Region of Feasibility of Interference Alignment in Underwater Sensor Networks

To enable underwater applications such as coastal and tactical surveillance, undersea explorations, and real-time picture/video acquisition, there is a need to achieve high data-rate and reliable communications underwater, which translates into attaining high acoustic channel spectral efficiencies. Interference alignment (IA), which has been recently proposed for radio-frequency multiple-input-multiple-output (MIMO) terrestrial communication systems, aims at improving the spectral efficiency by enabling nodes to transmit data simultaneously at a rate equal to half of the interference-free channel capacity. The core of IA in the space domain lies in designing transmit precoding matrices for each transmitter such that all the interfering signals align at the receiver along a direction different from that of the desired signal. While promising, however, there are still challenges to solve for the practical use of IA underwater, i.e., imperfect acoustic channel knowledge, high computational complexity, and high communication delay. In this paper, a feasibility study on the employment of IA underwater is presented. A novel distributed computing framework for sharing processing resources in the network so to parallelize and speed IA algorithms up is proposed; also, such framework enables “ensemble learning” of various precoding matrices computed using different (competing) IA algorithms so to achieve efficient alignment of the interference at the receiver. The robustness of the IA technique against imperfect acoustic channel knowledge is also quantified by estimating precoding matrices based on predicted channel coefficients. Finally, the performance of an algorithm to predict the underwater acoustic channel impulse response is presented using real data sets.