Cell-free massive multiple-input multiple-output (MIMO) employs a large number of distributed access points (APs) to serve a small number of user equipments (UEs) via the same time/frequency resource. Due to the strong macro diversity gain, cell-free massive MIMO can considerably improve the achievable sum-rate compared to conventional cellular massive MIMO. However, the performance of cell-free massive MIMO is upper limited by inter-user interference (IUI) when employing simple maximum ratio combining (MRC) at receivers. To harness IUI, the expanded compute-and-forward (ECF) framework is adopted. In particular, we propose power control algorithms for the parallel computation and successive computation in the ECF framework, respectively, to exploit the performance gain and then improve the system performance. Furthermore, we propose an AP selection scheme and the application of different decoding orders for the successive computation. Finally, numerical results demonstrate that ECF frameworks outperform the conventional CF and MRC frameworks in terms of achievable sum-rate.

翻译：大量无细胞的大规模多投入产出(MIMO)使用大量分布式接入点(APs),通过同一时间/频率资源为少量用户设备提供服务。由于宏观多样性增长强劲,无细胞的大型MIMO可以大大改善可实现的总和率,而常规的大型细胞巨量IMO则可以大大改善可实现的总和率。然而,无细胞的大型MIMO在接受者使用简单的最大比率结合(MRC)时,其性能受到用户间干扰(IUI)的上限限制。为利用IUI,采用了扩大的计算和前推框架。特别是,我们提出了在ECF框架内平行计算和连续计算的权力控制算法,分别用于利用业绩收益,然后改进系统性能。此外,我们提出了AP选择计划,以及连续计算应用不同的解码命令。最后,数字结果表明,ECF框架在可实现的总率方面超越常规的CF和MC框架。