Nonlinear hybrid precoding can approach capacity, but it is computationally demanding. This paper investigates low-complexity nonlinear hybrid precoding algorithms designed to maximize the sum-rate of massive multiple-input multiple-output broadcast channels (MIMO-BC). We propose a closed-form solution for analog precoding in narrowband systems, transforming the constant modulus constraint into an equivalent unconstrained problem with phase variables, which can be efficiently solved using the Fletcher-Reeves conjugate gradient method. Additionally, we introduce a nonlinear hybrid precoding technique for wideband systems, specifically tailored to delay-phase networks, which leverages channel characteristics to reduce computational complexity without relying on optimization. To further reduce the complexity of subcarrier-level digital precoding, we develop a resource block (RB)-level nonlinear precoding algorithm, where signals within each RB are independently precoded using a common precoder. Simulation results show that the proposed algorithms achieve near-optimal sum-rate performance while maintaining computational efficiency in both narrowband and wideband scenarios.

To overcome the performance limitations and computational complexity induced by the unimodular constraint in conventional hybrid precoding designs, this paper proposes a novel optoelectronic base station (OE-BS) architecture, replacing the traditional phase shifter network with an optical network. The proposed OE-BS architecture facilitates simultaneous amplitude and phase control in the analog domain, effectively eliminating the unimodular constraint and significantly enhancing the design flexibility. Based on the proposed OE-BS architecture, a closed-form hybrid precoding solution is first derived for narrowband systems, thus avoiding iterative optimization procedures. For wideband systems, a user-selective hybrid precoding algorithm is developed, where the digital precoder is designed according to the zero-forcing criterion, and the analog precoder and power allocation are jointly optimized using an alternating optimization method. Simulation results demonstrate that the proposed OE-BS schemes outperform conventional methods in both narrowband and wideband scenarios, enhancing overall system performance while reducing computational complexity.

Electromagnetic information theory (EIT) is an emerging interdisciplinary subject that integrates classical Maxwell electromagnetics and Shannon information theory. The goal of EIT is to uncover the information transmission mechanisms from an electromagnetic (EM) perspective in wireless systems. Existing works on EIT are mainly focused on the analysis of degrees-of-freedom (DoF), system capacity, and characteristics of the electromagnetic channel. However, these works do not clarify whether EIT can improve wireless communication systems. To answer this question, in this paper, we provide a novel example of how to improve channel estimators by integrating EM knowledge into the classical MMSE channel estimator. Specifically, the EM knowledge is first encoded into a spatial correlation function (SCF) of the channel, which we term the EM kernel. This EM kernel plays the role of side information to the channel estimator. Since the EM kernel takes the form of Gaussian processes (GP), we propose the EIT-based Gaussian process regression (EIT-GPR) to derive the channel estimations. Furthermore, we propose EM kernel learning to fit the EM kernel to channel observations. Simulation results show that EIT benefits the channel estimator and enables it to outperform traditional isotropic MMSE algorithm, thus proving the practical values of EIT.

It is well-known that dirty paper coding (DPC)-based transceiver can achieve the sum capacity of a multi-input multi-output (MIMO) broadcast channel. In this paper, we study DPC-based hybrid precoding to maximize the sum-rate of a massive MIMO (mMIMO) downlink broadcast channel, subject to the constant modulus constraint of the analog phase shifter network (PSN). Using the duality between a massive MIMO broadcast channel (mMIMO-BC) and a massive MIMO multiple access channel (mMIMO-MAC), we propose to first optimize the transmitters and the hybrid receiver for the mMIMO-MAC, before obtaining the hybrid precoder of mMIMO-BC according to the MAC-BC duality. The algorithm is also extended for mMIMO orthogonal frequency division multiplexing (OFDM) systems over broadband frequency-selectivity channels. Numerical results show that the proposed DPC-based hybrid precoding can significantly outperform the existing linear counterpart, especially in the case of numerous users. In simulations based on the 5G new radio (5G-NR) standard, the relative gain of the proposed scheme appears prominent, which suggests that the DPC can be a worthy candidate for the future generation of wireless communications.

The performance of small target detection restricts the development of the infrared search and track (IRST) system. Against the complicated background clutter of the infrared (IR) image, the small targets are difficult to separate from a noisy background. Aiming at solving the problem of residual background clutter in the local contrast method, a weighted three-layer window local contrast method (WTLLCM) is proposed in this letter. First, the images are filtered by a layered gradient kernel to enhance the contrast between targets and background. Then, a three-layer window is utilized to calculate the local contrast of the filtered images. Next, it is worth mentioning that a simple target aggregation strategy is considered to preserve the integrity of the target. Especially, a new region intensity level (NRIL) algorithm is proposed to weigh the local contrast map to further suppress the background. Finally, the targets are detected by adaptive threshold segmentation. Compared with state-of-the-art small targets detection baseline algorithms based on local contrast, extensive experimental results demonstrate the superiority of the proposed method, especially in complex backgrounds. And instead of utilizing multi-scale windows, multi-scale targets detection is accomplished by using a single-scale window to reduce the calculation of the method.

Research about infrared dim and small target detection (DSTD) is concentrated on single-frame algorithms, which are limited by the contrast between target and background and face with the problems of low detection probability, high false alarm rate, and lack of robustness in low signal-to-clutter ratio (SCR) and strong noise environment. Studying the use of multiframe sequences adequately is necessary. In order to effectively utilize the temporal and local spatial information of infrared sequences, we propose a similarity model for pixel temporal profile (TP). Different from current TP detection methods, we study waveform similarity calculation for detection, and local time-shift characteristics to eliminate false alarms. First, fast Fourier transform (FFT) and KL divergence are applied to calculate the similarity of TP and reference waveform, and errors due to time offsets can be avoided through the frequency domain; second, the peak ratio of the FFT is applied to calculate the time shift of the neighboring pixels relative to the center pixel; third, maximum suppression strategy is used to reduce false alarms. Experiments show that the model and algorithms proposed in this letter have excellent performance in target enhancement and background suppression, and have higher performance than other methods in receiver operator characteristic curve (ROC).

Radiometric calibration of satellites is one of the core technologies for analyzing satellite data quantitatively. For large-aperture remote sensing cameras, the blackbody is placed in the rear optical path due to its weight and size. As a result, the front optics’ self-emission cannot be evaluated when the inner blackbody is observed. To achieve full optical path calibrations, stars are used as radiation calibration sources for large-aperture cameras. In high energy concentration detection systems, the point spread function (PSF), intrapixel sensitivity (IPS), capacitive coupling and sampling phase may cause some energy of the point source to be lost, resulting in an energy difference between the extended and point sources. It is important to note that conventional aperture photometry is not always the ideal method for obtaining high-precision photometry. This paper proposes a method for compensation of point-source signals based on capacitive coupling correction, PSF reconstruction and IPS model, and establishes a response conversion model for stellar and inner blackbodies. According to calibration coefficients based on stars and inner blackbody, the error between the star calibration coefficient and the inner blackbody calibration coefficient is 0.18%, which is better than 59.4% before the proposed method was applied. The error between the two methods can be stabilized within 0.3% within 140 days of the launch of the satellite.

Due to the complicated background and noise of infrared images, infrared small target detection is one of the most difficult problems in the field of computer vision. In most existing studies, semantic segmentation methods are typically used to achieve better results. The centroid of each target is calculated from the segmentation map as the detection result. In contrast, we propose a novel end-to-end framework for infrared small target detection and segmentation in this paper. First, with the use of UNet as the backbone to maintain resolution and semantic information, our model can achieve a higher detection accuracy than other state-of-the-art methods by attaching a simple anchor-free head. Then, a pyramid pool module is used to further extract features and improve the precision of target segmentation. Next, we use semantic segmentation tasks that pay more attention to pixel-level features to assist in the training process of object detection, which increases the average precision and allows the model to detect some targets that were previously not detectable. Furthermore, we develop a multi-task framework for infrared small target detection and segmentation. Our multi-task learning model reduces complexity by nearly half and speeds up inference by nearly twice compared to the composite single-task model, while maintaining accuracy. The code and models are publicly available at https://github.com/Chenastron/MTUNet.

To maximize the throughput of wireless powered communication (WPC), it is critical for the device to decide when to harvest energy, when to transmit data and what transmit power to use. In this paper, we consider a WPC system with a single device using harvest-store-transmit protocol and aim to maximize the longterm average throughput with optimal allocation of the energy harvesting time, data transfer time and the device’s transmit power. With the consideration of many practical constraints including finite battery capacity, time-varying channels and non-linear energy harvesting model, we propose both deep Q-learning (DQL) and actor-critic (AC) approaches to solve the problem and obtain fully online policies. Simulation results show that the performance of our proposed AC approach comes close to that achieved by value iteration and is superior to DQL and other baseline algorithm. Meanwhile, its space complexity is 2-3 orders of magnitude less than that required by value iteration.

Geometric positioning of a remote sensing image is one of the core technologies for the quantitative application of the geostationary satellite data. Affected by the change of the incident angle of the sunlight, the spatial thermal environment surrounding the remote sensing cameras (RSCs), especially the geostationary RSCs, fluctuates greatly and has a noticeable impact on the installation matrix based on the reference to the satellite body. Therefore, the spatial thermal environment will ultimately influence the camera's geometric positioning model and the final positioning accuracy. This paper proposes a novel correction method based on stellar observations for correcting geometric positioning error caused by spatial thermal deformation (STD) of geostationary optical payloads. The proposed method overcomes the drawbacks associated with current stabilization methods that involve shutting down the camera to reduce STD effects. Experimental results show that the positioning error corrected by the proposed method can be within ±1.9 pixels (2σ) at a 95% confidence level and better than the ±18 pixels before correction.