无小区大规模多输入多输出支持的可扩展节能IoT系统
A Scalable and Energy Efficient IoT System Supported by Cell-Free Massive MIMO
摘要：
IoT（物联网）系统以无线方式支持大量IoT设备。我们展示了如何使用无小区大规模多输入多输出（多输入多输出）来提供可扩展且节能的物联网系统。我们采用最佳线性估计与随机导频获取信道状态信息（信道状态信息）MIMO预编码和解码。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，以获得两个准确的SINR（信号干扰加噪声比）近似只涉及大规模衰落系数。我们推导出几个最大-最小型功率控制算法的基础上精确的SINR表达式和RM近似。接下来，我们考虑下行链路（DL）传输的功率控制问题。为了避免解决一个耗时的准凹问题，需要重复测试的可行性的SOCP（二阶锥规划）问题，我们开发了一个神经网络（NN）辅助功率控制算法，结果在30倍的计算时间减少。该功率控制算法导致可扩展的无小区大规模多输入多输出网络，其中由每个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，可以成倍提高能效（EE），这对物联网网络至关重要。
英文摘要：
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.
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