The sixth-generation (6G) mobile network and the Industrial Internet of Things (IIoT) demand high-speed, low-latency communication in diverse environments. Hybrid free-space optical (FSO)/radio frequency (RF) systems offer a promising solution by combining the strengths of both technologies. However, maintaining robustness under dynamic atmospheric conditions remains a challenge. To tackle this issue, we propose a feedback-free hybrid FSO/RF system that analyzes atmospheric conditions from camera-captured images and utilizes a deep learning network for real-time power allocation. By dynamically optimizing link utilization, it mitigates obstacles and weather fluctuations while eliminating feedback overhead, enhancing efficiency. Simulations show that our feedback-free system maintains superior bit error rate (BER) and outage performance while offering greater stability and adaptability to dynamic environments. Compared to state-of-the-art methods, it achieves more reliable performance under varying conditions. By leveraging camera-based perception instead of feedback links, it optimally adjusts FSO/RF utilization, providing a practical, scalable framework for next-generation IoT applications and ensuring reliable communication under varying conditions.

Due to the wide bandwidth and large observation area, spaceborne synthetic aperture radar (SAR) is easily interfered by other electromagnetic signals, namely radio frequency interference (RFI), which can severely degrade SAR image quality and submerge useful information. Classic parametric and non-parametric methods are used to suppress RFI as much as possible without considering the useful information. To protect the real reflected signals, semi-parametric methods, based on low-rank and sparse recovery, are proposed to mitigate RFI, but they suffer from the singular-value over-shrinking problem when RFI is not strictly low-rank, resulting in interference residues in the recovered scene. Hence, in this paper, a robust interference self-cancellation (RISC) method is proposed to protect raw ground scenes from polluted data with better extraction accuracy of RFI. The proposed model can adaptively fit in different scenes and backgrounds by using adjacent homologous interference (HI) subregions instead of the low-rank constraints, thus better protecting SAR scenes and enhancing its robustness. Based on the alternating direction method of multipliers (ADMM), we design two different solvers for the proposed optimization model, and both are tested on four different scenes of Sentinel-1 measured data. All experiments demonstrate that the proposed method has excellent performance in RFI mitigation and SAR image recovery.

As an advanced remote sensing technology, synthetic aperture radar (SAR) generates high-resolution images by transmitting continuous electromagnetic waves toward the target area. SAR has played a pivotal role in both contemporary research and practical applications. This underscores the importance of maintaining imaging integrity. However, the performance of SAR systems is severely affected by the increasingly prevalent radio frequency interference (RFI). RFI not only degrades the quality of SAR images but also hinders the accurate interpretation of SAR data. The rapid development and widespread use of modern electromagnetic devices have led to a diversification of interference types, resulting in complex mixed-mode interference. Traditional interference mitigation techniques struggle to effectively alleviate these issues. Moreover, varying terrains add significant difficulty to mitigating interferences, often resulting in residual interference in processed images and the loss of substantial scene information. To tackle these challenges, this article proposes a novel interference mitigation method called the group-parametric method. Unlike previous semiparametric methods, the group-parametric method refines both the interference and target models and achieves more effective interference mitigation and scene preservation by applying distinct regularizations to the refined models. Based on the new model, we have designed a structured trifactorization (STF) algorithm across frequency and time domains, which achieves data recovery through regularizations of low-rank and sparsity applied to the interference. Experimental verification with Level-1 data from LuTan-1 (LT-1) and Sentinel-1 confirms the effectiveness and superiority of our proposed model and method.

In this paper, we propose an innovative camera-screen communication system designed for real-time communication between 60Hz commercial screens and smartphone cameras. Firstly, we employ Color Modulation to encode information into a video, achieving flicker-free transmission imperceptible to human eyes. Subsequently, we utilize a consecutive characteristics of this modulation method and employ the Random Sample Consensus algorithm for the detection of Regions of Interest without screen detection.In addition, we apply least-squares method to detect the refresh stripe imposed on image samples, which is a severe interference in camera-screen communications. Finally, inter-frame differencing is used for decoding. Our experimental results highlight the robustness and utility of the proposed system, even in complex environments. Specifically, the system achieves up to 80% accuracy at a range of 3 meters.

Synthetic aperture radar (SAR) is a kind of active microwave remote sensing imaging radar, which can obtain high-resolution two-dimensional SAR images. As a multi-parameter, multi-channel SAR, polarimetric SAR (PolSAR) provides rich scattering information for topographic mapping, ocean exploration, polar observation, target identification, and many other fields. Compared to single-polarization SAR, multi-polarization SAR greatly improves the potential information of the data by extending the one-dimensional information. However, the above tasks cannot be carried out without clean SAR echo signal. The radio frequency interference (RFI) signals, seriously affect the subsequent tasks of PolSAR, and there is a great deal of potential information between polarized data. Therefore, this paper proposes a framework for combining multiple polarization data to improve low-rank based methods’ performance. Based on the proposed framework, one experiment is conducted on real PolSAR data, the experiment uses the PCA method to verify the applicability of the proposed framework in interference suppression. At last, the result verifies the framework achieves better suppression of low-rank based method.

In the massive multiple-input and multiple-output (Massive MIMO) systems, the maximum likelihood (ML) detection problem is NP-hard and becoming classically intricate with the number of transmitting antennas and symbols increasing. The quantum approximate optimization algorithm (QAOA), a leading candidate algorithm running in the noisy intermediate-scale quantum (NISQ) devices, can show quantum advantage for approximately solving combinatorial optimization problems. In this paper, we propose the QAOA based on the maximum likelihood detection solver of binary symbols. In the proposed scheme, we first conduct a universal and compact analytical expression for the expectation value of the 1-level QAOA. Second, a Bayesian optimization based parameters initialization is presented, which can speedup the convergence of the QAOA to a lower local minimum and improve the probability of measuring the exact solution. Compared to the state-of-the-art QAOA based ML detection algorithm, our scheme has the more universal and compact expectation value expression of the 1-level QAOA, and requires few quantum resources and has the higher probability to obtain the exact solution.

Synthetic Aperture Radar (SAR) is a type of active microwave remote sensing imaging radar that can generate two-dimensional high-resolution images. Its ability to operate in all weather conditions and at all times has led to its widespread use. As a multi-parameter and multi-channel extension of SAR, polarimetric SAR (PolSAR) provides a wealth of scattering information for various applications, including topographic mapping, ocean exploration, polar observation, and target identification. Compared with single-polarization SAR, multi-polarization SAR enhances the information potential of the data by expanding its one-dimensional information, however, this potential cannot be fully realized without a clean SAR echo signal. The electromagnetic environment is becoming increasingly congested with radio frequency interference (RFI) signals, presenting a significant challenge for the subsequent tasks of PolSAR. Although there have been many related studies based on polarization information to carry out the aforementioned applications, there is a lack of research on the joint suppression of interference by using multi-polarization information, and single-polarization data alone is insufficient in effectively mitigating interference. To address these challenges, this paper presents a framework combining multi-polarization data to improve performance of low-rank based methods. Based on the proposed framework, experiments are conducted on real PolSAR data to assess the feasibility of the proposed framework in interference suppression. The results demonstrate that the framework significantly enhances the suppression performance of various low-rank based methods with clearer scene details being recovered.

Synthetic aperture radar (SAR) is a kind of active imaging radar, which can obtain high-resolution wide-swath SAR images, especially for spaceborne SAR systems. In practical electromagnetic environment, due to the overlap of same frequency bands, spaceborne SAR is extremely vulnerable to interferences from other electromagnetic systems, called radio frequency interference (RFI) to SAR systems. RFI seriously reduces the imaging quality of the SAR system and causes resolution reduction and scene occluded. To mitigate RFI in SAR systems, researchers have proposed many methods, in which semi-parametric methods, such as robust principal component analysis (RPCA)-based methods, played important roles in strong RFI mitigation in recent years. However, it is observed that they may be hard to recover the true scene well under extremely strong RFIs since the strong scatterers are also mixed in the extracted low-rank interferences. Therefore, in this paper, we propose a novel adaptive method, which combines the advantages of both semi-parametric method and frequency domain notched filter (FNF) method, called adaptive notch semi-parametric (ANSP) method, where the FNF method, as a non-parametric method, can retain more true scenes when mitigating interferences. As a result, the proposed method can not only effectively deal with strong RFIs but also protect the strong scatterers better with an adaptive threshold. This method can recover the true scene under extremely strong RFI and be applied to both Level-0 and Level-1 SAR data. Finally, we conduct experiments on several real SAR data and demonstrate the effectiveness of the proposed method.

Full-duplex transmission can be easily implemented on a visible light communication (VLC) link. In this paper, we investigate the performance of a VLC network with full-duplex optical links. We propose two contention protocols, named U-ALOHA and FD-CSMA, to utilize the full-duplex capability effectively. Their performances in terms of channel utilization and network throughput are analyzed and simulated and compared with a protocol with half-duplex links. The results show that the proposed protocols can effectively exploit the full-duplex capability. U-ALOHA achieves high channel utilization on the downlink channel while FD-CSMA has good performance on both downlink and uplink channels. Both of them outperform the protocol with half-duplex links when the traffic load is sufficiently high.

The two well-known on-demand protocols for ad hoc networks - DSR and AODV show degraded performance with traffic load increase. In this paper, we propose a traffic-balanced scheme, called TB scheme, to solve this problem. The TB scheme adopts MAC layer information for routing decisions. It avoids involving heavy loaded nodes in a new route and thus balances network traffic. Simulation results show that the new scheme significantly improves DSR and AODV's performance under heavy load conditions.