Interference management with realistic assumptions on the knowledge of channel state information at the transmitters

点击翻译按钮获取中文摘要

Our proposal aims at developing efficient transmission and reception schemes for interference-limited multi-user communication networks with realistic assumptions on the availability of channel state information (CSI) at the transmitter side. Extensive research on the capacity limits of interference-limited networks in the last several years has proved that knowledge of CSI at the transmitters (CSIT) can provide considerable performance improvements. Indeed, for many networks it has been shown that at high SNR the optimal performance can be achieved by exploiting the CSIT using a method called interference alignment (IA). However, the global CSI of the network must be available at each transmitter to perform IA in a multi-user network.In this proposal, we consider more realistic IA scenarios where the transmitters only have access to a limited amount of CSI. The goal is to develop schemes that optimally exploit whatever CSI is available to improve spectral efficiency.In our first scenario, we assume that the transmitters have access to delayed CSI, which means that the available information is completely outdated with respect to the current channel. It has been previously shown that using only delayed CSI it is still possible to achieve multiplexing gains over the case where no CSI is available. However, many of the solutions in the delayed-CSI problem are based on heuristic methods. We are looking for unified and compact solutions that can be applied to general settings.The second scenario is the case where some transmitters do not create interference for some receivers. This situation is common in the real world where some devices are at distant locations. In partially connected networks, it has been previously shown that IA can be effective even without CSI at the transmitter. Our objective here is to improve the IA-based schemes by exploiting network properties. For example, multi-antenna nodes at the transceivers can receive or transmit in reduced-dimensional subspaces while creating a virtual partially connected network.Finally, we investigate several realistic scenarios and adapt our solutions to account for practical impairments. We study joint application of different IA methods as multiple conditions coexist in reality. We will carry out real experiments on a multiple-input-multiple-output (MIMO) testbed to verify the performance of our algorithms in a practical setting.