发表机构
Korea Advanced Institute of Science and Technology; LG Electronics（韩国科学技术院; LG电子）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种基于深度学习的预测波束成形算法，利用回波信号和交叉注意力机制，在感知约束下最大化频谱效率，提升ISAC系统通信性能。
AI 中文摘要
本文针对车对基础设施网络中的集成感知与通信，提出了一种基于深度学习的预测波束成形算法，利用回波信号来增强通信性能。我们在感知信干噪比约束下构建了总和频谱效率最大化问题，并构造了一个基于惩罚的无约束替代目标用于网络训练。基于该公式，利用感知回波和先前时隙的历史波束成形器来预测波束成形矩阵。为此，我们开发了一个预测波束成形网络，通过独立的编码器处理感知回波和波束成形输入，以保留它们各自的模态特定特征。这种独立设计使每个模态能够学习丰富且互补的表示，与简单的输入融合相比，通过交叉注意力机制能更有效地利用这些表示。所得特征通过交叉注意力进行融合，而门控循环单元则捕捉跨时隙的时间依赖性。仿真结果验证了所提方法的性能。
英文摘要
In this paper, we propose a deep learning-based predictive beamforming algorithm for integrated sensing and communications in vehicle-to-infrastructure networks, where echo signals are leveraged to enhance communication performance. We formulate a sum spectral efficiency maximization problem under a sensing signal-to-interference-plus-noise ratio (SINR) constraint and construct a penalty-based unconstrained surrogate objective for network training. Based on this formulation, the beamforming matrix is predicted using sensing echoes and historical beamformers from previous time slots. To this end, we develop a predictive beamforming network that processes sensing echoes and beamforming inputs through separate encoders to preserve their modality-specific characteristics. This separate design enables each modality to learn rich and complementary representations which are more effectively exploited through a cross-attention mechanism compared to naive input fusion. The resulting features are fused via cross-attention, while gated recurrent units capture temporal dependencies across time slots. Simulation results validate the performance of the proposed method.
