A Parallel Decoding Approach for Mitigating Near–Far Interference in Internet of Underwater Things
Yuehai Zhou, Roee Diamant
Abstract
With the massive development of underwater small robotic vehicles and matching acoustic modems, applications for the Internet of Underwater Things (IoUT) are emerging. IoUT involves communication between the nonsynchronized network nodes organized in a mesh. A limiting factor of such communication is the so-called near-far effect, where transmissions from a node (near) close to a common receiver block the transmissions of a farther node (far). Due to the high-power attenuation in the underwater acoustic channel, near-far effect is common in underwater acoustic communication networks, and the phenomena occurs even for a distance ratio of 80% between the near and far nodes to the receiver, and the large number of nodes in the IoUT compounds the effect of this phenomena. While the current approaches only consider the jamming effect to the far signal, in this article, we consider canceling the interference from both sources by estimating and equalizing the channels on parallel, thereby significantly improving the decoding of both signals. As a result, IoUT performance improves. To limit mutual interference, we propose an automatic switching mechanism that controls the cancelation operation both in channel estimation and channel equalization. The simulation results show that our approach obtains significant improvement in communication from both near and far nodes. Results from a designated sea trial demonstrate that when both nodes are affected by their mutual transmissions, our proposed method improves the output signal-to-noise ratio (SNR) significantly.