Integrating the ocean segment is essential to realizing the sixth-generation (6G) vision of space-air-ground-ocean (SAGO) networks, yet progress remains constrained by limited connectivity and poor energy sustainability. To address these challenges, we develop an underwater omnidirectional simultaneous lightwave information and power transfer (SLIPT) architecture with two coupled components. First, we propose a harmonic-roundness framework for omnidirectional SLIPT cellular architecture, linking array geometry to coverage uniformity, capacity, and fairness. Second, using a photovoltaic-receiver model that characterizes energy harvesting and small-signal communication, we derive the energy-harvesting and communication Pareto frontier and its achievability proof. Together, these two components enable a practical underwater SLIPT cellular architecture for alignment-free simultaneous energy and data transfer. Experimentally, a hexagonal omnidirectional transmitter used as a cellular base station attains 98.15% roundness. With the omnidirectional base station and a photomultiplier tube receiver, a 5-m underwater link delivers 142.3 Mbps. With a 450-nm laser and a photovoltaic panel, a 5-m link achieves 100 Mbps while simultaneously harvesting 67.75 mW, an order-of-magnitude improvement over prior work.With the omnidirectional base station and a photovoltaic panel over a 0.5-m underwater channel, the link simultaneously sustains 13 Mbps and harvests 0.02 mW. An orthogonal frequency division multiple access (OFDMA) demon-stration with two users yields a Jain’s index of 0.85, providing initial experimental support for equitable multiuser performance. These results support harmonic-roundness-driven SLIPT with photovoltaic receivers as a promising cellular architecture for self-power underwater networking and as a potential building block for future SAGO 6G networks.

The increasing proliferation of internet-connected devices, together with the widespread reliance on wireless communication in daily activities, are limiting the capabilities of the radio frequency (RF) spectrum to meet data traffic demands. In particular, the rise in connectivity requirements to realize next generation networks, coupled with the growth in utilization of artificial intelligence (AI) and its associated transmission of massive amounts of data, contribute to the need to leverage the optical spectrum to complement the already-congested RF bands. Optical wireless connectivity encompasses a range of solutions that can support data transfer in various use cases, ranging from intra-chip interconnects to deep-space communication. They benefit from advantages in terms of operation within a broad unlicensed spectrum, minimization of RF interference, and inherent security. On the other hand, optical wireless communication (OWC) systems might experience a decline in performance as a result of factors like atmospheric conditions and link blockage. This requires customizing system designs according to the considered use cases and investigating new solutions for realizing optical wireless connectivity. In this article, we present an overview of OWC, encompassing both its conceptual frameworks and the scientific and technological advancements that hold the potential for shaping its future in next generation networks.

The delay-Doppler (DD) domain modulation has been regarded as one of the most competitive candidates to support wireless communications for emerging high-mobility applications in the sixth-generation mobile networks. Unfortunately, most of the existing designs for DD domain modulation suffer from high peak-to-average power ratio (PAPR) and unbearable detection complexity under uplink transmission since large time duration and bandwidth are required to guarantee high DD resolutions. To address these issues, the Doppler shift keying (DSK) modulation based on the orthogonal delay Doppler division multiplexing modulator is proposed in this paper, where the input-output characterization in the DD domain is fully exploited. The principle of the DSK transceiver is first established with the one-hot mapper and low-complexity iterative successive interference cancellation-maximum ratio combining detector for point-to-point scenarios. The proposed scheme is then generalized to the zero auto-correlation sequence-based implementation, which benefits the extension of multi-user (MU) uplink DSK frameworks. For uplink DSK transmission, Zadoff-Chu (ZC) sequences are adopted as the basis sequences. We optimize the assignment of ZC roots to different user equipments (UEs) by minimizing the maximum inter-user interference. This optimization process, which analyzes the root allocation, directly assigns a specific ZC sequence to each UE. The PAPR and bit error rate performance of the proposed DSK modulation with the low-complexity detector is finally verified by extensive simulation results under doubly-dispersive channels, which demonstrates the superiority of DSK modulation especially for uplink multiple access over doubly dispersive channels.

With the development of large-scale low Earth orbit satellite networks, multi-satellite collaboration has emerged as a critical paradigm for enhancing satellite network performance. In this article, a novel framework for multi-satellite collaboration with load balancing enabled by inter-satellite links is investigated. Specifically, the concept of this framework is first introduced and its necessity in dynamic communication scenarios is illustrated. Subsequently, the principal advantages of this framework are elucidated, demonstrating its superiority in different performance metrics including system throughput, user latency, and energy consumption. Moreover, key enabling technologies are investigated, including dynamic beam hopping, adaptive resource allocation, and queueing- based traffic control that collectively facilitate real-time load balancing. The proposed framework is quantitatively evaluated and benchmarked against representative baseline schemes. Finally, a discussion on future trends and challenges provides key insights and outlines research directions toward next-generation satellite networks.

Uncrewed aerial vehicles (UAVs) enable flexible network deployment but are vulnerable to detection due to the open-access nature of wireless signals. Visible light communication (VLC) emerges as a compelling technology for this context, offering inherent covertness through its reliance on line-of-sight links, while its wide spectrum supports high covert transmission rates. However, the high mobility of UAVs creates dynamic channel conditions that challenge traditional multiple access schemes. This motivates the utilization of rate-splitting multiple access (RSMA), a more robust and flexible framework for managing multi-user interference. Therefore, we investigate a cooperative, RSMA-based VLC system where a source UAV provides covert data transmission, assisted by a jamming UAV that creates power randomness to confuse wardens. By jointly optimizing the three-dimensional (3D) trajectory design and resource allocation of UAVs, we aim to maximize the minimum average covert transmission rate of users. To achieve this goal, the original problem is decomposed into three subproblems, namely horizontal coordinate arrangement, altitude adjustment, along with joint power assignment and rate-splitting, which are solved by the successive convex approximation method, geometric programming-based approach, and majorization-minimization algorithm, respectively. Numerical results validate the effectiveness of the proposed approach through extensive simulations under various parameters and comparative analyses against baselines, highlighting the potential of UAV-assisted visible light covert communications.

The growth of indoor terminals has put higher demands on wireless communications, which not only need to support high-rate communications, but are also required to provide power for energy-limited terminals. However, conventional wireless communication systems face challenges in meeting these demands due to the spectrum congestion and electromagnetic interference. Recently, visible light communication (VLC) has been emerged as a promising technology, which can take advantage of its ability to operate within the unlicensed spectra. Therefore, in this paper, VLC benefits from the facilitation of optical intelligent reflecting surface (OIRS) and non-orthogonal multiple access (NOMA) to support energy-constrained devices through simultaneous lightwave information and power transfer (SLIPT), which is well suited for future VLC systems. Moreover, an optimization framework is proposed for NOMA-based VLC systems with OIRS for SLIPT, and then the problem is formulated to minimize the total energy consumption with several key constraints. To solve this non-convex problem, we decompose it into four sub-problems, including the OIRS configuration, NOMA coefficient assignment, transmit power allocation, and direct current bias arrangement, which are solved iteratively by the block coordinate descent algorithm through relaxed iterative optimization, minorization-maximization algorithm, and successive convex approximation. Simulation results validate the convergence and effectiveness of the proposed algorithm, demonstrating the influence of OIRS unit number and other key factors on the system performance. These findings highlight the potential of OIRS to enhance the power transfer capabilities of future optical communications.

In practical visible light communication (VLC) networks, traffic demand exhibits strong temporal variations, necessitating the adaptive resource allocation to ensure both efficiency and fairness among users. Therefore, the dynamic resource optimization in an optical intelligent reflecting surface (OIRS)-assisted VLC network is investigated in this paper, considering time-varying user demands across time slots. The optimization is formulated as minimizing the long-term average total transmit power, and then is addressed via the per-slot power allocation and OIRS configuration with the Lyapunov optimization framework. Simulation results demonstrate that the proposed algorithm achieves substantial power savings, while the deployment of OIRS units further reduces the energy consumption and enhances the network adaptability.

The evolution of 6G networks is expected to support data-intensive applications, which impose energy demands on communication systems. In this paper, to address the challenge of sustainable power supply under dynamic traffic conditions, the integration of simultaneous lightwave information and power transfer (SLIPT) with optical intelligent reflecting surfaces (OIRS) is explored. Leveraging the large unlicensed spectrum of visible light communication (VLC), SLIPT offers a promising solution for combined high-speed data transmission and wireless power transfer. Specifically, we investigate adaptive resource allocation for the OIRS-aided SLIPT system, where the objective is to minimize the average total energy consumption under time-varying traffic. Therefore, a Lyapunov drift-plus-penalty method is utilized to transform the original problem and ensure system stability under stochastic traffic. To address the non-convex problem, a block coordinate descent framework is applied to decouple it into four sub-problems. First, the service association sub-problem is tackled by the Kuhn-Munkres algorithm; second, the power allocation is optimized via sequential convex programming; third, the power splitting factor is similarly derived; and finally, the OIRS configuration is updated via a minorization-maximization algorithm. Simulation results verify the convergence and effectiveness of the proposed algorithm, showing that the integration of OIRS significantly reduces the energy consumption while maintaining stable data transmission and energy supply. These findings highlight the potential of OIRS-aided SLIPT to provide sustainable and adaptive co-design of energy and information for future optical communication systems.

Integrated sensing and communication (ISAC) is a foundational usage scenario for the sixth-generation (6G) mobile communication system, with optical wireless (OW)-ISAC rapidly advancing as a compelling alternative to its radio-frequency counterpart. Among OW-ISAC technologies, frequency modulated continuous wave (FMCW)-based light detection and ranging (LiDAR) gains significant attention for its ability to achieve direct velocity estimation. In this paper, the channel capacity of FMCW-based OW-ISAC is analyzed to evaluate the influence of the sensing constraint on the communication performance. Firstly, the channel model of FMCW-based OW-ISAC is recast into an information-theoretic formulation, where an additional harmonic-mean constraint is imposed to ensure the sensing performance. Subsequently, the lower bound for channel capacity is given by the entropy-power inequality and the max-entropy distribution. In addition, the dual expression provides an upper bound for channel capacity, which converges to the same asymptotic expression as that of the lower bound in the high-signal-to-noise-ratio region. Numerical results illustrate the performance metrics of OW-ISAC and highlight the trade-off between its communication and sensing functionalities.

Continuous and precise monitoring of physiological signals via epidermal sensors could be of use in the development of personalized healthcare. However, the practical deployment of such sensors is hindered by the need for bulky batteries and limitations in data transfer. Here we report a battery-free epidermal network that is wirelessly interconnected through a wearable metamaterial and can provide continuous, high-fidelity biosensing. The network separates the power transfer (13.56 MHz) and data communication (2.4 GHz) channels through a dual-mode metamaterial textile, providing efficient wireless power transfer and low-latency data communication. We use a smartphone as a hub to wirelessly deliver power to and acquire biological signals from multiple networked epidermal sensors mediated by the metamaterial, which is integrated in clothing. The network can continuously monitor systolic blood pressure, including in dynamic environments such as during exercise.

The mega low-Earth-orbit (LEO) satellite constellation can achieve high throughput and low latency satellite communications, which is highlighted for next-generation communications. In this paper, the LEO satellite communications with coordinated beam hopping (BH) are investigated for throughput maximization and load balancing. Generally, the geographically non-uniform and time-varying packet arriving, along with the interference caused by dense beams, can present significant challenges. Hence, the proper BH pattern selection and dynamic power allocation are necessitated to enhance the throughput of satellite-terrestrial communications. Additionally, load balancing via inter-satellite links (ISLs) adjusts the queue length among satellites, thus indirectly altering the throughput by mitigating congestion. Therefore, an optimization problem is formulated to maximize long-term throughput by determining the BH pattern and dynamic power, while the problem for load balancing via ISLs is also proposed for stabilizing queues in a low congestion state to enhance the throughput. As both problems are inherently associated with each other through the queue length, they are solved in cycles. Specifically, the problem for long-term throughput is converted into decisions for each time slot with the Lyapunov drift-plus-penalty method, and then the BH pattern and power allocation are optimized alternately through successive convex approximation. Moreover, load balancing via ISLs is determined according to the queue length by minimizing the upper bound of Lyapunov drift, thereby decreasing the congestion of queues. Simulation results indicate that the proposed method can effectively improve the throughput and reduce the packet loss compared with the existing baselines, demonstrating the effectiveness of the Lyapunov optimization based method.

In this letter, optical intelligent reflecting surface (OIRS) is integrated into the visible light positioning (VLP) system to boost the positioning accuracy. Initially, we model the channel gains and the received signals as functions of the user locations, which facilitates the adoption of the maximum likelihood estimation approach to estimate the user positions. Subsequently, the Cram & eacute;r-Rao lower bound is derived for each user to theoretically characterize the positioning accuracy. Moreover, an OIRS alignment problem is formulated to minimize the lower bound of the mean squared error across all users, and then solved by the proposed iterative optimization algorithm based on the Schur complement condition. Finally, simulation results validate the effectiveness of the proposed algorithm and demonstrate the significant performance improvements achieved with OIRS assistance.

Optical wireless integrated sensing and communication (OW-ISAC) is rapidly burgeoning as a complement and augmentation to its radio-frequency counterpart. In this paper, the achievable rate is analyzed to guide the envelope design of a coherent OW-ISAC system based on frequency-modulated continuous wave (FMCW). First, an ambiguity-function-based sensing algorithm is developed in the FMCW-based OW-ISAC system, where a harmonic-mean constraint on the transmitted envelope quantifies the coherent sensing performance. Subsequently, by defining a well-posed mathematical channel subject to the harmonic-mean constraint, lower and upper bounds for the sensing-constrained achievable rate are derived, based on which asymptotic expressions are presented for both low and high signal-to-noise-ratio (SNR) regions. Moreover, the analysis of achievable rate guides the FMCW envelope design based on pulse-amplitude modulation (PAM), whose rate-approaching capabilities are demonstrated by numerical results. Furthermore, simulations reveal the trade-off between communication and sensing functionalities. In summary, the analysis of sensing-constrained achievable rate provides insights into both the optimality and the practicality of OW-ISAC system design.

The endurance of unmanned aerial vehicles (UAVs) is usually restricted by their limited onboard energy, thus posing challenges to sustainable operations. To address this challenge, we investigate a UAV-assisted mobile relay system enabled by simultaneous lightwave information and power transfer (SLIPT), where free-space optical links jointly provide high-speed data transmission and energy replenishment. Moreover, considering the diverse spatial distribution of users, a max–min fairness optimization problem is formulated to balance communication performance, energy harvesting efficiency, and UAV mobility. Then, the resulting mixed-integer nonlinear problem is decomposed into three more tractable subproblems and solved iteratively. In addition, simulation results demonstrate the convergence of the proposed algorithm, and highlight its superior fairness performance compared with existing schemes in the literature.

Silicon photomultiplier (SiPM) facilitates high-performance visible light communication (VLC) with exceptional sensitivity, but its nonlinear response poses a significant challenge for non-orthogonal multiple access (NOMA) integration. Our analysis reveals that NOMA’s superiority over orthogonal multiple access (OMA) is conditional under this nonlinearity. To mitigate this impact, an adaptive NOMA/OMA switching mechanism is devised. For multi-user extension, a two-step resource allocation scheme is proposed. It leverages the switching mechanism with Stackelberg game-based user pairing, followed by an alternating optimization for time allocation that balances the achievable rate and coverage ratio. Simulation results validate the effectiveness of the proposed scheme.

A novel channel modeling framework for visible light communication (VLC) is proposed by introducing ray-tracing operators from computer vision into optical wireless simulations. Built on the NVIDIA Falcor platform, the framework incorporates communication-related physical quantities such as delay, power, and wavelength, while employing next event estimation (NEE) and multiple importance sampling (MIS) to achieve unbiased path estimation with high efficiency. The proposed method is validated against MATLAB simulations, Zemax, and real-world measurements. Results demonstrate that the approach provides accuracy comparable to conventional methods while offering significant computational speedup, highlighting its potential as a scalable tool for realistic VLC channel modeling and simulation.

Due to the widespread use of Low-Density Parity-Check (LDPC) codes in modern wireless communication and broadcasting standards, the architectures of their decoders have been extensively discussed in the literature. In this paper, we propose a unified decoding framework for pipelined layered quasi-cyclic LDPC decoders, which encompasses various decoder architectures as special cases, each corresponding to a specific parameter setting within a three-dimensional parallel architecture. Based on this framework, we derive an optimal grid re-scheduling method to mitigate memory conflicts between layers under a fixed decoding order. We also explore the impact of both adjacent check nodes and previously processed check nodes on the number of inserted NOPs, which leads to a reformulation of the scheduling problem as an episodic Markov decision process. To solve this problem, we apply classical heuristic algorithms, such as genetic programming and Beam Search, and propose a Deep Reinforcement Learning aided approach to further enhance the performance. Experimental results demonstrate that our proposed method outperforms traditional approaches in terms of memory conflict resolution.

Ultraviolet (UV) optical wireless communication (OWC) is promising for non-line-of-sight (NLOS) links for its robustness against interference. To overcome the limited output power and clipping distortion of the single UV LED, multi-LED transmitters are employed with DC-biased optical OFDM (DCO OFDM) and linear pre-equalization (pre-EQ). Unlike analyses that assume Gaussianity based on subcarrier count and independent and identically distributed (i.i.d). conditions, this paper derives a transmitter side model that is independent of the Gaussian assumption and generally applicable to such systems. We first derive an analytical expression for the probability density function (PDF) of the pre-equalized DCO-OFDM signal via the characteristic function method, accurately capturing its non-Gaussian character. By incorporating a clipping model for UV LEDs, an analytical BER expression is subsequently derived. As an evaluation model, its calculated results are similar to Monte Carlo simulation with significantly higher efficiency. Based on the theoretical derivation, we propose a spatial diverse pre-equalization (SDP) strategy that distributes pre-EQ coefficients across multiple LEDs to mitigate clipping distortion. While SDP achieves only a modest peak to average power ratio (PAPR) reduction (∼0.5 dB), the substantial suppression of tail probability leads to a significant BER improvement (from 4×10⁻⁴ to 2.4×10⁻⁴). Calculated results indicate that optimal BER performance with SDP can be achieved using 5 LEDs. Experimental results obtained using multiple UV LEDs are consistent with the theoretical calculations.

The escalating demands for software-defined vehicles have prompted advancements in automated driving, facilitating a shift toward a novel chassis domain electronics architecture equipped with control-by-wire actuators. How to bolster driving dynamics and stability performance via chassis domain control represents a significant and challenging endeavor. This article presents a novel dual-layer adaptive active front steering and electronic stability control (AFS-ESC) coordinated control architecture based on the robust nonsingular fast terminal sliding mode control (NFTSMC) method, aimed at stabilizing the vehicle and enhancing the maneuverability. Initially, the robust NFTSMC theory is rigorously derived and analyzed for uncertain nonlinear multiple input multiple output systems. It is then applied to develop yaw rate-based maneuverability and side-slip angle-based stability decision-making using vehicle and tire models with unknown nonlinearities, uncertainties, and disturbances. Additionally, an adaptive gain based on a stability index is introduced to coordinate the decision-making outcomes. Subsequently, the requisite total yaw-moment is allocated to the front wheel steering angle and wheel cylinder pressures through AFS and ESC, respectively, based on an adaptive semiempirical tire nonlinearity index. The efficacy of the proposed architecture is validated via 0.7 Hz sine with dwell tests with high, medium, and low tire-road adhesion on the hardware-in-the-loop platform, demonstrating its superior performance in stabilizing the vehicle and enhancing maneuverability compared to the robust control-based and NFTSMC-based AFS-ESC coordination, ESC, AFS, and uncontrolled cases.