Shark-IA

Abstract

A fundamental issue of underwater acoustic (UWA) communications is large propagation delays due to water medium. A new direction to address this issue is to take advantage of large propagation delays rather than enduring them as a disadvantage. Recent advances in time-based interference alignment (IA), or propagation delay (PD)-based IA, offer a new potential to turn the adverse effect of large propagation delays into something that is beneficial to throughput improvement. The goal of this paper is to investigate PD-IA in a multi-hop UWA network. We develop an analytical PD-IA model with a set of constraints that guarantee PD-IA feasibility at the physical layer. Based on this model, we develop a distributed PD-IA scheduling algorithm, called Shark-IA, to maximally overlap interference in a multi-hop UWA network. Simulation results show that Shark-IA algorithm can improve throughput performance when compared to an idealized benchmark algorithm with perfect scheduling and zero propagation delay. Further, the throughput gain increases with the amount of interference in the network.