UAV-assisted distributed IoT data collection plays a vital role in scenarios ranging from post-disaster response to large-scale temporary events. This paper investigates dynamic resource allocation for UAV-assisted IoT uplink transmission under partial channel state information (CSI). Considering the impracticality of acquiring full CSI due to pilot overhead and limited device computing capabilities, the problem is modeled as a Partially Observable Markov Decision Process (POMDP). A dynamic scheduling strategy is proposed, jointly optimizing node selection, beamforming weights, and UAV trajectory. By leveraging belief updates to integrate noisy channel observations with historical information, the proposed approach enhances decision-making under uncertainty. Furthermore, we introduce a novel channel-aware belief-space rollout (CABR) algorithm, which combines reliability-driven action candidate generation, a weighted multi-factor reward function, and adaptive planning depth based on task process to efficiently allocate resources under partial CSI. Simulation results demonstrate that the proposed method significantly improves throughput and reduces latency compared to the state of the art.

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.

Efficient communication in massive multiple-input-multiple-output (MIMO) systems relies on accurate channel estimation to optimize signal transmission efficiency, reliability, and minimize interference and power consumption. However, the presence of nonuniform array gain-phase perturbations among antenna elements poses practical challenges, degrading the precision of estimation. In response, this article introduces a parameterized joint angle and delay estimation (JADE) method tailored for multipath channel estimation in fully uncalibrated arrays within massive MIMO systems. Our innovative spatial and frequency-based co-smoothing method is proposed to construct a rank-recovered data covariance matrix, enhancing the system’s ability to distinguish coherent multipath signals. The JADE method employs a 1-D angular spectrum and delay spectrum search under the principle of rank reduction, providing a closed-form solution for array gain-phase perturbation estimates. The deterministic Cramér-Rao lower bound for the proposed model is derived. Numerical simulations affirm the method’s superior performance. In conclusion, our approach addresses the demand for precise channel estimation in low-signal-to-noise ratio scenarios, particularly benefiting Internet of Things (IoT) applications.

Abstract Polarisation‐sensitive array (PSA) has earned extensive attention in satellite communication owing to excellent anti‐interference performance. Nevertheless, the configuration of traditional PSA where each polarised antenna requires multiple radio frequency (RF) front‐ends makes it much more costly in terms of hardware and software for system design. In this paper, resorting to RF switches, a novel reconfigurable PSA optimisation technique is developed that utilises fewer RF front‐ends, which can considerably decrease computational expenditure and achieve high anti‐jamming performance. An RF switch switching (RFSS) scheme is devised to guide the implementation of PSA reconfiguration. To accurately demonstrate the effect of the array configuration on interference rejection performance, the polarisation‐spatial subspace correlation coefficient (PSC) is presented. Then the relationship between the optimal signal to interference plus noise ratio (SINR) and the PSC based on adaptive processing is formulated. Aiming to acquire the optimal reconstructed PSA outputting the maximum SINR, the mathematical model of the PSA reconfiguration problem is established. Subsequently, two optimisation methods are provided to address the problem efficiently, thereby gaining the optimal configuration of the reconstructed PSA and implementing the reconfiguration by RF switches. Numerical and experimental simulations verify the correctness and reliability of the developed scheme and approaches.

Wide-band interference (WBI) may severely affect the imaging quality of synthetic aperture radar (SAR) systems. Since it highly overlaps with useful signals in the 1-dimensional (1-D) time or frequency domain, the existing WBI mitigation methods usually transform 1-D echoes into a 2-D transform domain. However, they usually suffer from a model mismatch, which results in the loss of the useful signal. To tackle this problem, a novel algorithm combining time-frequency-pulse (TFP) joint characteristics and robust principal component analysis (RPCA) is proposed for WBI mitigation. The TFP joint feature of SAR echo is introduced for interference mitigation for the first time. We first transform the SAR echoes into the time-frequency domain, and construct a new TFP matrix by reshaping the STFT matrices between adjacent pulses. In terms of the WBI-occupied SAR echoes, the short-time Fourier transformation (STFT) in adjacent pulses can be modeled as a combination of a low-rank part (i.e. useful SAR echoes) and a sparse counterpart (i.e. WBIs), which well fits the assumption of RPCA. Then, the TFP matrix is decomposed into the useful signal TFP matrix and the WBI TFP part by taking full advantage of the low-rank and sparse properties. Finally, the WBIs can be reconstructed and subtracted from the echoes to realize interference mitigation. Experimental results on both simulated and measured datasets show that the proposed algorithm not only suppresses WBIs effectively but also preserves useful information as much as possible.

In this paper, a novel efficient algorithm is developed for direction of arrival (DOA) estimation of coherent signals under the direction-dependent mutual coupling (DDMC) based on weighted subspace fitting. DOAs are determined by applying the least square fitting between signal space and the modified array manifold at first. Subsequently, we put forward an approach to calculate the DDMC matrices and the complex fading coefficients by utilizing the estimated DOAs. Without any iterations, the proposed algorithm can identify the angular information of the coherent signals in a single step in the presence of DDMC. Numerical simulation results show the effectiveness of the proposed algorithm.

On the ground with complex electromagnetic environment, protecting the received satellite signals from interference is a key issue for the receiver. To effectively cope with the coexistence of jamming and spoofing interference, and accurately suppress both jamming and spoofing, in this paper, a hybrid interference suppression algorithm based on robust beamforming is proposed. The combination of subspace projection algorithm, despreading algorithm, multiple signal classification (MUSIC) algorithm and robust beamforming based on the linearly constrained minimum variance criterion can suppress both jamming and spoofing effectively, and ensure that the desired signal is undistorted. At the same time, it can overcome the problem of inaccurate direction estimation under low signal-to-noise ratio. Numerical examples show that the proposed algorithm can effectively suppress hybrid interference.

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.

With the increasing sensing ability of smartphone, both recognizing and understanding a user's activity using sensor data have become a popular topic of ubiquitous computing systems. Individual transportation mode identification can provide essential data for road planning and traffic management. In this paper, we present a Convolutional Neural Networks (CNN) based method to extract expressive and discriminative features automatically for transportation mode identification. The signal preprocessing in the time and frequency domain is performed before the sensor data is fed into the deep learning framework. We optimize various important hyper-parameters such as learning rate, kernel size and number of convolutional layers to adapt the characteristics of multiple sensor signals. Extensive experimental results indicate that the proposed CNN based transportation mode identification algorithm can achieve 98% accuracy to distinguish between car, bus, train and metro, which outperforms the Support Vector Machines and Adaboost based transportation identification with better robustness and generalization.