Trainable Projected Gradient Detector for Sparsely Spread Code Division Multiple Access
Trainable Projected Gradient Detector for Sparsely Spread Code Division Multiple Access
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DOI：
10.1109/iccworkshops49005.2020.9145193
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
Satoshi Takabe;Yuki Yamauchi;T. Wadayama
中科院分区：
文献类型：
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作者：
Satoshi Takabe;Yuki Yamauchi;T. Wadayama
稀疏传播代码部多访问（SCDMA）是一种有希望的非正交多访问技术，用于将来的无线通信。在本文中，我们提出了一种新型的可训练的多源探测器，称为稀疏训练梯度（STPG）检测器，该检测器基于深度展开的概念。在STPG检测器中，可训练的参数嵌入到投影的梯度下降算法中，该算法可以通过标准深度学习技术（例如背部传播和随机梯度下降）进行训练。检测器的优点是其计算成本低和少量可训练参数，这使我们能够治疗大量的SCDMA系统。特别是，其计算成本小于常规信念传播（BP）检测器，而STPG检测器与BP检测器的检测性能几乎相同。我们还建议对签名序列和STPG检测器进行签名设计的可扩展关节学习。数值结果表明，联合学习改善了多源检测性能，尤其是在低SNR制度中。
Sparsely spread code division multiple access (SCDMA) is a promising non-orthogonal multiple access technique for future wireless communications. In this paper, we propose a novel trainable multiuser detector called sparse trainable projected gradient (STPG) detector, which is based on the notion of deep unfolding. In the STPG detector, trainable parameters are embedded to a projected gradient descent algorithm, which can be trained by standard deep learning techniques such as back propagation and stochastic gradient descent. Advantages of the detector are its low computational cost and small number of trainable parameters, which enables us to treat massive SCDMA systems. In particular, its computational cost is smaller than a conventional belief propagation (BP) detector while the STPG detector exhibits nearly same detection performance with a BP detector. We also propose a scalable joint learning of signature sequences and the STPG detector for signature design. Numerical results show that the joint learning improves multiuser detection performance particularly in the low SNR regime.