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

点击翻译按钮获取中文摘要

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.