Cell-Free Massive MIMO is a highly promising approach to enhance network capacity by moving a large number of distributed access points (AP) closer to mobile users while utilizing simple matched filtering and conjugate beamforming. Recent work using minimum mean-squared-error (MMSE) receiver that suppress multi-user interference (MUI) shows significant capacity increase, but at the cost of high computational complexity and residual MUI enhancement. We propose a significantly lower complexity adaptive approach where central processing unit (CPU) removes MUI without amplifying the residual interference. It does so dynamically by using available knowledge of channel estimates to perform joint process of combining selected strongest AP signals for each user and subtracting the sum of interference estimates from other users at the same time. We provide signalto-interference plus noise-ratio (SINR) and complexity analyses backed by numerical results to show the superiority of this approach compared with the state-of-the-art techniques.

翻译：通过将大量分布式接入点更靠近移动用户,同时使用简单匹配的过滤器和组合光束成形,无细胞大量MOIM组织是提高网络能力的一个非常有希望的办法。最近使用最小平均半成形接收器(MMSE)来抑制多用户干扰(MUI)的工作显示,能力显著增加,但代价是计算复杂程度高和残留的 MUI 增强。我们建议采用一种大大降低复杂性的适应方法,中央处理器(CPU)在不扩大剩余干扰的情况下消除MUI。它利用现有频道估计数据来进行联合,将每个用户选定的最强的AP信号合并,同时减去其他用户的干扰估计总和。我们提供信号干扰加噪音拉皮(SINR)和复杂性分析,并以数字结果支持,以显示这一方法相对于最新技术的优势。