Papers

This paper presents the design and deployment of a process-aware cyber-physical system that automates plant-level logistics, traceability, and sanitation in a centralized maple-syrup boiling center. The system replaces ad-hoc, manual operations with event-driven orchestration on a local server, employing reusable device abstractions and a centralized interlock with priority-based arbitration for shared piping. It implements deterministic routines for delivery, reverse osmosis integration, evaporator feed, and permeate management. The system is sensor rich: inline measurements of flow, temperature, and sugar concentration (degrees Brix) drive routing decisions and trigger systematic post-transfer rinses (cleaning-in-place), ensuring consistent hygiene and complete, immediate traceability up to the evaporator inlet. During the 2025 production season, the system queued 431 operations without incident; executed 908 \enquote{topstock} and \enquote{downstock} balancing cycles; increased usable permeate reserves from 22,712 to approximately 41,640 L through dynamic storage assignment; eliminated mid-season contamination incidents previously observed under manual practice; and reduced administrative effort for billing and reporting from more than 30 hours to roughly 1 hour through automatic documentation. These results demonstrate a practical path to modular, plant-scale automation beyond traditional architectures, and lay the groundwork for packaging reusable elements for similar plants or adjacent industries. This work is part of a larger project involving the first scientifically-documented integration of Industry 4.0 technologies in a maple syrup boiling center.

In real-world singing voice conversion (SVC) applications, environmental noise and the demand for expressive output pose significant challenges. Conventional methods, however, are typically designed without accounting for real deployment scenarios, as both training and inference usually rely on clean data. This mismatch hinders practical use, given the inevitable presence of diverse noise sources and artifacts from music separation. To tackle these issues, we propose R2-SVC, a robust and expressive SVC framework. First, we introduce simulation-based robustness enhancement through random fundamental frequency ($F_0$) perturbations and music separation artifact simulations (e.g., reverberation, echo), substantially improving performance under noisy conditions. Second, we enrich speaker representation using domain-specific singing data: alongside clean vocals, we incorporate DNSMOS-filtered separated vocals and public singing corpora, enabling the model to preserve speaker timbre while capturing singing style nuances. Third, we integrate the Neural Source-Filter (NSF) model to explicitly represent harmonic and noise components, enhancing the naturalness and controllability of converted singing. R2-SVC achieves state-of-the-art results on multiple SVC benchmarks under both clean and noisy conditions.

Global Navigation Satellite System (GNSS) signals are vulnerable to jamming, particularly in urban areas where multipath and shadowing distort received power. Previous data-driven approaches achieved reasonable localization but poorly reconstructed the received signal strength (RSS) field due to limited spatial context. We propose a hybrid Bayesian mixture-of-experts framework that fuses a physical path-loss (PL) model and a convolutional neural network (CNN) through log-linear pooling. The PL expert ensures physical consistency, while the CNN leverages building-height maps to capture urban propagation effects. Bayesian inference with Laplace approximation provides posterior uncertainty over both the jammer position and RSS field. Experiments on urban ray-tracing data show that localization accuracy improves and uncertainty decreases with more training points, while uncertainty concentrates near the jammer and along urban canyons where propagation is most sensitive.

In this paper, we introduce a decentralized optimization-based density controller designed to enforce set invariance constraints in multi-robot systems. By designing a decentralized control barrier function, we derived sufficient conditions under which local safety constraints guarantee global safety. We account for localization and motion noise explicitly by modeling robots as spatial probability density functions governed by the Fokker-Planck equation. Compared to traditional centralized approaches, our controller requires less computational and communication power, making it more suitable for deployment in situations where perfect communication and localization are impractical. The controller is validated through simulations and experiments with four quadcopters.

Achieving immersive auditory experiences in virtual environments requires flexible sound modeling that supports dynamic source positions. In this paper, we introduce a task called resounding, which aims to estimate room impulse responses at arbitrary emitter location from a sparse set of measured emitter positions, analogous to the relighting problem in vision. We leverage the reciprocity property and introduce Versa, a physics-inspired approach to facilitating acoustic field learning. Our method creates physically valid samples with dense virtual emitter positions by exchanging emitter and listener poses. We also identify challenges in deploying reciprocity due to emitter/listener gain patterns and propose a self-supervised learning approach to address them. Results show that Versa substantially improve the performance of acoustic field learning on both simulated and real-world datasets across different metrics. Perceptual user studies show that Versa can greatly improve the immersive spatial sound experience. Code, dataset and demo videos are available on the project website: https://waves.seas.upenn.edu/projects/versa.

Fluid antenna is a promising wireless communication technology that enhances communication rate by changing the antenna positions. This article proposes a new communication system that combines multiple-input single-output (MISO) fluid antennas with traditional fixed-position antennas, utilizing antenna position optimization to improve energy harvesting efficiency. In this model, we consider simultaneous wireless information and power transfer (SWIPT) which transmits identical signals from the base station to both information receiver (IR) and energy receiver (ER). We strive to enhance the power delivered to the ER by fine-tuning the positions of transmit and receive fluid antennas, along with optimizing the transmit covariance matrix, subject to a given minimum signal-to-interference-plus-noise ratio (SINR) constraint at the IR. Simulation results indicate that fluid antenna systems significantly enhance the energy harvesting efficiency of the ER compared to traditional fixed-position antennas.

Conditional differential entropy provides an intuitive measure for relatively ranking time-series complexity by quantifying uncertainty in future observations given past context. However, its direct computation for high-dimensional processes from unknown distributions is often intractable. This paper builds on the information theoretic prediction error bounds established by Fang et al. \cite{fang2019generic}, which demonstrate that the conditional differential entropy \textbf{$h(X_k \mid X_{k-1},...,X_{k-m})$} is upper bounded by a function of the determinant of the covariance matrix of next-step prediction errors for any next step prediction model. We add to this theoretical framework by further increasing this bound by leveraging Hadamard's inequality and the positive semi-definite property of covariance matrices. To see if these bounds can be used to rank the complexity of time series, we conducted two synthetic experiments: (1) controlled linear autoregressive processes with additive Gaussian noise, where we compare ordinary least squares prediction error entropy proxies to the true entropies of various additive noises, and (2) a complexity ranking task of bio-inspired synthetic audio data with unknown entropy, where neural network prediction errors are used to recover the known complexity ordering. This framework provides a computationally tractable method for time-series complexity ranking using prediction errors from next-step prediction models, that maintains a theoretical foundation in information theory.

The increasing share of converter based resources in power systems calls for scalable methods to analyse stability without relying on exhaustive system wide simulations. Decentralized small gain and small-phase criteria have recently been proposed for this purpose, but their applicability to grid forming converters is severely limited by the sectoriality assumption, which is not typically satisfied at low frequencies. This work revisits and extends mixed gain phase conditions by introducing loop shaping transformations that reformulate converter and network models in alternative coordinate frames. The proposed approach resolves intrinsic non sectoriality at low frequencies and reduces conservativeness, thereby improving the applicability of decentralized stability certification. Analytical results are illustrated using an infinite bus system first and then extended to the IEEE 14 bus network, demonstrating the practicality and scalability of the method. These findings provide a pathway toward less conservative and more widely applicable decentralized stability certificates in power grids.

We analyze the identifiability of nonlinear networks with non necessarily additive node dynamics, where the influence of in-neighbors is represented by a multivariate nonlinear function that cannot necessarily be separated into individual edge functions. We consider the full measurement case (all the nodes are measured) and we introduce the notion of generic identifiability. Based on a generic nonlinear matrix associated with an unfolded digraph constructed from the network, we characterize the space of functions that satisfies the generic property. For directed acyclic graphs (DAGs) composed of analytic functions, we derive a sufficient condition for identifiability based on vertex-disjoint paths from excited nodes to the in-neighbors of each node in the network. Furthermore, for the class of polynomial functions, by using well-known results on algebraic varieties, we prove that the identifiability is impossible if the vertex-disjoint path condition is not satisfied. Finally, we show that this identifiability condition is not necessary for the additive nonlinear model, where the node function can be decomposed into a sum of edge-wise nonlinearities.

Low Earth orbit (LEO) satellite networks have shown strategic superiority in maritime communications, assisting in establishing signal transmissions from shore to ship through space-based links. Traditional performance modeling based on multiple circular orbits is challenging to characterize large-scale LEO satellite constellations, thus requiring a tractable approach to accurately evaluate the network performance. In this paper, we propose a theoretical framework for an LEO satellite-aided shore-to-ship communication network (LEO-SSCN), where LEO satellites are distributed as a binomial point process (BPP) on a specific spherical surface. The framework aims to obtain the end-to-end transmission performance by considering signal transmissions through either a marine link or a space link subject to Rician or Shadowed Rician fading, respectively. Due to the indeterminate position of the serving satellite, accurately modeling the distance from the serving satellite to the destination ship becomes intractable. To address this issue, we propose a distance approximation approach. Then, by approximation and incorporating a threshold-based communication scheme, we leverage stochastic geometry to derive analytical expressions of end-to-end transmission success probability and average transmission rate capacity. Extensive numerical results verify the accuracy of the analysis and demonstrate the effect of key parameters on the performance of LEO-SSCN.

Precoding design based on weighted sum-rate (WSR) maximization is a fundamental problem in downlink multi-user multiple-input multiple-output (MU-MIMO) systems. While the weighted minimum mean-square error (WMMSE) algorithm is a standard solution, its high computational complexity–cubic in the number of base station antennas due to matrix inversions–hinders its application in latency-sensitive scenarios. To address this limitation, we propose a highly parallel algorithm based on a block coordinate descent framework. Our key innovation lies in updating the precoding matrix via block coordinate gradient descent, which avoids matrix inversions and relies solely on matrix multiplications, making it exceptionally amenable to GPU acceleration. We prove that the proposed algorithm converges to a stationary point of the WSR maximization problem. Furthermore, we introduce a two-stage warm-start strategy grounded in the sum mean-square error (MSE) minimization problem to accelerate convergence. We refer to our method as the Accelerated Mixed weighted-unweighted sum-MSE minimization (A-MMMSE) algorithm. Simulation results demonstrate that A-MMMSE matches the WSR performance of both conventional WMMSE and its enhanced variant, reduced-WMMSE, while achieving a substantial reduction in computational time across diverse system configurations.

Dynamic metasurface antennas (DMAs) represent a novel approach to programmable and affordable electromagnetic wave manipulation for enhanced wireless communications, sensing, and imaging applications. Nevertheless, current DMA designs and models are usually quasi-narrowband, neglecting the versatile frequency-diverse manifestation and its utilization. This work demonstrates the frequency-diversity and dispersion operations of a representative DMA structure at the millimeter-wave band. We demonstrate flexible dispersion manipulation through dynamic holographic reconfigurability of the meta-atoms in a DMA. This effect can create distinct radiation patterns across the operating frequency band, achieving flexible frequency diversity with enhanced scanning range within a compact, reconfigurable platform. It eliminates the need for wideband systems or complex phase-shifting networks while offering an alternative to frequency-scanned static beams of traditional leaky-wave antennas. The results establish fundamental insights into modelling and utilization of dispersive effects of DMAs in next-generation near-field and far-field holographic sensing and computational holographic imaging applications.

Devices within the marine Internet of Things (MIoT) can connect to low Earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs) to facilitate low-latency data transmission and execution, as well as enhanced-capacity data storage. However, without proper traffic handling strategy, it is still difficult to effectively meet the low-latency requirements. In this paper, we consider a cooperative satellite-aerial-MIoT network (CSAMN) for maritime edge computing and maritime data storage to prioritize delay-sensitive (DS) tasks by employing mobile edge computing, while handling delay-tolerant (DT) tasks via the store-carry-forward method. Considering the delay constraints of DS tasks, we formulate a constrained joint optimization problem of maximizing satellite-collected data volume while minimizing system energy consumption by controlling four interdependent variables, including the transmit power of UAVs for DS tasks, the start time of DT tasks, computing resource allocation, and offloading ratio. To solve this non-convex and non-linear problem, we propose a joint computation offloading and resource management (JCORM) algorithm using the Dinkelbach method and linear programming. Our results show that the volume of data collected by the proposed JCORM algorithm can be increased by up to 41.5% compared to baselines. Moreover, JCORM algorithm achieves a dramatic reduction in computational time, from a maximum of 318.21 seconds down to just 0.16 seconds per experiment, making it highly suitable for real-time maritime applications.

Predicting the motion of surrounding vehicles is key to safe autonomous driving, especially in unstructured environments without prior information. This paper proposes a novel online method to accurately predict the occupancy sets of surrounding vehicles based solely on motion observations. The approach is divided into two stages: first, an Extended Kalman Filter and a Linear Programming (LP) problem are used to estimate a compact zonotopic set of control actions; then, a reachability analysis propagates this set to predict future occupancy. The effectiveness of the method has been validated through simulations in an urban environment, showing accurate and compact predictions without relying on prior assumptions or prior training data.

This work is concerned with the coordination gain in integrated sensing and communication (ISAC) systems under a compress-and-estimate (CE) framework, wherein inference performance is leveraged as the key metric. To enable tractable transceiver design and resource optimization, we characterize inference performance via an error probability bound as a monotonic function of the discriminant gain (DG). This raises the natural question of whether maximizing DG, rather than minimizing mean squared error (MSE), can yield better inference performance. Closed-form solutions for DG-optimal and MSE-optimal transceiver designs are derived, revealing water-filling-type structures and explicit sensing and communication (S&C) tradeoff. Numerical experiments confirm that DG-optimal design achieves more power-efficient transmission, especially in the low signal-to-noise ratio (SNR) regime, by selectively allocating power to informative features and thus saving transmit power for sensing.

We present a multifunctional, antenna-integrated capacitive sensing (MAiCaS) platform for passive, wireless, and real-time cardiovascular monitoring. Unlike conventional systems that require separate sensors and wireless modules, our device unifies sensing, telemetry, and mechanical functionality into a compact and scalable design by exploiting the parasitic capacitance of an inductive antenna as a strain-sensitive element. The sensor is fabricated using a cleanroom-free, single-step UV laser patterning process on a flexible PDMS substrate, reducing manufacturing complexity and enabling high reproducibility. The MAiCaS is suitable for three different applications: as a sensor for epicardial strain measurement, a stent as a sensor, and a vascular graft sensor. We demonstrate MAiCaS's versatility by validating its wireless resonance-based response to strain, pressure, and deformation across unrolled and rolled forms. In vitro experiments demonstrated consistent resonance frequency shifts under physiological conditions, with stable performance on skin, in PBS, human serum, and simulated vascular environments. Repeatability and aging tests confirmed its long-term reliability and elasticity under cyclic loading. Calibration curves revealed high sensitivity across all configurations, with wireless interrogation achieved through S11 parameter measurements and resonance frequency shift as the output metric. The sensitivity of the device was measured to be 2.9 MHz per 1% strain, 0.43 MHz/mmHg, and 309.6kHz/\textmu m for epicardial patch, graft, and stent integrated sensor, respectively. The operation of MAiCaS was evaluated in a human experiment. This monolithic sensor architecture provides a scalable and cost-effective solution for battery-free monitoring of vascular dynamics, with potential for remote diagnostics, post-surgical follow-up, and continuous cardiovascular health management.

Autonomous control of multi-stage industrial processes requires both local specialization and global coordination. Reinforcement learning (RL) offers a promising approach, but its industrial adoption remains limited due to challenges such as reward design, modularity, and action space management. Many academic benchmarks differ markedly from industrial control problems, limiting their transferability to real-world applications. This study introduces an enhanced industry-inspired benchmark environment that combines tasks from two existing benchmarks, SortingEnv and ContainerGym, into a sequential recycling scenario with sorting and pressing operations. We evaluate two control strategies: a modular architecture with specialized agents and a monolithic agent governing the full system, while also analyzing the impact of action masking. Our experiments show that without action masking, agents struggle to learn effective policies, with the modular architecture performing better. When action masking is applied, both architectures improve substantially, and the performance gap narrows considerably. These results highlight the decisive role of action space constraints and suggest that the advantages of specialization diminish as action complexity is reduced. The proposed benchmark thus provides a valuable testbed for exploring practical and robust multi-agent RL solutions in industrial automation, while contributing to the ongoing debate on centralization versus specialization.

The main goal of this contribution is to explain how to use interlacing techniques for LTI controllers implementation and analyze different struc- tures in this environment. These considerations lead to an important com- putation saving in constrained resource environments. It has been also intro- duced new procedures for obtaining the blocks related to different real and complex controllers poles. The resultant time-varying system is modeled using proper discrete lifting techniques and a new and efficient dual-rate fre- quency response computation allows to determine the characteristics of the control loop with interlaced controller. Examples illustrate the theoretical proposals.

This paper presents a critical review of methods commonly employed in the literature for small signal stability analysis of inverter based resources (IBRs). It discusses the intended purposes of these methods and outlines both their proper and improper implementations. The paper provides insights into the applicability of these techniques, clarifies their inherent limitations, and discusses and illustrates common sources of misinterpretation.

Efficient medium access control (MAC) is critical for enabling low-latency and reliable communication in industrial Machine-to-Machine (M2M) net-works, where timely data delivery is essential for seamless operation. The presence of multi-priority data in high-risk industrial environments further adds to the challenges. The development of tens of MAC schemes over the past decade often makes it a tough choice to deploy the most efficient solu-tion. Therefore, a comprehensive cross-comparison of major MAC protocols across a range of performance parameters appears necessary to gain deeper insights into their relative strengths and limitations. This paper presents a comparison of Contention window-based MAC scheme BoP-MAC with a fragmentation based, FROG-MAC; both protocols focus on reducing the delay for higher priority traffic, while taking a diverse approach. BoP-MAC assigns a differentiated back-off value to the multi-priority traffic, whereas FROG-MAC enables early transmission of higher-priority packets by fragmenting lower-priority traffic. Simulations were performed on Contiki by varying the number of nodes for two traffic priorities. It has been shown that when work-ing with multi-priority heterogenous data in the industrial environment, FROG-MAC results better both in terms of delay and throughput.

We present AttDet, a Transformer-inspired MIMO (Multiple Input Multiple Output) detection method that treats each transmit layer as a token and learns inter-stream interference via a lightweight self-attention mechanism. Queries and keys are derived directly from the estimated channel matrix, so attention scores quantify channel correlation. Values are initialized by matched-filter outputs and iteratively refined. The AttDet design combines model-based interpretability with data-driven flexibility. We demonstrate through link-level simulations under realistic 5G channel models and high-order, mixed QAM modulation and coding schemes, that AttDet can approach near-optimal BER/BLER (Bit Error Rate/Block Error Rate) performance while maintaining predictable, polynomial complexity.

Pixel-based reconfigurable intelligent surfaces (RISs) employ a novel design to achieve high reflection gain at a lower hardware cost by eliminating the phase shifters used in traditional RIS. However, this design presents challenges for channel estimation and passive beamforming due to its non-separable state response, rendering existing solutions ineffective. To address this, we first approximate the non-separable RIS response functions using a kernel-based method and a deep neural network, achieving high accuracy while reducing computational and memory complexity. Next, we propose a simplified cascaded channel model that focuses on dominated scattering paths with limited unknown parameters, along with customized algorithms to estimate short-term and long-term parameters separately. Finally, we introduce a low-complexity passive beamforming algorithm to configure the discrete RIS state vector, maximizing the achievable rate. Our simulation results demonstrate that the proposed solution significantly outperforms various baselines across a wide SNR range.

This paper proposes a multi-actor coordination platform for the optimal utilization of smart buildings resources, including roof top PV generation and battery energy storage system (BESS), in active power distribution systems. The proposed multi-actor coordination includes the Smart Building Coordinator (SBC), Micro-Grid Coordinator (MGC) and Distribution System Coordinator (DSC). The coordinators operate independently and only exchange limited information with each other to reach an optimal solution. In the proposed platform, a hierarchical optimization problem is solved to optimally determine the operating point of all distribution system resources. The proposed platform fully preserves the confidentiality of the behind the meter (BTM) data of the buildings since no information about the status of the PV system, BESS, and load of the building is shared with the owner of the power system. The proposed platform has a flexible and scalable architecture where the computational task of coordinating microgrids and smart buildings with distribution grid is performed locally at the MGC and SBC layers, respectively. Numerical simulations show the efficacy of the proposed platform in coordinating the BTM resources with the rest of the distribution system.

This paper investigates the design of channel estimation and 3D localization algorithms in a challenging scenario, where a sub-connected planar extremely large-scale multiple-input multiple-output (XL-MIMO) communicates with multi-antenna users. In the near field, the uplink MIMO channel is of full column rank and therefore can not be estimated effectively by applying existing codebooks that are designed for the far-field case or for the near-field case but limited to single antenna users. To solve this problem, we propose a three-stage algorithm aided by orthogonal matching pursuit (OMP) and sparse Bayesian learning (SBL). Specifically, we firstly partition the XL-MIMO into subarrays and use OMP to solve the compressed sensing (CS) problem about subarray channel estimation with the Discrete Fourier Transform (DFT)-based dictionary matrix. Secondly, exploiting the estimated subarray channels and employing one-dimensional multiple signal classification (MUSIC), we estimate the central location of the user array under the Least Squares (LS) criterion. Finally, we utilize the estimated central location to construct a refined location-aided dictionary matrix and obtain the MIMO channel estimation using SBL. Results exhibit the significant superiority of the proposed algorithm compared with several benchmarks, in terms of both the pilot overhead and estimation accuracy.

Image dehazing is a restoration task that aims to recover a clear image from a single hazy input. Traditional approaches rely on statistical priors and the physics-based atmospheric scattering model to reconstruct the haze-free image. While recent state-of-the-art methods are predominantly based on deep learning architectures, these models often involve high computational costs and large parameter sizes, making them unsuitable for resource-constrained devices. In this work, we propose GUSL-Dehaze, a Green U-Shaped Learning approach to image dehazing. Our method integrates a physics-based model with a green learning (GL) framework, offering a lightweight, transparent alternative to conventional deep learning techniques. Unlike neural network-based solutions, GUSL-Dehaze completely avoids deep learning. Instead, we begin with an initial dehazing step using a modified Dark Channel Prior (DCP), which is followed by a green learning pipeline implemented through a U-shaped architecture. This architecture employs unsupervised representation learning for effective feature extraction, together with feature-engineering techniques such as the Relevant Feature Test (RFT) and the Least-Squares Normal Transform (LNT) to maintain a compact model size. Finally, the dehazed image is obtained via a transparent supervised learning strategy. GUSL-Dehaze significantly reduces parameter count while ensuring mathematical interpretability and achieving performance on par with state-of-the-art deep learning models.

Orthogonal time frequency space (OTFS) is a two-dimensional modulation technique that uses the delay-Doppler (DD) domain and is a candidate for providing robust, high-capacity wireless communications for envisioned 6G and beyond networks. The OTFS technique maps data to the DD domain instead of the traditional time-frequency domain, enabling it to fully utilize channel diversity and transform fast time-varying channels into nearly static channels. Index modulation (IM) is a communication paradigm that conveys information not only through conventional modulation symbols but also by encoding data bits in the indices of the selected communication resources to improve error performance, spectral efficiency, and energy efficiency. In this survey, a comprehensive review of work on OTFS-based wireless communication systems is presented. In particular, the existing OTFS-IM schemes are reviewed and systematically categorized according to their system architectures, detection methods, and performance aspects such as capacity, peak-to-average power ratio, diversity, complexity, imperfect channel state information, spectral efficiency, and outage probability. Furthermore, the operating principles and system models of OTFS-IM variants-including OTFS-based space shift keying, OTFS-based spatial modulation, OTFS-based quadrature spatial modulation, OTFS-based media-based modulation, and OTFS-based code index modulation-are described, followed by a comparative performance analysis in terms of computational complexity, error performance, capacity, energy saving, spectral efficiency, and throughput. Finally, the challenges, benefits, and future directions for OTFS-IM systems are discussed, covering key aspects such as complexity, efficiency, latency, channel estimation, hardware constraints, synchronization, security, and potential integration with other advanced wireless communication techniques.

Spatial filtering with a desired directivity pattern is advantageous for many audio applications. In this work, we propose neural directional filtering with user-defined directivity patterns (UNDF), which enables spatial filtering based on directivity patterns that users can define during inference. To achieve this, we propose a DNN architecture that integrates feature-wise linear modulation (FiLM), allowing user-defined patterns to serve as conditioning inputs. Through analysis, we demonstrate that the FiLM-based architecture enables the UNDF to generalize to unseen user-defined patterns during interference with higher directivities, scaling variations, and different steering directions. Furthermore, we progressively refine training strategies to enhance pattern approximation and enable UNDF to approximate irregular shapes. Lastly, experimental comparisons show that UNDF outperforms conventional methods.

We present a collection of different types of observation systems that work as differentiators. These observer-based differentiators can produce estimates for derivatives of a given signal, even though the given signal is prone to noise.

Rapidly increasing demand for high speed data is pushing 6G wireless networks to support larger link scales, lower latency, and higher spectral efficiency. Visible light communications (VLC) is a strong complement to radio frequency (RF) systems within 6G. The latest ITU G.9991 and IEEE 802.11bb standards are adapted from cable and RF wireless technologies for use in VLC, so they do not fully exploit the optical nature of light links. VLC links are often asymmetric between uplink and downlink, which makes TDMA style protocols inefficient when many users generate bursty and asymmetric traffic. Compared with RF, the strong directionality and frequent line of sight in VLC can mitigate hidden and exposed terminals, yet these effects can still appear under limited field of view, blockage, or reflections. CSMA/CA and related methods remain usable in VLC and in RF plus VLC networks, but they usually need design tweaks such as RTS/CTS or directional sensing to perform well. Although the optical spectrum is vast, the bandwidth of practical LEDs and of common PIN or APD receivers is limited, so efficient multiple access can yield large gains. This motivates a clean slate design for VLC, especially at the MAC layer. NOMA, first explored in 5G RF systems, is also promising for 6G VLC. It lets multiple users share the same time and frequency resources while tolerating controlled interference. This paper reviews progress in VLC and in NOMA based VLC, outlines key optimization constraints and objectives, surveys scenarios that fit NOMA in VLC, and points to several directions for future work.

Control barrier functions (CBFs) have a well-established theory in Euclidean spaces, yet still lack general formulations and constructive synthesis tools for systems evolving on manifolds common in robotics and aerospace applications. In this paper, we develop a general theory of geometric CBFs on bundles and, for control-affine systems, recover the standard optimization-based CBF controllers and their smooth analogues. Then, by generalizing kinetic energy-based CBF backstepping to Riemannian manifolds, we provide a constructive CBF synthesis technique for geometric mechanical systems, as well as easily verifiable conditions under which it succeeds. Further, this technique utilizes mechanical structure to avoid computations on higher-order tangent bundles. We demonstrate its application to an underactuated satellite on SO(3).