Single Carrier Frequency Domain Detectors for Internet of Underwater Things

TL;DR

This paper introduces low-complexity single carrier frequency domain detectors for underwater IoT communications, employing FFT, cyclic prefix, and adaptive algorithms to handle fast channel variations with near-optimal BER performance.

Contribution

It proposes a novel single carrier frequency domain equalizer with adaptive LMS and RLS algorithms tailored for underwater IoT, reducing computational complexity and improving robustness.

Findings

BER performance close to ideal MMSE detectors

Effective handling of fast Doppler shifts in underwater channels

Simplified algorithms using FFT and cyclic prefix

Abstract

This paper proposes low complexity detection for internet of underwater things (IoUT)s communication. The signal is transmitted from the source to the destination using several sensors. To simplify the computational operations at the transmitter and the sensory nodes, a single carrier frequency domain equalizer (SC-FDE) is proposed and amplify-and-forward (AF) protocols are employed. Fast Fourier transform (FFT) and use of cyclic prefix (CP) are also proposed to simplify these algorithms when compared to time-domain equalization. As precise channel data is difficult to capture in underwater communications, the adaptive implementation of FDE is proposed as a solution that can be employed when the channel experiences a fast doppler shift. The two adaptive detectors are based on the least mean-square (LMS) and recursive least square (RLS) principles. Numerical simulations show that the performance of the bit error rate (BER) performance of the proposed detectors is close to that of the ideal minimum mean square error (MMSE).