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arXiv 2402.10071eess.SPcs.ITmath.IT
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用于OTFS数据检测的近似消息传递增强图神经网络

Approximate Message Passing-Enhanced Graph Neural Network for OTFS Data Detection

Wenhao Zhuang, Yuyi Mao, Hengtao He, Lei Xie, Shenghui Song, Yao Ge, Zhi Ding

AI总结:

针对高移动性通信中OTFS调制的检测需求,提出结合近似消息传递与图神经网络的检测器,搭配基于学习的多普勒间干扰近似,在低复杂度下实现更优误码率性能。

摘要:

正交时频空间(OTFS)调制已成为支持高移动性无线通信的一种极具前景的方案,而具备成本效益的数据检测器对这类系统至关重要。尽管基于图神经网络(GNN)的数据检测器能以合理的计算成本实现不错的检测精度,但它们未能充分利用发射数据的先验信息。为进一步降低OTFS系统的数据检测误差,本文提出一种基于AMP-GNN的检测器,利用近似消息传递(AMP)算法迭代优化GNN的符号估计结果。鉴于多普勒间干扰(IDI)符号会给构建的GNN带来大量计算开销,本文采用基于学习的IDI近似方法,以维持较低的检测复杂度。仿真结果表明,与现有基线方法相比,所提出的基于AMP-GNN的检测器实现了优异的误码率(BER)性能。同时,所提出的IDI近似方案在误码率性能下降可忽略不计的情况下,规避了大量计算。

英文摘要:

Orthogonal time frequency space (OTFS) modulation has emerged as a promising solution to support high-mobility wireless communications, for which, cost-effective data detectors are critical. Although graph neural network (GNN)-based data detectors can achieve decent detection accuracy at reasonable computational cost, they fail to best harness prior information of transmitted data. To further minimize the data detection error of OTFS systems, this letter develops an AMP-GNN-based detector, leveraging the approximate message passing (AMP) algorithm to iteratively improve the symbol estimates of a GNN. Given the inter-Doppler interference (IDI) symbols incur substantial computational overhead to the constructed GNN, learning-based IDI approximation is implemented to sustain low detection complexity. Simulation results demonstrate a remarkable bit error rate (BER) performance achieved by the proposed AMP-GNN-based detector compared to existing baselines. Meanwhile, the proposed IDI approximation scheme avoids a large amount of computations with negligible BER degradation.

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。

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