In this article, a hybrid radio frequency-visible light communication and positioning (RF-VLCP) system is designed, which can support high-data rate communication and high accuracy positioning with good energy efficiency (EE) performance. The hybrid system uses two links for downlink communication, namely, radio frequency (RF) and visible light communication (VLC) links, and employs visible light positioning (VLP) technology for positioning. Furthermore, an optimization problem is developed to allocate power for VLC and VLP links and to design beamformer for the RF transmitter. By doing so, the EE of the hybrid system is maximized while the Cramer–Rao Lower bound (CRLB) of the positioning error and the minimum data rate of the communication are guaranteed. A two-step algorithm is proposed to tackle the formulated optimization problem, which first determines the power allocation of the VLP signal and then obtains the power allocation of the VLC signal and the beamformer of the RF transmitter. Numerical results demonstrate the advantages of the proposed two-step algorithm over the existing algorithm in terms of computation speed. In addition, the EE performance of the hybrid system is evaluated under different data rate and positioning accuracy requirements. Besides, we also show that the hybrid RF-VLCP system is more energy efficient compared to standalone RF and VLP technologies.

This article proposes a new framework of aerial reconfigurable intelligent surface (ARIS) enhancing the nonorthogonal multiple access (NOMA) system. The base station (BS) transmits superimposed signals to multiple users with different channel gains through ARIS which can flexibly change channel conditions and perform intelligent NOMA operations. It ensures that our system can perform well in providing services to multiple users simultaneously. In this system, the placement of the unmanned aerial vehicle (UAV) is jointly optimized along with the AIRS passive beam and the multiuser power allocation in order to maximize the communication sum rate. Since the joint optimization problem is nonconvex and coupled, it is hence disintegrated into three subproblems and it is solved alternately through the successive convex approximation (SCA). Moreover, semi definite programming (SDP) is used to deal with the rank one constraint of RIS reflection matrix and comparisons are made using particle swarm optimization (PSO). The numerical results show that the proposed ARIS-NOMA framework can achieve better sum rate performance than traditional NOMA with fixed RIS and OMA-ARIS.

Specific emitter identification (SEI) refers to a process to determine the category of emitters by extracting, analyzing and matching the characteristics of received emitter signals. With the increasingly complex environment, traditional SEI methods, such as parameter matching, become difficult to meet the needs of robust and effective signal identification. Deep learning (DL) possesses powerful feature extraction ability and has been widely used in SEI. The superior performance of DL-based SEI methods also brings problems of redundant model parameters and high feature dimensionality, which further causes slow convergence rate, high storage requirements, and ever-increasing computational complexity. In this paper, we propose an SEI method based on end-to-end sparse feature selection (SFS) to make model pay more attention to features with good identification performance. Specifically, we add sparse parameters to features and design loss function composed of cross-entropy loss and sparse regularization. Several experiments are conducted on ADS-B, WiFi and LoRa datasets. From the simulation results, our proposed SFS-SEI method improves feature sparsity, speeds up loss convergence, reduces model parameters on the premise of ensuring accuracy. Code is available at: https://github.com/sleepeach/SFS-SEI.

Deep learning (DL)-based specific emitter identification (SEI) is a potential physical layer authentication technique for Industrial Internet-of-Things (IIoT) Security, which detects the individual emitter according to its unique signal features resulting from transmitter hardware impairments. The success of DL-based SEI often depends on sufficient training samples and the integrity of samples’ labels. The extensive deployment of wireless devices generates a huge amount of signals, but signals labeling is quite difficult and expensive with the high demand for expertise. In this article, we present an SEI method based on dual consistency regularization (DCR), which enables feature extraction and identification using a few labeled samples and a large number of unlabeled samples. With the help of pseudo labeling, we leverage consistency between the predicted class distribution of weakly augmented unlabeled training samples and that of strongly augmented training unlabeled samples, and consistency between semantic feature distribution of labeled samples and that of pseudo-labeled samples, which takes the unlabeled samples into account to model parameter tuning for a more accurate emitter identification. Extensive numerical results demonstrate that compared with well-known semi-supervised learning-based SEI methods, our method obtains 99.77% identification accuracy on a WiFi data set and 90.10% identification accuracy on an automatic dependent surveillance-broadcast (ADS-B) data set when only 10% of training samples are labeled, and improves the identification accuracy on the WiFi data set and the ADS-B data set by more than 19.07% and 5.30%, respectively. Our codes are available athttps://github.com/lovelymimola/DCR-Based-SemiSEI.

Abstract This study discusses the integrated system of full‐duplex (FD) multiple‐input‐multiple‐output (MIMO) communication and MIMO radar, which has been rarely considered in published studies. Such an integrated system is composed of a base station and several uplink (UL) and downlink (DL) users, and is supposed to simultaneously implement three functionalities, that is, target detection, UL and DL communications. The degrees of freedom of system design involve the precoding and combining schemes for UL and DL communications, as well as the receiving filter for radar echo. An optimization problem is developed to maximise a compound communication sum‐rate metric subject to the SINR constraint imposed on target detection performance. The resulted non‐convex and NP‐hard problem is first divided into two sub‐problems. Then, for one of the sub‐problems that is still intractable, its solution is iteratively obtained based on the fractional programing technique and the consensus alternating direction method of multipliers algorithm. In the simulation part, the convergence performance and computational efficiency of the proposed algorithm are illustrated. In addition, the target detection performance, DL and UL communication performance of the integrated system are also evaluated.

This paper considers the hybrid beamforming design for wideband OFDM-DFRC system serving multiple users (MUs). The analog beamformer for the whole bandwidth and the digital beamformers for each subcarrier and are jointly optimized by considering flexible performance trade-off between the radar and communication. To deal with the resulting optimization problem, a consensus alternating direction method of multipliers (consensus-ADMM) framework based on the weighted mean-square error minimization (WMMSE) approach is proposed. Numerical simulations are provided to demonstrate the effectiveness of the proposed scheme.

This article considers the waveform design problem for airborne multiple-input-multiple-output (MIMO) radar systems with space-time adaptive processing (STAP). We choose the mutual information between the received signal and the target impulse response as the design metric to achieve enhanced detection performance under the influences from neighboring range cells. In order to solve the resulting optimization problem, we first decompose the objective function into two parts and consider their optimization individually. We optimize the first part by relaxing it using an upper bound and optimize the second part using a simple optimization procedure based on the majorization-minimization (MM) algorithm. The alternation direction method of multipliers (ADMM) framework is then adopted to derive the overall solution, where the MM algorithm is employed again to handle the fourth-order term of the waveform vector. Numerical results are provided to show that the proposed algorithm has better detection performance than that of the existing methods.

In this paper, the problem of transmit sequence design for a dual-function radar-communication (DFRC) system equipped with one-bit digital-to-analog converters (DACs) is investigated. More specifically, a nonconvex problem is formulated by minimizing the symbol mean-square error, while ensuring the target localization performance for radar. A direct one-bit sequence design tackled by an alternating minimization (AM) approach, which involves a simple unconstrained quadratic sub-problem with closed-form solution and a quadratically constrained nonconvex sub-problem tackled by the alternating direction method of multipliers (ADMM) algorithm, is developed. The solutions of primal variables under the ADMM framework are provided and the convergence is discussed. Additionally, an indirect but computationally more efficient design, which is realized by transmit beamforming based on an accelerated primal gradient (APG) method, is presented. For better understanding of this design, both the Cramér-Rao Bound (CRB) and symbol error probability of the resulting beamformer with one-bit quantization using the Bussgang theorem are analyzed. Numerical simulations are provided to demonstrate the effectiveness of the proposed schemes.

The sky-wave over-the-horizon radar is easily interfered with by radio frequency interference (RFI) since the high-frequency band is shared by many radio devices. Various RFI suppression techniques have been developed by both spatial and temporal processing. However, in practice, where the interference characteristic is usually unknown, it is still hard to evaluate the effects of interference suppression. In this letter, the Tamura coarseness is introduced as an indicator to evaluate the RFI and its suppression algorithms. By analyzing the range-Doppler maps with RFI, the relationship between coarseness and signal-to-interference-plus-noise ratio is established. An application example of coarseness is also given, in which the diagonal loading factor of adaptive beamformer is optimized. The analysis of coarseness and performance of proposed scheme are demonstrated by experimental data.