In the context of future 6G networks, reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA) are emerging as pivotal technologies for enhancing signal quality and eliminating coverage blind spots. This paper addresses the issue of secure and resilient transmission in RISassisted NOMA systems. Specifically, the base station transmits private signals to multiple legitimate users while dealing with the threat of potential eavesdropping. To model this challenge, we optimize the beamforming vectors and the RIS phase-shift matrix to maximize the sum secrecy rate while satisfying the user quality of service (QoS) requirements and the power constraints of the base station. Since the problem involves high-dimensional variables and non-convex objective functions, it is difficult to be solved by traditional optimization methods. Therefore, the twin delayed deep deterministic policy gradient algorithm (TD3) based on deep reinforcement learning (DRL) is proposed in this paper to effectively address the complexity of the original problem. Numerical results show that the proposed scheme exhibits satisfactory performance in improving communication security, transmission efficiency, and resistance to channel errors.

The drastic growth of the low-altitude airspace (LAA) applications, such as over the horizon control, low-altitude logistic, and all-time supervision, produces strong demands for communication and sensing services toward LAA. Under the idea of integrated sensing and communication (ISAC), this work investigates the low-complexity/high-accuracy active sensing problem toward LAA using a communication information-bearing orthogonal frequency division multiplexing (OFDM) waveform. A general and structured fast/slow-time representation for the OFDM input-output relation is first derived, which can clearly show the effects of delay and Doppler frequency shift (or equivalently, range and radial velocity of target) on the received echo. Based on the fast/slow-time representation, an ambiguity function (AF)-assisted active sensing scheme for the OFDM-based ISAC system is proposed. The scheme consists of three modules, i.e., a coarse search and triangular AF matching module, a gradient-based fine tune module and a modified cell-averaging constant false alarm rate (CA-CFAR) target detection module, which work coordinately to realize joint target detection and high-accuracy target range/velocity estimation. Simulation results show that the proposed scheme can realize better detection reliability and range/velocity estimation accuracy with much less computations than the reference schemes.

Radio frequency fingerprint identification (RFFI) has become a crucial technology in physical layer authentication, and plays an important role in authenticating the identities of wireless communication devices in the Internet of Things (IoT). Although open-set recognition has been applied in RFFI tasks, these schemes still demand extensive RF signal samples. In this paper, few-shot open-set recognition is being dedicated to exploring in RFFI tasks. To surmount mentioned challenges, we propose meta-learning by gaussian prototype network (MLGPN) scheme to achieve the goal of few-shot open-set recognition. MLGPN adopts the Mahalanobis distance between the embedding feature and the gaussian prototype as its metric. With the introduction of open-set loss function, the proposed scheme shows excellent open-set recognition performance. It is worth mentioning that meta-learning not only satisfies the demands of few-shot scenarios, but also enables new devices to join and leave without the need for retraining. Experiments conducted based on real LoRa RF signals confirmed the excellent performance of our proposed scheme for few-shot open-set recognition which surpasses traditional prototypical network model by 5.3% of AUC and 6.7% of ACC under the 1-shot condition. Compared with other schemes, the proposed scheme also demonstrated significant advantages.

PHY-layer spoofing attack is a potential critical issue in wireless network communication security, which could lead to catastrophic consequences for critical mission and applications, especially in Industrial Internet of Things scenarios with enormous number of devices. In this paper, we propose a novel spoofing attack detection scheme exploiting Channel State Information (CSI) phase difference. Firstly, we establish a mapping between CSI phase difference and the location of wireless communication devices to achieve the goal of spoofing attack detection. Due to the stable property of CSI phase difference, we convert CSI phase difference into heatmaps for subsequent training of the neural network model. Then we propose Wasserstein generative adversarial network and Encoder (WGAN-Encoder) deep-learning-based model in the scheme. This model utilizes discriminator feature residual error and image reconstruction error to get anomaly score for spoofing attack detection. This model overcomes the limitations of traditional detection methods on prior knowledge the attacker’s real CSI under real communication scenarios. Finally, we carry out extensive experimental evaluations about the detection performance and robustness of the proposed scheme based on data collected in time-varying scenarios. The results have successfully demonstrated that the proposed scheme exhibits outstanding performance.

Orthogonal time frequency space (OTFS) modulation has been advocated as a promising waveform for achieving integrated sensing and communication (ISAC) due to its superiority in high-mobility adaptability and spectral efficiency. Despite its advantages, due to the complex input-output relation of the OTFS processing, the current OTFS based ISAC schemes require high computation complexity/latency to achieve satisfactory sensing accuracy. In this paper, a concisely fast/slow-time representation for the OTFS input-output relation is derived, which can clearly reveal the physical interactions between the OTFS signals and sensing channels. Under this basis, a novel delay-Doppler spectrum matching assisted active sensing framework is proposed for the OTFS based ISAC systems. The framework consists of three sequentially operated modules, i.e., a coarse searching and triangular spectrum matching module, a local fine tune and quadratic spectrum matching module, and a constant false alarm rate (CFAR) target detection module. The three modules work coordinately to realize joint target detection and high-accuracy target range/velocity estimation. Simulation results show that the proposed framework achieves better detection performance and range/velocity estimation accuracy with much less computations when compared with the reference schemes.

In this article, an intelligent reflecting surface (IRS)-assisted integrated satellite-unmanned aerial vehicle (UAV)-terrestrial (SUT) Internet of Things (IoT) network faced with a smart jammer under imperfect channel state information (CSI) conditions is considered. We propose a Stackelberg game model to describe the adversarial relationship between the satellite, UAV, and IRS and the jammer, which are modeled as the leader and the follower, respectively. For the follower subgame, the jammer aims to minimize the jamming power while guaranteeing that the jamming energy efficiency surpasses a certain threshold. Under this setup, the Angle of Arrival (AoA)-based discretization method is utilized to address the imperfect CSI issue. Then, the use-and-then-forget method and the Lagrangian function are employed to obtain a closed-form expression for the jammer’s power. Finally, a feasible jamming power solution is obtained by means of the Cauchy–Schwarz inequality. For the leader subgame, we aim to optimize the hybrid beamforming design of the satellite, UAV and IRS, with the goal of minimizing the total transmit power, while guaranteeing that the downlink received signal-to-interference-plus-noise ratio (SINR) surpasses the minimum communication threshold. We propose an alternating optimization scheme, in which the Cauchy–Schwarz inequality, the AoA-based discretization method, and nonsmooth penalty functions are employed to alternately obtain the optimal satellite beamforming vector, UAV beamforming vector and IRS phase matrix when the other variables are fixed. Through our analytical and numerical results, the proposed beamforming scheme achieves a reduction of 16.9% in average power consumption compared with other benchmark schemes when obtaining the same anti-jamming performance.

The utilization of the aerial base station (ABS) has been treated as a promising solution to the coverage, deployment and cost savings problems in the wireless networks. Different from the terrestrial networks, in the ABS-assisted air-to-ground network, the service positions of ABSs and user association should be jointly optimized, which is challenging due to the unknown propagation environment in the task area. In this article, a binary radio map (BRM) is constructed to grant the ABSs the location-specific channel knowledge within the entire task area. With the assistance of the BRM, a multiple ABS deployment and user association framework is proposed. The framework consists of an offline optimization stage during which the initial ABS deployment position and user association are jointly optimized based on the BRM, and an online refinement stage during which the ABS positions and user association are refined in real time according to both the observed Quality of Service (QoS) and virtual QoS emulated by the BRM. The performance of the proposed framework is examined in a complex urban scenario with mobile users. The results show that higher achievable rate performance and lower on-board energy consumption can be realized when compared with the reference schemes.

Due to the openness of the wireless propagation environment, wireless networks are highly susceptible to malicious jamming, which significantly impacts their legitimate communication performance. This study investigates a reconfigurable intelligent surface (RIS) assisted anti-jamming communication system. Specifically, the objective is to enhance the system’s anti-jamming performance by optimizing the transmitting power of the base station and the passive beamforming of the RIS. Taking into account the dynamic and unpredictable nature of a smart jammer, the problem of joint optimization of transmitting power and RIS reflection coefficients is modeled as a Markov decision process (MDP). To tackle the complex and coupled decision problem, we propose a learning framework based on the double deep Q-network (DDQN) to improve the system achievable rate and energy efficiency. Unlike most power-domain jamming mitigation methods that require information on the jamming power, the proposed DDQN algorithm is better able to adapt to dynamic and unknown environments without relying on the prior information about jamming power. Finally, simulation results demonstrate that the proposed algorithm outperforms multi-armed bandit (MAB) and deep Q-network (DQN) schemes in terms of the anti-jamming performance and energy efficiency.

In this article, we propose a novel localization and channel estimation integration framework for cell-free massive multiple-input–multiple-output (MIMO) Internet of Things (IoT) systems, in which position information supports accurate channel estimation and accurate channel information can, in turn, improve positioning accuracy. Under this integration framework, we propose a two-phase fingerprint-based localization method consisting of both initial and accurate localization phases and a coarse-location-based (CLB) pilot reassignment scheme. The coarse location information for pilot reassignment is obtained in the initial localization phase of the two-phase localization method, and the fingerprint information used in the accurate localization phase is extracted through channel estimation based on the CLB scheme. Furthermore, for localization, two different fingerprint similarity criteria are proposed to meet the requirements of the different localization phases. Simulation results demonstrate that our proposed two-phase fingerprint-based localization method achieves better positioning performance than existing methods, although there is a slight increase in computational complexity compared to the initial localization. Moreover, our proposed CLB pilot reassignment scheme outperforms the conventional pilot assignment schemes in the comprehensive performance considering both channel estimation performance and complexity.

Vehicle-to-infrastructure (V2I) communication enables vehicles to acquire surrounding traffic information in real time, which significantly improves the driving safety and comfort. The cable-connected roadside unit (c-RSU) with high communication capability and large communication range plays an indispensable role in V2I communication. Meanwhile, unmanned aerial vehicle (UAV) technology has developed rapidly. In terms of its unique mobility and flexibility, the UAV-assisted RSU (u-RSU) can dynamically adjust its position according to the traffic density and emergencies. So the u-RSU can be a reliable supplement to the ground RSU in V2I communication. In this paper, a combined c-RSU and u-RSU deployment strategy is proposed to achieve the maximal effective traffic coverage ratio (ETCR) under a given tough budget bound. To solve this problem, we introduce a two layer improved greedy algorithm (TLIGA). Within TLIGA, the first layer greedy algorithm embedded with the improved Kruskal algorithm is used to deploy c-RSUs and cable, while the second layer improved greedy algorithm is used to determine the optimal number of u-RSUs and their flight strategy. Simulation results show that compared with the existing methods the proposed algorithm TLIGA can significantly increase ETCR.

Recently, many researches based on millimeter wave (mmWave) together with analog beamforming technology have been done to provide higher transmission throughput in vehicle-to-everything (V2X) communication. While hybrid beamforming technology, which can generate multi radio frequency (RF) chains with reduced hardware complexity, continues attracting attention. Hence, this paper focuses on a hybrid-beamforming-based roadside unit (RSU) beam resource scheduling problem in vehicle-to-infrastructure (V2I) communication and intends to improve transmission fairness. Then, this paper proposes an indicator Q to evaluate transmission fairness. Furthermore, a Power-allocated-based Beam resource Scheduling strategy (PBS) and a Time-allocated-based Beam resource Scheduling strategy (TBS) are designed for the multi-RF-chain based V2I communication to enhance transmission fairness. Simulation results prove that, the two proposed beam resource scheduling strategies can effectively improve the transmission fairness in V2I communication.

Vehicle-to-infrastructure (V2I) communication enables vehicles to acquire surrounding traffic information in real time, which significantly improves the driving safety and comfort. The roadside unit (RSU) which transfers information among vehicles plays an indispensable role in V2I communication. For a long time, researchers have been focusing on choosing proper RSU deployment locations to deploy fixed RSUs. Meanwhile, unmanned aerial vehicle (UAV) technology has developed rapidly. In terms of its unique mobility and flexibility, the UAV-assisted RSU (u-RSU) is able to cover a larger area. If the flight trajectory is designed properly, fewer u-RSUs can solve the RSU deployment problem and ensure the network performance. Therefore, the large-scale u-RSU collaborative trajectories design problem is investigated in this paper. We propose a u-RSU flight trajectory strategy and solve the problem in three steps. An improved greedy algorithm and an ant colony optimization (ACO) algorithm are used in the solution. Under different conditions, the proposed u-RSU flight strategy is analyzed and compared with other deployment strategies. Simulation results show that our strategy always has better network performance and deploys fewer u-RSUs. Thus, the proposed strategy is more economical and effective compared with the traditional deployment strategy.

In this paper, we investigate the interactions between a base station (BS) and a full-duplex (FD) active eavesdropper in the uplink of multi-cell massive multiple-input multiple-output (MIMO) systems. Focusing on the resistance against simultaneous eavesdropping and jamming, we introduce a beam extraction method based on spatial information to eliminate pilot contamination, from which a beam-domain (BD) distributed receiving scheme is proposed to suppress the jamming mixed in users' signals. From a practical point of view, we use the perspective of a hierarchical game to analyze the interactions without the prior information of the eavesdropper. The results show that with the help of the proposed scheme, the performance of the FD-mode attack is inferior to that of passive eavesdropping. A statistical upper bound of eavesdropping is given in a closed-form, which is mainly determined by the direction and distance of the eavesdropper relative to users. Then, a joint power and combining matrix optimization algorithm is proposed to improve the secrecy capacity of the system.

In this paper, A low complexity decoding scheme of raptor-like low-density parity-check code (LDPC) in sufficient SNR scenarios such as in an over-engineered network or core layer of layered-division multiplexing (LDM), is proposed. This scheme is meaningful to emerging power-limited devices, such as IoT and handheld devices. Based on the proof of boundedness and convergence properties of raptor-like LDPC code, the proposed decoding scheme selectively skips some check node operations during iterative decoding. Furthermore, we propose a corresponding algorithm to choose a proper set of skipped check nodes, which ensures an acceptable performance with much lower complexity. Simulation results show that the proposed scheme is good enough to meet the performance requirements, while yielding attractive power benefits over conventional scheme. The decoding complexity can be reduced by up to 80% in the best case while the SNR is sufficient.

In traditional automatic retransmission request (ARQ) based wireless broadcasting (WBC) systems, one lost packet is retransmitted per slot to guarantee the correct reception of each packet, which leads to low spectrum efficiency. In this paper, we propose an Ave-Max-Min Network Coding (NC) based WBC protocol. Specifically, to reduce the overall number of retransmissions, lost packets of different user equipments (UEs) are combined by performing Ave-Max-Min NC at the base station. Then, NC combined packets are broadcasted to all the UEs. At each UE, lost packets can be recovered by using the proposed joint network recursive systematic convolution (RSC) decoder (JNRD). Theoretical analyses and simulation results show that the proposed Ave-Max-Min NC based WBC protocol outperforms traditional NC based WBC protocol on the average number of transmissions performance.

In this paper, we study the integer carrier frequency offset (ICFO) estimation problem for dynamic lattice multicarrier transmission (DLMT) system over time-varying rayleigh fading channel. Firstly, a novel preamble structure based on two constant amplitude zero auto-correlation (CAZAC) sequences is designed for DLMT system. The proposed preamble structure is named as dual-CAZAC preamble. Then, by using the designed dual-CAZAC preamble, a weighted cross ambiguity function (WCAF) based ICFO estimation algorithm is proposed. Simulation results show that the proposed WCAF based ICFO estimation algorithm can mitigate the impact of time-varying multipath rayleigh fading channel and outperforms traditional ICFO estimator on the correct estimation probability performance.

In this study, a hybrid user association scheme for maximising energy efficiency is proposed in a wireless uplink heterogeneous network with human‐to‐human and machine‐to‐machine communications coexistence. Different from conventional user association algorithms, the authors consider access control protocol for both the human‐type‐communications (HTCs) users equipments’ (UEs) and machine‐type‐communications (MTCs) UEs according to their characteristics during user association phase, i.e. a contention‐free access mechanism for HTC UEs and a random access based on access class barring mechanism for MTC UEs. They formulate the user association problem as a maximisation of the overall UEs’ energy efficiency in consideration of both the HTC and MTC UEs’ quality of service (QoS) guarantees and load balance among base stations simultaneously. To solve the problem, they propose a distributed iterative algorithm which adopts both the matching theory and the dual decomposition theory, and prove its convergence. Finally, simulation results show that their proposed user association scheme not only outperforms existing schemes in terms of the load balance and the overall energy efficiency but also achieves the same performance as exhaustive search in the case of less number of UEs, while satisfying both the HTC and MTC UEs’ QoS requirements.

In this study, the authors consider the direct link aware relay selection schemes for full‐duplex relaying (FDR) protocol. Different from traditional scheme, the direct link between source and destination is exploited to improve the performance of considered schemes. By investigating the signal‐to‐interference‐plus‐noise ratios at the relay and the destination, the exact expressions of outage probabilities for FDR protocol with proposed relay selection schemes are derived in closed‐form over Nakagami‐ m fading channels. To get more insight about the effect of system parameters, they develop an asymptotic analysis on the outage performance. The authors’ results show that the achievable diversity of the schemes depends on the shape factors of fading channels and the power scaling scheme of the relays. Moreover, they show that the proposed relay selection schemes achieve better outage performance when compared with traditional schemes and are robust to the residual self‐interference caused by the full‐duplex operation.

This paper considers the multi-pair full-duplex massive MIMO relaying, where multiple source-destination pairs communicate simultaneously with a common full-duplex amplify-and-forward relay. The low-complexity transceiver design at the relay based on zero-forcing (ZF) processing is presented. The asymptotic expression of the end-to-end signal-to-interference-plus-noise power (SINR) is derived under the general power scaling scheme. Through the theoretical results, we show that the deployment of very large antenna arrays at the relay has potential to eliminate the effect of echo interference due to the full-duplex operation, if the power scaling scheme is properly selected. Moreover, it is shown that the multi-pair full-duplex relaying outperforms the multi-pair half-duplex relaying in the spectral and energy efficiencies when the number of relay receive/transmit antennas is large.

In this paper, a novel carrier frequency offset estimation approach, including preamble structure, carrier frequency offset estimation algorithm, is proposed for hexagonal multi-carrier transmission (HMCT) system. The closed-form Cramer-Rao lower bound of the proposed carrier frequency offset estimation scheme is given. Theoretical analyses and simulation results show that the proposed preamble structure and carrier frequency offset estimation algorithm for HMCT system obtains an approximation to the Cramer-Rao lower bound mean square error (MSE) performance over the doubly dispersive (DD) propagation channel.