We propose an alternating optimization (AO) approach to enhance the performance of energy-efficient multi-user visible light communication (MU-VLC) systems, which integrates both lighting and communication functions using multi-input multi-output (MIMO) technology and sub-array architecture. The rate-splitting multiple access (RSMA) technique is used to fully exploit the VLC channel as well as flexibly handle the interference and noise. Numerical results exhibit the superiority of the proposed scheme over the minimum mean squared error (MMSE) and successive interference cancellation (SIC) methods under various user quality of service (QoS) requirements. Moreover, the energy efficiency (EE) achieved by the proposed scheme surpasses that of existing VLC MIMO systems, highlighting its potential for green communication applications.

In underwater wireless optical communications (UWOC), channel model is crucial for system design and performance evaluation. The absorption and scattering of seawater may lead to energy loss and direction change of photons, introducing dispersion in both time and space domains, which complicates channel modeling. There have been many studies on single domain channel modeling, but few consider the joint modeling of time and space domains, especially in multiple-input multiple-output (MIMO) scenarios. In this paper, we consider UWOC MIMO links and propose a weighted delay exponential polynomial (WDEP) to model the joint spatial-temporal response of general UWOC MIMO links with arbitrary numbers of light sources and receivers. Numerical results show that the proposed WDEP model fits well with the Monte Carlo simulation results of UWOC MIMO links in turbid water environments including harbor water and coastal water.

Integrated sensing and communication (ISAC) is a key technology of next generation wireless communication. Backscatter communication (BackCom) plays an important role for internet of things (IoT). Then the integration of ISAC with BackCom technology enables low-power data transmission while enhancing the system sensing ability, which is expected to provide a potentially revolutionary solution for IoT applications. In this paper, we propose a novel backscatter-ISAC (B-ISAC) system and focus on the joint beamforming design for the system. We formulate the communication and sensing model of the B-ISAC system and derive the metrics of communication and sensing performance respectively, i.e., communication rate and detection probability. We propose a joint beamforming scheme aiming to optimize the communication rate under sensing constraint and power budget. A successive convex approximation (SCA) based algorithm and an iterative algorithm are developed for solving the complicated non-convex optimization problem. Numerical results validate the effectiveness of the proposed scheme and associated algorithms. The proposed B-ISAC system has broad application prospect in IoT scenarios.

More recently, unmanned aerial vehicle (UAV)-assisted full-duplex (FD) communication has been considered to enhance system capacity and reliability. Nevertheless, traditional UAV-assisted systems based on radio frequency (RF) face certain challenges, including limited bandwidth and inability to operate in RF-denied areas. In fact, the optical wireless communication (OWC) approach can effectively resolve the aforementioned issues, which is rarely employed in UAV-assisted systems. In this paper, we develop a UAV-assisted FD OWC system utilizing light-emitting diodes (LEDs) and vertical-cavity surface-emitting laser (VCSEL) arrays for downlink (DL) and uplink (UL) light sources to facilitate communications of ground users (GUs). To effectively allocate resources for GUs with different priorities, we introduce a priority-focused UAV trajectory planning approach for this system and apply deep reinforcement learning (DRL) techniques to determine UAV flight trajectories, thereby enhancing the DL communication capacity for each GU. Numerical results demonstrate that the proposed system improves the DL communication capacity of each GU, and successfully achieves the intended tilting of communication resources with the introduced priority sets.

In this paper, we apply non-orthogonal multiple access (NOMA) to unmanned aerial vehicle (UAV)-assisted visible light communication (VLC) systems, to accommodate the demand of multiple ground users. Specifically, we jointly optimize the trajectory planning of UAV, the grouping of users, and the allocation of power, to maximize the total rate of users. However, it is hard to solve this non-convexity and NP-hard problem by traditional optimization algorithms. Moreover, it is also difficult for deep reinforcement learning (DRL) algorithm to train due to the limitation of single-type action space. Therefore, we adopt a hybrid action space DRL (HA-DRL) algorithm to solve the problem, designing the grouping of users as discrete action, and the displacement and power allocation of UAV as continuous action. Numerical results show that the proposed algorithm can significantly improve the system performance by exploiting the user grouping operation in these UAV-assisted NOMA-VLC systems, and can achieve faster convergence and better performance than traditional single-type action DRL algorithms.

Most conventional optical OFDM (O-OFDM) schemes suffer from low spectral efficiency due to the interference introduced by time-domain signal processing. In this paper, we propose a novel design principle for layered O-OFDM schemes for intensity modulation with direct detection (IM/DD) optical wireless communication (OWC) to fully exploit the spectral resources. All layered O-OFDM schemes are designed to meet the constraint that the interference introduced by time-domain signal processing at each layer is designed mutually orthogonal to the frequency-domain signals at the current and lower layers. To satisfy this constraint, we discuss two approaches for time-domain signal processing and corresponding spectrum resource allocation, and present the subsequent transceiver design for the proposed layered O-OFDM schemes. The proposed layered O-OFDM schemes exhibit high spectral efficiencies, and numerical results demonstrate their low bit error rates (BERs) and high power efficiencies. Furthermore, the high flexibility of these schemes guided by the proposed design principle enables them to meet a wide range of diverse communication requirements.

The recently proposed dual-functional radar-communication (DFRC) system aims to save more resources by sharing the hardware platform and spectrum while providing users with both communication and sensing services. However, in previous works of DFRC systems, the issue of adaptively designing waveforms without prior information on targets in a dynamic environment has not been studied yet. In this paper, we propose a cognitive dual-functional radar-communication (CDFRC) system with closed-loop feedback to sense the targets and design waveform with a dynamic beampattern to address this issue. We construct a generalized likelihood ratio test (GLRT) target detection scheme to obtain information on targets and propose a cognitive waveform design method utilizing feedback information. Our proposed system can jointly optimize the multi-user interference (MUI) and detection probabilities of potential targets while ensuring the detection performance of detected targets. Numerical results indicate that, compared with previous work, our proposed system can effectively detect the target and adaptively design the waveform to ensure the ability of target detection and communication.

Lightness illusions, such as the seemingly opposing effects of brightness contrast and assimilation, are characterized by visually perceived intensity images that differ from physical reality. Traditional hypotheses from signal processing community primarily use filtering to explain these phenomena. However, these methods may fail due to the change in geometry (e.g., homography transform). In this paper, we attempt to explain lightness illusion from a novel Compressive Sensing perspective. The underlying mathematics is based on the new theory of compressive sensing, which provides an efficient method for sampling and reconstructing a signal that is sparse in Fourier domain. The sampling amounts to a random sampling of locally averaged values. Reconstruction amounts to solving an underdetermined linear equation system using L1norm minimization. The Accelerated Proximal Gradient (APG) method is used to reconstruct the compressed signal. We demonstrate that the reconstruction error can be used for robustly explaining well known lightness illusions.