Physical-layer key generation (PLKG) has attracted significant attention due to its lightweight and strong randomness, making it highly suitable for drones with energy and computational constraints. However, when drone communications rely on relay nodes, untrusted relay nodes may launch attacks such as eavesdropping, tampering, replay and man-in-the-middle, leading to key leakage. To address this problem, we propose a drone -physical unclonable functions (PUFs) - PLKG (DPPLKG) scheme that enhances resistance to untrusted relay attacks. In DPPLKG, legitimate drones are equipped with paired PUF hardware, and artificial noise is injected during the PLKG process to reduce the accuracy of untrusted relay channel estimation. The generated physical-layer key serves as the PUFs challenge, and the unique hardware response of PUFs generates the final session key, ensuring consistent and secure key generation among legitimate drones. After analysis, DPPLKG can effectively resist various threats such as relay eavesdropping, tampering, replay, and impersonation attacks. The simulation results show that DPPLKG outperforms traditional PLKG in terms of key generation rate, bit error rate, and key entropy. It can also resist Doppler frequency changes caused by drone mobility and hardware noise caused by temperature changes in PUF devices. In addition, the proposed scheme has an acceptable delay time, making it a practical and efficient solution for achieving drone secure communications in the presence of untrusted relays.

Automatic modulation classification (AMC) enables the identification of modulation schemes without prior information, facilitating efficient signal processing. Recently, deep-learning (DL)-based AMC has significantly advanced signal detection and recognition across various domains, including Internet of Things (IoT) systems and industrial cognitive communication systems. While high-performing AMC models achieve remarkable accuracy, their substantial storage and computational demands hinder deployment in resource-limited IoT communication systems. To address this challenge, we propose CPPCNet, a high-performance, lightweight complex-valued partial pointwise convolutional neural network. By leveraging complex-valued operations for automatic feature extraction, CPPCNet preserves phase information, enhancing classification performance. To alleviate the computational burden of complex-valued operations in resource-limited IoTs, we introduce complex-valued partial pointwise convolution (CPPC), which optimally balances accuracy and model complexity. Experimental results show that CPPCNet, with only 65302 parameters, achieves a state-of-the-art (SOTA) accuracy of 66.38% on RML2016.10b among all existing AMC models. On RML2016.10a, it also achieves a strong performance with an accuracy of 62.25%. Furthermore, it achieves 83.5% accuracy on the HisarMod2019.1 dataset, which includes more realistic channel impairments, outperforming existing lightweight AMC models in both accuracy and inference speed. These results highlight CPPCNet’s strong generalization ability and its ability to balance performance and efficiency, making it a promising solution for AMC applications in resource-constrained and dynamically changing environments.

Integrated sensing and massive multiple-input-multiple-output (MIMO) communication (mMIMO-ISAC) at terahertz (THz) bands can provide vast spatial degrees of freedom and abundant bandwidth resources. However, the employment of a massive number of antennas will pose prominent challenges to both target sensing and channel training in THz-mMIMO-ISAC. In this article, our goal is to integrate the target sensing functionality into the channel estimation stage and develop a channel-training-aided target sensing framework to facilitate the efficient resource sharing of THz-mMIMO-ISAC. Specifically, by exploiting the sparse characteristics of THz mMIMO channels, we build up the intrinsic connection between the channel parameters and the target parameters in angular, delay, and Doppler dimensions. Then, we propose a shared channel training pattern accommodating the hybrid architecture constraints of THz transceiver. Both the channel estimation and the target sensing can be formulated as two structured tensor decomposition problems and then concurrently addressed at the UE and BS sides, respectively. Next, we propose a tensor-based parameter estimation algorithm to acquire the target and channel parameters, where the associated angles of arrival/departure, time delays, Doppler shifts, and coefficients can be extracted from the estimated factor matrices. In addition, we present the detailed derivation of the Cramér-Rao bound (CRB) for the considered parameter estimation problem in THz-mMIMO-ISAC. Numerical results demonstrate that the proposed algorithm can achieve the target parameters estimation performance close to their corresponding CRB, and recover the high-dimensional THz mMIMO channels with substantially reduced training overhead.

In recent years, the particle filter (PF)-based track-before-detect (TBD) method has garnered attention in long baseline (LBL) localization algorithms. This approach can overcome the measurement-to-track association (MTA) challenges and complex underwater environments. In this paper, LBL underwater target localization capability is enhanced by designing the likelihood ratio function and constructing adaptive thresholds. Specifically, our contributions are as follows: First, we design the likelihood ratio function to enable automatic tracking management decisions and reduce the convergence time. Second, we construct adaptive thresholds to cope with the dynamically changing environment. Based on simulation results, the proposed algorithm outperforms the traditional localization algorithm and PF-TBD algorithm in dynamically changing environments, especially when signal-to-noise ratios are low, and has superior tracking and location capabilities.

Employing ambient backscatter communication (AmBC) technology in Intelligent Transportation Systems (ITS) has emerged as an appealing solution to boost the awareness of crosswalks. However, the AmBC-based ITS is expected to face serious security threats due to the presence of malicious eavesdroppers. In this paper, we investigate the secure multi-antenna transmission in an AmBC-based ITS coexisting with a passive eavesdropper with jamming. Specifically, a cooperative jammer is placed in the system to deliberately disrupt the eavesdropper without affecting the legitimate receiver. In order to characterize the performance of the proposed scheme, new approximate closed-form expressions of secrecy outage probability (SOP) are derived by adopting the Gauss-Chebyshev quadrature. Additionally, the asymptotic behavior of SOP at the high signal-to-noise ratio (SNR) regime is also studied to provide more insights into the system design. We also derive the asymptotic SOP, when the number of transmit antennas tends to infinity. Monte Carlo simulations are provided to demonstrate the validity of our analytical results and to show that 1) the secrecy performance can be significantly improved by allocating part of the transmit power to perform cooperative jamming and 2) the optimal power allocation factor is related to the total transmit power.

Ambient backscatter communication (AmBC) has emerged as a paradigm distinguished by its energy-efficient attributes and low-power dynamics, ideally suited to address the vast expanse of the Internet of Things (IoT). Unmanned aerial vehicles (UAVs) deployed with flexibility can effectively establish wireless connections for isolated IoT devices through AmBC. This paper delves into the exploration of secure transmission within a UAV-assisted AmBC network, particularly addressing the challenges posed by the presence of a passive eavesdropper. Specifically, a UAV is utilized as an aerial base station to offer services to an isolated ground user, an AmBC tag transmits its information to its associated receivers by leveraging the UAV’s radio frequency (RF) signals. Furthermore, a multi-antenna cooperative jammer is integrated within the system to intentionally interfere with the eavesdropper without affecting legitimate receivers. To characterize the secrecy performance, the expressions of secrecy outage probability of the air-ground link and backscatter link are both deduced leveraging a two-layer Gaussian-Chebyshev quadrature. Moreover, the asymptotic behaviors under the high signal-to-noise ratio (SNR) regime are also analyzed. Monte Carlo simulations are performed to validate the correctness and effectiveness of the analytical results.

Benefitting from the vast spatial degrees of freedom, the amalgamation of integrated sensing and communication (ISAC) and massive multiple-input multiple-output (MIMO) is expected to simultaneously improve spectral and energy efficiencies as well as the sensing capability. However, a large number of antennas deployed in massive MIMO-ISAC raises critical challenges in acquiring both accurate channel state information and target parameter information. To overcome these two challenges with a unified framework, we first analyze their underlying system models and then propose a novel tensor-based approach that addresses both the channel estimation and target sensing problems. Specifically, by parameterizing the high-dimensional communication channel exploiting a small number of physical parameters, we associate the channel state information with the sensing parameters of targets in terms of angular, delay, and Doppler dimensions. Then, we propose a shared training pattern adopting the same time-frequency resources such that both the channel estimation and target parameter estimation can be formulated as a canonical polyadic decomposition problem with a similar mathematical expression. On this basis, we first investigate the uniqueness condition of the tensor factorization and the maximum number of resolvable targets by utilizing the specific Vandermonde structure. Then, we develop a unified tensor-based algorithm to estimate the parameters including angles, time delays, Doppler shifts, and reflection/path coefficients of the targets/channels. In addition, we propose a segment-based shared training pattern to facilitate the channel and target parameter estimation for the case with significant beam squint effects. Simulation results verify our theoretical analysis and the superiority of the proposed unified algorithms in terms of estimation accuracy, sensing resolution, and training overhead reduction.

Space–air–aqua integrated network (SAAIN) is an emerging maritime network architecture to support reliable and timely communications. Considering the computation capability and information security in maritime transportation systems, this work proposes a double-edge secure offloading scheme, where both base-station (BS) and satellites provide secure mobile-edge computing services for delay-sensitive applications. Specifically, maritime mobile users may offload their computation tasks adaptively to the BS or satellites securely relayed by unmanned aerial vehicles (UAVs). To minimize offloading delay, we formulate an optimization problem to allocate the transmit power cooperatively via UAVs’ trajectory optimization. Moreover, jamming UAVs are deployed to protect the offloading process. For such a nonconvex optimization problem, two iterative algorithms are proposed to determine the transmit power and design the UAVs’ trajectories. Numerical results show the effectiveness of the proposed scheme in terms of offloading delay.

There is an increasing interest in direction-of-arrival (DOA) estimation using nested arrays composed of vector sensors. Considering acoustic vector sensors (AVSs) commonly used in underwater applications, this paper proposes a novel algorithm, called augmented nested quaternion-MUSIC (ANQ-MUSIC), to perform DOA estimation. By judiciously arranging the multi-component outputs of AVSs, we model the received signals from the entire nested AVS array as a quaternion observation vector in a compact way to reduce the computational complexity. Next, we formulate a quaternion-based difference co-array (QDCA) model via vectorizing the quaternion covariance matrix (QCM). Based on the obtained insights from the QDCA model, we derive a suitable QCM, which is constructed by applying the spatial smoothing technique. Finally, classical quaternion-MUSIC is logically introduced to estimate the DOA parameters. In simulations, we take into account non-uniform received noise and inter-component correlated noise, which may occur in practical underwater environments. The results demonstrate that the proposed method shows superiority in angular resolution and achieves a desirable trade-off between estimate accuracy and computational burden, besides showing robust performance in the above test scenarios.

With the proliferation of marine activities, underwater Internet of Things (UIoT), which integrates various techniques for supporting smart ocean, has attracted more research interest. The trend is that one desires to use computationally efficient and widely applicable algorithms for source localization. For fulfilling the above requirements, this paper proposes a novel unitary quaternion (UQ) model, which is applied to widespread centro-symmetric arrays. The estimation and decomposition of the corresponding covariance matrix can be executed in the real number field, thus benefiting from low complexity. Moreover, we analyze the physical implication of the proposed model and associate it with the emerging quaternion-based attitude estimation and control, which reveals the potential advantages of the UQ model in UIoT. In the simulations, we test the algorithm performance in several realistic underwater scenarios, demonstrating the flexibility and applicability of the UQ model.

Radio signals are electromagnetic waves that are propagated in freespace. This nature makes it vulnerable to be attacked from eavesdroppers. Fortunately, with the aid of the reconfigurable intelligent surface (RIS), which passively reflects the incident signal, the spatial distribution of the signal strength can be customized to benefit legitimate users. In this work, we propose a RIS aided non-orthogonal multiple access (NOMA) transmission scheme to provide secure links for two users, where the unmanned aerial vehicle (UAV) equipped with RIS serves as a relay to change radio coverage flexibly. In the proposed scheme, the NOMA transmit power of base-station (BS), the UAV's location, and the RIS phase shift are jointly optimized to maximize the secure transmission rate, which is a nonconvex optimization problem. For this non-convex problem, we first decompose it into three subproblems. Then an efficient iterative algorithm is proposed, where the transmit power and UAV's location are optimized through the successive convex approximation (SEA) method, and the phase shift is optimized through the semi-definite relaxation (SDR) strategy. Numerical results verify the secrecy superiority of the proposed scheme compared with the current schemes.

Nested acoustic vector sensor (AVS) arrays have attracted growing interest, and their performance can be further improved by assembling AVSs with spatially separated (SS) configurations. Following this trend, we propose an augmented tensor-MUSIC (AT-MUSIC) method for SS-AVSs while exploiting the inherent multidimensional structure of the array data. The implications of “augmented” are twofold: (i) conventional tensor models based on co-centered AVSs are generalized to the case of SS-AVSs, and (ii) available information is fully utilized by judiciously arranging and aggregating the received data. Finally, we show the enhanced performance of AT-MUSIC in simulations.

Massive multiple-input multiple-output (MIMO) integrated with millimeter-wave (mmWave) can provide unprecedented performance improvement for realizing future wireless communications. However, acquiring accurate channel state information in wideband mmWave massive MIMO systems with hybrid transceiver architectures is even challenging, especially in high-mobility scenarios with severe Doppler effects. In this paper, we propose a tensor decomposition-based method to estimate the time-varying and frequency-selective (TVFS) mmWave MIMO channels. Specifically, by exploiting the sparse scattering nature of TVFS channels, we model the frequency-domain received signal as a third-order tensor that admits a canonical polyadic (CP) decomposition format. Then, we analyze the uniqueness condition of the proposed CP decomposition-based channel estimation problem and propose a novel estimator to acquire TVFS channel parameters including angle of departure/arrival (AoD/AoA), time delay, path gain, and the Doppler shift. To address the sophisticated coupling among unknown parameters, we further propose a joint AoD and Doppler shift estimation (JADE) algorithm that provides reliable initial and iteratively refined estimates. The derived analysis and simulation results verify that the proposed JADE algorithm achieves higher estimation accuracy and guarantees the superiority of the proposed TVFS channel estimator over existing schemes.

In this letter, we present a novel computationally efficient DOA estimation algorithm based on quaternion theory for two-dimensional (2-D) direction-of-arrival (DOA) estimation. An orthogonal propagator method based on the cross-correlation of the quaternion models (OPM-CQM) is developed to alleviate the computation burden. To eliminate the effect of additive noise, we construct two quaternion-based signal models judiciously. Then, we obtain the statistics of the observed signals by performing the cross-correlation between the quaternion models. Meanwhile, the additive noise is eliminated without introducing other denoising methods. Moreover, the compact modeling approach based on quaternions provides a significant advantage to OPM-CQM in terms of computational effort. Simulations demonstrate that the proposed algorithm offers performance superiority in angular resolution compared with the non-quaternion schemes.

An exact closed-form average bit-error rate expression of a selection combining (SC) scheme is derived for a differential cooperative system employing an amplify-and-forward relay with simultaneous wireless information and power transfer capability. Monte Carlo simulations verify the analytical expressions. In particular, the SC scheme can achieve the same performance as that of a suboptimal maximal ratio combining scheme, and this is achieved with lower implementation complexity and energy consumption for SC.

Two weighted selection combining (WSC) schemes are proposed for a differential decode-and-forward relaying system in Rayleigh fading channels. Compared to the conventional SC scheme, the decision variable of the relay link is multiplied by a scale factor to combat the error propagation phenomenon. Average bit-error rate (ABER) expressions of the two proposed WSC schemes are derived in closed form and verified by simulation results. For the second WSC scheme, asymptotic ABER expression and diversity order are derived to gain more insight into this scheme. Moreover, it is demonstrated that both WSC schemes can overcome the extra noise amplification induced by the link adaptive relaying scheme. The first WSC scheme is slightly inferior to the second one, which has a higher complexity. Both proposed WSC schemes outperform the conventional SC scheme.