Phase Shift Adjustable Pilots for Channel Acquisition in Vehicle-to-Vehicle Underlay Wideband Massive MIMO

Abstract

Decrease of the pilot overhead is an important issue in vehicle-to-vehicle (V2V) underlay wideband massive multiple-input multiple-output (mMIMO) communication. To reduce the pilot overhead for channel acquisition, we propose phase shift adjustable pilots (PSAPs) for V2V underlay wideband mMIMO systems employing orthogonal frequency division multiplexing (OFDM). Motivated by the sparse property of the angle-delay (AD) domain channels in mMIMO, we first investigate channel acquisition for V2V underlay mMIMO-OFDM with PSAPs, and derive the optimal conditions to achieve the sum mean square error (sMSE) minimums of channel estimation and channel prediction, respectively. The optimal conditions indicate that the minimum sMSE can be achieved when the AD domain channel power spectra of cellular users (CUs) and transmitters in V2V communication pairs are non-overlapping after appropriate allocation of the available pilot phase shifts. Then an AD domain channel power spectrum based pilot allocation (ADCPS-PA)algorithm is developed according to the optimal conditions. Simulation results indicate that the achievable spectral efficiency for both cellular and V2V links can be significantly improved with PSAPs compared with that obtained by the conventional phase shift orthogonal pilot approach. INDEX TERMS Channel acquisition, phase shift adjustable pilots, pilot phase shift allocation, V2V underlay, wideband massive MIMO.

Implementing intelligent‐based sparse channel estimation in Multi User‐Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing system with hybridized optimization algorithm

“…In the host cell infection, once a host cell is infected and then it is destroyed or invaded until its death. Initially, update the population of the host cells C q that is defined in Equation (12).…”

“…The interference from users is suppressed by assisting the Bayesian estimator when the covariance matrices of the channel fulfill certain non-overlapping criteria in the multi-cell scenario on their dominant subspace. 12 These works are assisted 13 to rectify the problems that arise during channel estimation issues and provides efficient performance under various scenarios. Based on black-boxes, the neural networks are usually regarded in the channel estimation that might lack stable performance guarantees.…”

“…In recent days, many research works have been done by researchers to solve the pilot contamination issues in MIMO‐OFDM. The interference from users is suppressed by assisting the Bayesian estimator when the covariance matrices of the channel fulfill certain non‐overlapping criteria in the multi‐cell scenario on their dominant subspace 12 . These works are assisted 13 to rectify the problems that arise during channel estimation issues and provides efficient performance under various scenarios.…”