Qiyu Ma

Qiyu Ma, Jiajie Xu, Mohamed-Slim Alouini

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.

Qiyu Ma, Wanli Ni, Zhijin Qin

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.