Interference Alignment and Its Applications: A Survey, Research Issues, and Challenges

Abstract

The capacity of interference network is a fundamental issue that eludes the researchers for decades. Interference alignment (IA) is an emerging interference management technique that is degrees of freedom (DoFs) optimal, which means it can approach the capacity of interference network at very high signal-to-noise ratio (SNR). In IA networks, the signals are constrained into the same subspaces at the unintended receivers through cooperative precoding, and the desired signal can be recovered at each receiver by eliminating the aligned interferences using decoding matrix. Due to its promising performance in interference management, IA has successfully been applied to many kinds of multiuser wireless networks with excellent performance. Nevertheless, there are still some challenges for the practical utilization of IA, e.g., the overhead of channel state information (CSI) feedback, performance degradation at low and moderate SNRs, etc. In this review, we provide a survey on IA and its applications and discuss some research issues and challenges. The dimensions, networks topologies, and applications of IA are first introduced. Then some fundamental aspects of IA are discussed, including feasibility condition, performance metrics, iterative algorithms, and CSI. We also present some recent research issues of opportunistic IA, spectrum sharing, green IA, topology management, and physical layer security. Finally, some research challenges of IA are identified.