The Internet of Things (IoT) supported by satellites is becoming indispensable for remote sensing in the forthcoming sixth-generation (6G) communication network. However, it is tempting and easy for unauthorized users to exploit the Direct Sequence Spread Spectrum (DSSS) technique to quietly complete their own transmissions due to the open nature of propagation and the publicly available satellite orbits and frequencies. Therefore, it is necessary to conduct a blind estimation of the DSSS signal to take a more proactive approach to protect satellites against illegal use. However, the modulation information is unknown, and the spreading code structure varies, making the blind estimation of spreading codes a significant challenge. Additionally, under data modulation, the dimensionality of the received spread spectrum sequence increases, greatly raising the complexity of spreading code estimation. Against this background, we propose a hybrid learning-based blind estimation algorithm for spreading codes, which combines K-means clustering and Convolutional Neural Networks (CNN). This algorithm achieves low-complexity blind estimation of spreading codes with unknown modulation information and low signal-to-noise ratio. Specifically, the K-means clustering algorithm uncouples the data modulation from the spreading code, reducing the dimensionality of the estimation process. On this basis, the CNN-based parallel convolution architecture is employed to achieve low-complexity and accurate estimation of the spreading code. Simulation results demonstrate that our proposed algorithm outperforms existing algorithms in both computational complexity and estimation performance.

Low Earth Orbit (LEO) satellite communication presents a promising solution for data backhaul from oceanic buoys. However, due to the power-limited buoys and large path loss between the buoy and LEO satellites, the received signal at the LEO satellites is critically weak, which poses severe challenges to both signal detection and localization of the buoys. To make things worse, the high mobility of LEO satellites introduces large Doppler frequency shifts that complicate signal detection, while ocean currents exacerbate the positional uncertainty of buoys. The interaction of these factors highlights the inadequacy of conventional approaches that fail to address detection and localization jointly. To address these challenges, we propose a direct detection and localization algorithm based on the Generalized Likelihood Ratio Test (GLRT). To reduce the complexity of the exhaustive grid search algorithm, we employ Particle Swarm Optimization (PSO) as a low-complexity search algorithm. Furthermore, the theoretical analysis is proposed from the perspective of closed-form expressions for the probability of detectionPDand the Earth-surface-constrained CRLB. Numerical simulation results demonstrate that the proposed algorithms can attain the CRLB at high SNR.

This article presents a novel noncoherent multisatellite weak signal acquisition scheme by aggregating the observations at multiple low-Earth orbit (LEO) satellites. Existing aggregation schemes rely on exhaustive search over grids on Earth surface to compensate for the differences in the delay and the Doppler frequency shift experienced at different satellites, which have high-computational complexity due to the wide coverage of LEO satellites. Motivated by this observation, we propose to directly work with satellites’ time-frequency (TF) grid and facilitate efficient delay and Doppler compensation by gradually narrowing down the search space over each satellite’s TF grid with multisatellites observations. Our scheme employs geometric search space reduction scheme to reduce the search space for possible Doppler frequency shift and utilizes hierarchical geometric correlation peak matching to eliminate fake correlation peaks based on multisatellite observations. Through extensive performance evaluation, we demonstrate that, in comparison with existing schemes, our proposed multisatellite signal acquisition scheme can achieves significantly better acquisition performance with a much lower computational complexity.

In this work, a reconfigurable intelligent surface (RIS)-based multi-input–multi-output (MIMO) covert communication system is considered and studied for Internet of Things (IoT) networks, while the joint optimization of precoder and RIS reflection phase is carried out to improve the covert communication performance. To solve this problem, we first derive the optimal signal-interference-to-noise ratio under covert communication and transmit power constraints. Then, we simplify the optimized function and use an iterative optimization algorithm to determine the optimal phase shift and precoding in continuous and discrete cases. Simulation results show that the RIS-aided MIMO covert communication system proposed in this article can significantly improve the invisibility of the implementation from Willie. In addition, for covert communication, the performance of the continuous phase shift case outperforms that of the discrete phase shift and fixed phase.

The Internet of Remote Things (IoRT) supported by low-Earth orbit (LEO) satellites is becoming indispensable for remote sensing and it will play an important role in the forthcoming sixth-generation (6G) communication network. In exploring its applications in remote mining and smart grid, etc., it is found that the implementation of IoRT faces challenges, including limited energy supplies, high-mobility, and security vulnerabilities. To address these challenges, we propose employing collaborative LEO satellites to enable the implementation of secure and green IoRT. By combining the uplink signals received at collaborative LEO satellites, the signal-to-noise ratio (SNR) can be significantly improved, so that relieving the transmit power requirement of the energy-limited terminal. Aiming at constructing a collaborative LEO satellite-based IoRT network, this article introduces the system design principles regarding to frequency planning, waveform selection, collaboration strategies, and terminal design. In order to obtain optimal collaboration performance, we propose a signal coherent combining scheme to compensate Doppler shift, propagation delay, and phase differences. Furthermore, we propose a modified SUMPLE algorithm to estimate and compensate phase differences among satellites, which is applicable to direct-sequence spread spectrum (DSSS) signal scheme. Simulation results demonstrate that our proposed algorithm outperforms the traditional SUMPLE algorithm in combining gain.

Reconfigurable intelligent surface (RIS) assisted millimeter wave (mmWave) communications has been envisioned as a prominent technology for future wireless networks, since it is capable of simultaneously providing abundant spectrum resources and favorable propagation environments. The small wavelength at mmWave bands also enables the widespread use of large antenna arrays, of which the hybrid beamforming structure has emerged as a cost-effective solution. In this paper, we aim to minimize the sum-mean-square-error (sum-MSE) in the RIS-assisted mmWave multiuser multiple input multiple output (MU-MIMO) system by jointly optimizing the hybrid analog-digital precoders and the RIS reflection matrix. We demonstrate that the role of RIS in assisting mmWave communications can be completely replaced by a large-scale Kronecker-structured hybrid array. Moreover, an accelerated Riemannian gradient algorithm using majorization minimization technique is proposed to tackle the unit-modulus constrained analog precoder/RIS design. Under the assumption of perfect channel state information (CSI), we firstly consider the single-user MIMO (SU-MIMO) setup and propose an effective alternating minimization (AM) procedure to characterize the system performance limit. Moreover, a two-stage scheme is developed for low-complexity implementation. This AM procedure is then extended to the general MU-MIMO scenario. In addition, we develop a novel enhanced regularized zero-forcing (ERZF) scheme for simultaneously combating strong noise in the low-SNR regime and mitigating multi-user interference (MUI) in the high-SNR regime. The optimality of our proposed algorithms is validated for some simplified practical scenarios. Numerical results illustrate that the proposed algorithms outperform existing benchmark schemes in terms of the actual complexity and performance.