Bayesian Estimator and Detector for Massive Communication With Ultra Massive MIMO

In this article, the Bayesian estimator and detector are proposed in the scenario of massive communication. The ultra massive multiple-input-multiple-output (MIMO) is established at the base station (BS), which is communicated with a huge number of online devices in the near field. In order to estimate the uplink channel responses, the novel nonorthogonal pilot sequences are designed and the principle of turbo decoding is applied. Then, the sparse estimation of extra large-scale channel state information (CSI) is performed depending on the extrinsic information transferring in the spatial domain and angular domain. Besides, the mixed analog-to-digital converter (ADC) architecture is considered to accomplish the linear and nonlinear measurements. Based on this framework, the tradeoff between the system performance and hardware overhead can be achieved. Additionally, a submodule-based segmentation technique is addressed to eliminate the energy spreading phenomenon caused by the near filed effects of the ultra massive MIMO. Specifically, we also analyze the theoretical statistical result of sparse channel estimation and device activity detection using the state evolution method. Furthermore, several engineering implementation strategies are provided to enhance the efficiency improvements in the practical system of massive communication. Numerical simulation results demonstrate that the satisfactory performance of estimation/detection is beyond other methods in terms of hardware costs and computational complexity in the extra large Internet of Things (IoT) network.