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.

翻译：我们研究由无细胞(CF)大规模MIMO(MMIMO)支持的大型线性频道估计支持的事物互联网系统。 对于上行链路,我们考虑最佳线性线性MIMO接收器,并获得仅涉及使用随机矩阵(RM)理论的大规模衰减系数的SINR近似链接。我们利用这个近距离线设计了数种最大功率控制算法,将功率系数和加权系数纳入到高能效的目标率中。对于下行链路,我们考虑最大功率(MIMO)波形。我们不解决下行链路电控的复杂准连通问题,而是使用神经网络(NNN)技术获得类似的电源控制,在计算时间上减少大约30倍。对于大型网络,我们提出了不同的基于NNN的功率控制算法。这一算法是次优化的,但其优势在于它可以缩放。