arXiv subjects

Qiyu Ma

Publications and source records attributed to Qiyu Ma.

3 recordsLinked to original sources

arXiv2026

Integrated Discovery and State-Aware Servicing for Mobile AUVs With UOWC: Modeling and Performance Analysis

Underwater wireless optical communication (UWOC) is an enabling technology for high-throughput subsea networks, yet its long-term deployment is constrained by the finite energy budget of underwater nodes. To address this challenge, we investigate a mobile system wherein an autonomous underwater vehicle (AUV) performs joint wireless information transfer (WIT) and wireless power transfer (WPT) for a network of randomly distributed sensor nodes. This paper develops \textcolor{blue}{an integrated mission-level framework} that combines stochastic node discovery with state-aware servicing. First, we present an analytical model for node discovery based on a signal-to-noise ratio (SNR) analysis, deriving performance metrics that include the probability distribution of the discovery distance. Second, we introduce \textcolor{blue}{a threshold-based scheduling framework}, termed State-Aware Optimal Point Servicing (SA-OPS), which \textcolor{blue}{selects one of three actions according to the node's real-time energy state: preemptive charging, communication followed by charging, or communication only.} Simulations and multi-criteria decision analysis show that, \textcolor{blue}{under the considered assumptions and parameter ranges}, SA-OPS can improve the tradeoff between AUV energy expenditure and network-wide energy health relative to the adopted baseline strategies. The results also indicate that the selected charging threshold can be approximated by \textcolor{blue}{a simple state-dependent heuristic}, providing a practical guideline for autonomous energy replenishment in underwater networks.

eess.SY↗

arXiv2026

Offset Pointing for Energy-efficient Reception in Underwater Optical Wireless Communication: Modeling and Performance Analysis

Underwater Wireless Optical Communication is a key enabling technology for future space-air-ground-sea integrated networks. However, UOWC faces critical hurdles from spatial randomness and stringent energy constraints. These challenges fundamentally limit network lifetime and sustainability. This paper develops a comprehensive stochastic geometry framework to perform a differential energy analysis of UOWC links.Instead of relying on simplified models, we employ a three-dimensional truncated Poisson point process to accurately capture the anisotropic nature of the underwater environment, specifically the disparity between horizontal spread and vertical depth. It incorporates a Lambertian emission pattern, random receiver positions and orientations, and a realistic channel model with extinction effects. Under this model, we derive a full suite of closed-form expressions for key performance indicators. These include the nearest-neighbor distance distribution, expected received power, SNR, and BER. A principal and counter-intuitive finding of our analysis is an offset-pointing strategy. This strategy involves intentionally misaligning the receiver by a deterministically optimal angle. This approach maximizes the integrated received power across the aperture, contrary to the conventional pursuit of perfect alignment. We formulate and solve an energy-efficiency optimization problem. Our results demonstrate that this strategy enhances system robustness and yields substantial performance gains. Simulation results validate our analytical models. They show that the optimal offset strategy can reduce the required transmit power by nearly 20\% to achieve a target BER. This reduction directly translates into extended network lifetime and higher total data throughput. These findings offer a new design paradigm for deploying robust, cost-effective, and sustainable UOWC networks.

cs.IT↗

arXiv2025

Large AI Model-Enabled Generative Semantic Communications for Image Transmission

The rapid development of generative artificial intelligence (AI) has introduced significant opportunities for enhancing the efficiency and accuracy of image transmission within semantic communication systems. Despite these advancements, existing methodologies often neglect the difference in importance of different regions of the image, potentially compromising the reconstruction quality of visually critical content. To address this issue, we introduce an innovative generative semantic communication system that refines semantic granularity by segmenting images into key and non-key regions. Key regions, which contain essential visual information, are processed using an image oriented semantic encoder, while non-key regions are efficiently compressed through an image-to-text modeling approach. Additionally, to mitigate the substantial storage and computational demands posed by large AI models, the proposed system employs a lightweight deployment strategy incorporating model quantization and low-rank adaptation fine-tuning techniques, significantly boosting resource utilization without sacrificing performance. Simulation results demonstrate that the proposed system outperforms traditional methods in terms of both semantic fidelity and visual quality, thereby affirming its effectiveness for image transmission tasks.

cs.CV↗