论文标题
由无细胞大规模MIMO支持的可扩展和节能的物联网系统
A Scalable and Energy Efficient IoT System Supported by Cell-Free Massive MIMO
论文作者
论文摘要
物联网（物联网）系统可无线支持大量的物联网设备。我们展示了如何使用无细胞的大规模MIMO（多输入和多出输出）来提供可扩展且节能的物联网系统。我们使用随机飞行员使用最佳的线性估计来获取MIMO预编码和解码的CSI（通道状态信息）。在上行链路中，我们采用最佳线性解码器并利用RM（随机矩阵）理论获得两个精确的SINR（信噪比和噪声比）近似值，仅涉及大规模褪色系数。我们基于精确的SINR表达和RM近似值来得出几种Max-Min型功率控制算法。接下来，我们考虑下行链路（DL）传输的功率控制问题。为了避免解决耗时的准综合问题，该问题需要重复测试SOCP（二阶锥形编程）问题的可行性，我们开发了一个神经网络（NN）辅助功率控制算法，这会导致计算时间减少30倍。该功率控制算法导致可扩展的无细胞大规模MIMO网络，其中每个AP进行的计算量不取决于网络AP的数量。 UL和DL功率控制算法都可以明显提高系统频谱效率（SE），更重要的是，可以提高能源效率（EE）的多倍提高，这对于物联网网络至关重要。
An IoT (Internet of things) system supports a massive number of IoT devices wirelessly. We show how to use Cell-Free Massive MIMO (multiple-input and multiple-output) to provide a scalable and energy efficient IoT system. We employ optimal linear estimation with random pilots to acquire CSI (channel state information) for MIMO precoding and decoding. In the uplink, we employ optimal linear decoder and utilize RM (random matrix) theory to obtain two accurate SINR (signal-to-interference plus noise ratio) approximations involving only large-scale fading coefficients. We derive several max-min type power control algorithms based on both exact SINR expression and RM approximations. Next, we consider the power control problem for downlink (DL) transmission. To avoid solving a time-consuming quasi-concave problem that requires repeat tests for the feasibility of a SOCP (second-order cone programming) problem, we develop a neural network (NN) aided power control algorithm that results in 30 times reduction in computation time. This power control algorithm leads to scalable Cell-Free Massive MIMO networks in which the amount of computations conducted by each AP does not depend on the number of network APs. Both UL and DL power control algorithms allow visibly improve the system spectral efficiency (SE) and, more importantly, lead to multi-fold improvements in Energy Efficiency (EE), which is crucial for IoT networks.
