Wirtinger Flow for Nonconvex Blind Demixing With Optimal Step Size
Wirtinger Flow for Nonconvex Blind Demixing With Optimal Step Size
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DOI：
10.1109/lwc.2022.3203313
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发表时间：
2022-12
影响因子：
6.3
通讯作者：
Chih-Ho Hsu;Carlos Feres;Zhi Ding
中科院分区：
文献类型：
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作者：
Chih-Ho Hsu;Carlos Feres;Zhi Ding
大规模部署物联网(IoT)设备的前景推动了多个节点同时进行上行链路传输的免授权访问。盲解混技术是在未知信道上恢复多个这样的源信号的一种很有前途的技术。最近的研究表明，Wirtinger Flow(WF)算法可以有效地进行盲解混。然而，现有的关于WF步长选择的理论结果往往比较保守，收敛速度较慢。为了克服这一局限性，我们提出了一种改进的WF(WF-OPT)，通过优化其每次迭代的步长，加快了收敛速度。我们为WF-OPT的严格收缩提供了理论保证，并给出了收缩比的上界。仿真结果表明，该算法具有预期的收敛增益。
The prospect of massive deployment of devices for Internet-of-Things (IoT) motivates grant-free access for simultaneously uplink transmission by multiple nodes. Blind demixing represents a promising technique for recovering multiple such source signals over unknown channels. Recent studies show Wirtinger Flow (WF) algorithm can be effective in blind demixing. However, existing theoretical results on WF step size selection tend to be conservative and slow down convergence rates. To overcome this limitation, we propose an improved WF (WF-OPT) by optimizing its step size in each iteration and expediting the convergence. We provide a theoretical guarantee on the strict contraction of WF-OPT and present the upper bounds of the contraction ratio. Simulation results demonstrate the expected convergence gains.