In this paper,a powerful model-driven deep learning framework is exploited to overcome the challenge of multi-domain signal detection in spacedomain index modulation(SDIM)based multiple input multiple output(MIMO)syst...In this paper,a powerful model-driven deep learning framework is exploited to overcome the challenge of multi-domain signal detection in spacedomain index modulation(SDIM)based multiple input multiple output(MIMO)systems.Specifically,we use orthogonal approximate message passing(OAMP)technique to develop OAMPNet,which is a novel signal recovery mechanism in the field of compressed sensing that effectively uses the sparse property from the training SDIM samples.For OAMPNet,the prior probability of the transmit signal has a significant impact on the obtainable performance.For this reason,in our design,we first derive the prior probability of transmitting signals on each antenna for SDIMMIMO systems,which is different from the conventional massive MIMO systems.Then,for massive MIMO scenarios,we propose two novel algorithms to avoid pre-storing all active antenna combinations,thus considerably improving the memory efficiency and reducing the related overhead.Our simulation results show that the proposed framework outperforms the conventional optimization-driven based detection algorithms and has strong robustness under different antenna scales.展开更多
正交时频空(Orthogonal Time Frequency Space,OTFS)调制技术和索引调制(Index Modulation,IM)技术适用于高移动性场景,具有高频谱效率等优势,在图像传输领域应用前景广泛。然而,结合索引调制的正交时频空(Orthogonal Time Frequency Sp...正交时频空(Orthogonal Time Frequency Space,OTFS)调制技术和索引调制(Index Modulation,IM)技术适用于高移动性场景,具有高频谱效率等优势,在图像传输领域应用前景广泛。然而,结合索引调制的正交时频空(Orthogonal Time Frequency Space with Index Modulation,OTFS-IM)调制技术的算法实现复杂度较高。对此,在广义近似消息传递(Generalized Approximate Message Passing,GAMP)的基础上改进,提出一种基于概率排序的阻尼广义消息传递算法。实验结果表明,在图像传输应用中,与传统的最小均方误差结合最大似然方案(Minimum Mean Squared Error with Maximum Likelihood,MMSEML)相比,所提的算法具有更低的复杂度,性能也优于传统的MMSE-ML传输方案。展开更多
期望最大化贝努利高斯（BG）近似信息传递（EM-BG-AMP）算法中的BG模型因为具有对称性,在逼近实际信号先验分布时会受到限制;而期望最大化高斯混合近似信息传递（EM-GM-AMP）算法中的GM模型是BG模型的高阶形式,复杂度较高。为了解决以...期望最大化贝努利高斯（BG）近似信息传递（EM-BG-AMP）算法中的BG模型因为具有对称性,在逼近实际信号先验分布时会受到限制;而期望最大化高斯混合近似信息传递（EM-GM-AMP）算法中的GM模型是BG模型的高阶形式,复杂度较高。为了解决以上问题,提出贝努利不对称高斯模型（BAG）,进而推导得到期望最大化贝努利不对称高斯近似信息传递（EM-BAG-AMP）算法。该算法的主要思路是假设输入信号服从BAG模型,然后使用广义近似信息传递（GAMP）重构信号并在算法迭代中同时更新模型参数。实验证明,在处理不同图像数据时,EM-BAG-AMP和EM-BG-AMP相比,时间增加了1.2%,峰值信噪比（PSNR）值提升了0.1~0.5 d B,尤其在处理纹理较少以及色差变化明显的图像时峰值信噪比（PSNR）值提升了0.4~0.5 d B。EM-BAG-AMP是对EM-BG-AMP算法的扩展和延伸,更适合实际信号的处理。展开更多
基金supported by the National Natural Science Foundation of China under Grant U19B2014the Sichuan Science and Technology Program under Grant 2023NSFSC0457the Fundamental Research Funds for the Central Universities under Grant 2242022k60006.
文摘In this paper,a powerful model-driven deep learning framework is exploited to overcome the challenge of multi-domain signal detection in spacedomain index modulation(SDIM)based multiple input multiple output(MIMO)systems.Specifically,we use orthogonal approximate message passing(OAMP)technique to develop OAMPNet,which is a novel signal recovery mechanism in the field of compressed sensing that effectively uses the sparse property from the training SDIM samples.For OAMPNet,the prior probability of the transmit signal has a significant impact on the obtainable performance.For this reason,in our design,we first derive the prior probability of transmitting signals on each antenna for SDIMMIMO systems,which is different from the conventional massive MIMO systems.Then,for massive MIMO scenarios,we propose two novel algorithms to avoid pre-storing all active antenna combinations,thus considerably improving the memory efficiency and reducing the related overhead.Our simulation results show that the proposed framework outperforms the conventional optimization-driven based detection algorithms and has strong robustness under different antenna scales.
文摘正交时频空(Orthogonal Time Frequency Space,OTFS)调制技术和索引调制(Index Modulation,IM)技术适用于高移动性场景,具有高频谱效率等优势,在图像传输领域应用前景广泛。然而,结合索引调制的正交时频空(Orthogonal Time Frequency Space with Index Modulation,OTFS-IM)调制技术的算法实现复杂度较高。对此,在广义近似消息传递(Generalized Approximate Message Passing,GAMP)的基础上改进,提出一种基于概率排序的阻尼广义消息传递算法。实验结果表明,在图像传输应用中,与传统的最小均方误差结合最大似然方案(Minimum Mean Squared Error with Maximum Likelihood,MMSEML)相比,所提的算法具有更低的复杂度,性能也优于传统的MMSE-ML传输方案。
文摘期望最大化贝努利高斯（BG）近似信息传递（EM-BG-AMP）算法中的BG模型因为具有对称性,在逼近实际信号先验分布时会受到限制;而期望最大化高斯混合近似信息传递（EM-GM-AMP）算法中的GM模型是BG模型的高阶形式,复杂度较高。为了解决以上问题,提出贝努利不对称高斯模型（BAG）,进而推导得到期望最大化贝努利不对称高斯近似信息传递（EM-BAG-AMP）算法。该算法的主要思路是假设输入信号服从BAG模型,然后使用广义近似信息传递（GAMP）重构信号并在算法迭代中同时更新模型参数。实验证明,在处理不同图像数据时,EM-BAG-AMP和EM-BG-AMP相比,时间增加了1.2%,峰值信噪比（PSNR）值提升了0.1~0.5 d B,尤其在处理纹理较少以及色差变化明显的图像时峰值信噪比（PSNR）值提升了0.4~0.5 d B。EM-BAG-AMP是对EM-BG-AMP算法的扩展和延伸,更适合实际信号的处理。
