Low-Rank Tensor MMSE Equalization

Abstract

New-generation wireless communication systems will employ large-scale antenna arrays to satisfy the increasing capacity demand. This massive scenario brings new challenges to the channel equalization problem due to the increased signal processing complexity. We present a novel low-rank tensor equalizer to tackle the high computational demands of the classical linear approach. Specifically, we propose a method to design a canonical polyadic tensor filter to minimize the mean square error criterion. Our simulation results indicate that the proposed equalizer needs fewer calculations and is more robust to short training sequences than the benchmark.

Simulation-Based Enhancement of SNR in Drone Communication through Uniform Linear Array Configurations

“…SNR enhancement by array gain is a technique used to improve the quality and reliability of wireless communication in line-of sight propagation scenarios. Modern wireless communication systems often implement large-scale antenna arrays to take advantage of the benefits offered by array gain [14]. To improve the SNR level and consequently wireless communication, we proposed the use of an array antenna instead of the single antenna into the drone side [15], [16].…”