Combined Massive MIMO and Interference Alignment for Future Wireless Networks Involving Machine Type Communications

机器类型通信（MTC），如物联网（IoT），设备对设备，机器对机器和无线传感器节点，最近作为无线通信领域的突破性概念被引入，提供设备和日常物品之间的通信。人们普遍认为，蜂窝网络在提供如此广泛的连接机器及其相关服务以实现物联网方面发挥着至关重要的作用。思科视觉网络指数报告预测，到2025年，将有1000亿个物理对象连接到云端。为了保持高质量的服务质量（QoS），并满足日益增长的数据速率需求，显著提高未来无线网络的频谱和能量效率是工业界和学术界研究人员的重要目标之一。增加无线系统容量的主要方法有三种：a)进入新的频谱（毫米波），2)使该频谱更有效，3)使网络致密化。为了容纳大量的设备，节点必须非常节能。大规模天线系统（即大规模MIMO）为提高频谱和能源效率铺平了道路。在小型蜂窝中，基站的额外天线通过将能量集中到更少的空间区域来减少在同一频率-时间编码资源中服务的用户之间的蜂窝内干扰。异构网络频谱效率的制约因素之一是干扰管理和有限的无线电资源。众所周知，干扰对准（IA）是一种潜在的干扰缓解方法，它受益于在接入点（即基站）和用户端增加大规模MIMO部署。其主要思想是通过将信号子空间中的多个干扰信号以小于干扰信号数的维数对齐来最小化干扰子空间的维数。本研究计划的目的是建立一个统一的理论框架，以解决涉及MTC的IA和大规模MIMO结合的基本限制，并开发复杂的信号处理算法，以便在现实环境中实现这一概念。在系统层面，我们的研究将通过结合IA和涉及MTC的大规模MIMO来开发新颖而精细的干扰缓解算法和技术。在现实场景下（即在硬件缺陷和多址策略选择的影响下），无线网络架构布局的新模型设计也有目标。关于错误概率、中断概率、接收信噪比统计和吞吐量的性能分析将通过数值和模拟进行评估，以获得结论性结果。

Machine Type Communications (MTC), such as the Internet of Things (IoT), device-to-device, Machine-to-Machine and wireless sensor nodes, have been recently introduced as groundbreaking concepts in the field of wireless communications, offering communication between devices and everyday objects.  It has been widely accepted that cellular networks play an essential part in providing such a wide range of connected machines and their related services in attaining the IoT. The Cisco Visual Networking Index report projects that 100 billion physical objects will be connected to the cloud by 2025. To maintain high Quality-of-Service (QoS),  and to address the ever-increasing demand of data rate, one of the essential objectives of researchers in both industry and academia is to significantly improve the spectral and energy efficiencies for future wireless networks. There are three primary ways of adding capacity to wireless systems: a) moving into a new spectrum (mm wave), 2) making that spectrum more efficient, and 3) densifying the network. To accommodate an immense number of devices, the nodes must be extremely energy efficient. Large-scale antenna systems (i.e. Massive MIMO) pave the way towards improving both spectral and energy efficiency. In small-sized cells, extra antennas at the base stations diminish intra-cell interference among users served in the same frequency-time-code resource by concentrating the energy into fewer areas of space. One of the limitations in the spectral efficiency of heterogeneous networks is the interference management and limited radio resources. It is known that Interference Alignment (IA) is one of the potential interference mitigation methods that benefits from the increased deployment of Massive MIMO at both the access point, i.e., base station, and the user side. The main idea is to minimize the dimension of the interference subspace by aligning multiple interference signals in a signal subspace with a dimension smaller than the number of interference signals. The aim of this research program is to establish a unified theoretical framework for the fundamental limits concerning the marriage of IA and Massive MIMO involving MTC with various practical constraints, and to develop sophisticated signal processing algorithms to realize such a concept in realistic environments. On the system-level, our research will develop novel and refined interference mitigation algorithms and techniques through the combination of IA and Massive MIMO involving MTC. There are also goals regarding the design of new models for wireless network architecture layouts under realistic scenarios (i.e. with the impacts of hardware-impairments and selection of multiple access strategies). Analysis of the performance of the proposed algorithms regarding error probability, outage probability, received signal-to-noise ratio statistics, and throughput will be assessed numerically and via simulations for conclusive results.