Improved spectral efficiency in massive MIMO ultra-dense networks through optimal pilot-based vector perturbation precoding

Abstract

In multi-user massive MIMO (mMIMO) Ultra Dense Networks (UDN), the acquisition of Channel State Information (CSI) is crucial at the transmitter to maximize the estimation, but it suffers from increased overhead and complexity. The processing of CSI from imperfect channels further increases the complexity of precoders. Because of the complexity, it is hard to find the optimal perturbing vector, which increases the transmit power with reduction in spectral efficiency. This research improves the spectral efficiency of mMIMO UDN using optimal pilot-based vector perturbation (OPVP) precoding. The optimal pilot design senses the CSI intelligently for feedback into the transmitter, and the perturbing signal, for transmission and efficient reception, is selected optimally using Evolutionary Chaotic Behavior (ECB). This integrated OPVP precoding uses compressive sensing to select the low-dimensional CSI, based on which the EEA allows the generation of perturbing signals within its constellation bounds to get efficiently detected with reduced transmit power, computational complexity, and overhead in comparison with the conventional CSI estimation schemes. The simulation is carried out in Matlab to evaluate if the OPVP scheme makes the transmission in MU-MIMO UDN a spectrally efficient one. Compared to state-of-the-art precoding schemes, the integrated model reduces the feedback overhead and avoids high-dimensional CSI recovery.