Computer-Assisted Collaborative Learning for Enhancing Students Intellectual Ability Using Machine Learning Techniques

This paper proposes low complexity detection for internet of underwater things 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 is proposed and amplify-and-forward protocols are employed. Fast Fourier transform and use of cyclic prefix 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 and recursive least square principles. Numerical simulations show that the performance of the bit error rate performance of the proposed detectors is close to that of the ideal minimum mean square error.