) protocols designed for asymmetric full duplex radio networks, most of existed protocols are only suitable for fixed uplink and downlink nodes, which leads to a waste of channel resource. In this paper, we propose a novel MAC protocol for full duplex radio networks depend on the different transmission characteristics of uplink and downlink. To make an efficient use of channel and improve the network throughput, the proposed protocol is composed of two parts, uplink initiated link (UIL) based and downlink initiated (DIL) link based, respectively. In UIL model, access point (AP) can transmit data to different stations according to the UIL transmission slot design, vice versa in DIL model. The analysis and simulation results indicate that the proposed MAC protocol achieves throughput gains compared to existing protocol.

Wireless sensors network (WSN) is widely used in the Internet of Things at present. However, limited energy source is a critical problem in the improvement and practical applications of WSN, so it is necessary to improve the energy efficiency. As another important evaluation criterion of transmission performance, throughput should be improved too. To mitigate both of the problems at the same time, by taking the advantages of Time Division Multiple Access (TDMA) and Carrier Sense Multiple Access (CSMA) at the medium access control (MAC) layer of WSN, we propose a hybrid TDMA/CSMA MAC layer protocol. Meanwhile, we design a novel power control scheme to further reduce the energy consumption and optimize the transmission slots. The simulation results demonstrate that the proposed protocol significantly improves the throughput and energy efficiency.

Narrow-band interference (NBI) is a common jamming signal against synthetic aperture radar (SAR), in which the imaging quality can be degraded severely. To suppress NBI effectively, a novel interference suppression algorithm using robust principal component analysis (RPCA) based time-frequency signal separation is proposed. The RPCA algorithm is introduced for time-frequency signal separation for the first time. The experimental results of simulated and measured data demonstrate that the proposed algorithm not only can suppress interference effectively, but also preserve the useful information as much as possible.