基于HARQ的协同感知语义通信
Semantic Communication for Cooperative Perception using HARQ
AI总结：
针对自动驾驶协同感知的可靠传输需求，提出结合重要性图、OFDM与HARQ语义错误检测的中间融合语义通信框架，在感知性能和吞吐量上均优于传统分离编码方法。
摘要：
协同感知相比单设备感知拥有更宽视野，在自动驾驶领域的重要性正日益提升。这种感知通过车对车（V2V）通信实现，支持网联自动驾驶车辆（CAV）交换激光探测与测距（LiDAR）点云等传感器数据，从而增强对环境的集体感知能力。本文中，我们利用重要性图提取关键语义信息，提出了一种采用中间融合的协同感知语义通信框架。为应对时变多径衰落带来的挑战，我们的方法结合了正交频分复用（OFDM）以及信道估计与均衡策略。此外，考虑到可靠传输的必要性，尤其是在低信噪比（SNR）场景下，我们借鉴混合自动重传请求（HARQ）的思路，提出了一种新型语义错误检测方法，并将其集成到我们的语义通信框架中。仿真结果表明，无论是否启用HARQ，我们的模型在感知性能上均优于传统的分离信源信道编码方法。此外，在吞吐量方面，我们提出的HARQ方案比传统编码方法展现出更优的效率。
英文摘要：
Cooperative perception, offering a wider field of view than standalone perception, is becoming increasingly crucial in autonomous driving. This perception is enabled through vehicle-to-vehicle (V2V) communication, allowing connected automated vehicles (CAVs) to exchange sensor data, such as light detection and ranging (LiDAR) point clouds, thereby enhancing the collective understanding of the environment. In this paper, we leverage an importance map to distill critical semantic information, introducing a cooperative perception semantic communication framework that employs intermediate fusion. To counter the challenges posed by time-varying multipath fading, our approach incorporates the use of orthogonal frequency-division multiplexing (OFDM) along with channel estimation and equalization strategies. Furthermore, recognizing the necessity for reliable transmission, especially in the low SNR scenarios, we introduce a novel semantic error detection method that is integrated with our semantic communication framework in the spirit of hybrid automatic repeated request (HARQ). Simulation results show that our model surpasses the traditional separate source-channel coding methods in perception performance, both with and without HARQ. Additionally, in terms of throughput, our proposed HARQ schemes demonstrate superior efficiency to the conventional coding approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
