论文标题
最佳用无细胞的大型MIMO支持物联网
Optimally Supporting IoT with Cell-Free Massive MIMO
论文作者
论文摘要
我们研究了由无细胞（CF）大量MIMO（MMIMO）支持的物联网（IoT）系统，并具有最佳的线性通道估计。对于上行链路，我们考虑最佳的线性MIMO接收器，并获得上行链路SINR近似，仅使用随机矩阵（RM）理论涉及大规模褪色系数。使用此近似值，我们设计了几种最大力控制算法，这些算法结合了功率和速率加权系数，以实现具有高能量效率的目标速率。对于下行链路，我们考虑最大比率（MR）波束形成。我们使用神经网络（NN）技术来获得可比较的功率控制，而计算时间降低了约30倍，而不是解决下行链路功率控制的复杂问题问题。对于大型网络，我们提出了另一种基于NN的电源控制算法。该算法是次优的，但其最大的优势是它是可扩展的。
We study internet of things (IoT) systems supported by cell-free (CF) massive MIMO (mMIMO) with optimal linear channel estimation. For the uplink, we consider optimal linear MIMO receiver and obtain an uplink SINR approximation involving only large-scale fading coefficients using random matrix (RM) theory. Using this approximation we design several max-min power control algorithms that incorporate power and rate weighting coefficients to achieve a target rate with high energy efficiency. For the downlink, we consider maximum ratio (MR) beamforming. Instead of solving a complex quasi-concave problem for downlink power control, we employ a neural network (NN) technique to obtain comparable power control with around 30 times reduction in computation time. For large networks we proposed a different NN based power control algorithm. This algorithm is sub-optimal, but its big advantage is that it is scalable.
