Downlink Training Sequence Design Based on Waterfilling Solution for Low-Latency FDD Massive MIMO Communications Systems

Abstract

Future generations of wireless communications systems are expected to evolve toward allowing massive ubiquitous connectivity and achieving ultra-reliable and low-latency communications (URLLC) with extremely high data rates. Massive multiple-input multiple-output (m-MIMO) is a crucial transmission technique to fulfill the demands of high data rates in the upcoming wireless systems. However, obtaining a downlink (DL) training sequence (TS) that is feasible for fast channel estimation, i.e., meeting the low-latency communications required by future generations of wireless systems, in m-MIMO with frequency-division-duplex (FDD) when users have different channel correlations is very challenging. Therefore, a low-complexity solution for designing the DL training sequences to maximize the achievable sum rate of FDD systems with limited channel coherence time (CCT) is proposed using a waterfilling power allocation method. This achievable sum rate maximization is achieved using sequences produced from a summation of the user’s covariance matrices and then applying a waterfilling power allocation method to the obtained low-complexity training sequence. The results show that the proposed TS outperforms the existing methods in the medium and high SNR regimes while reducing computational complexity. The obtained results signify the proposed TS’s feasibility for practical consideration compared with the existing DL training sequence designs.

Maximizing the Downlink Data Rates in Massive Multiple Input Multiple Output with Frequency Division Duplex Transmission Mode Using Power Allocation Optimization Method with Limited Coherence Time

“…To this end, several research works have investigated the downlink CSI estimation in FDD MMIMO systems, thereby taking into account the spatial correlation at the BS; see, e.g, [20][21][22][23][24][25][26][27][28]. Another area of study has been on CSI estimation utilizing methods based on compressed sensing (CS) [29][30][31][32][33][34].…”

Multi-Head Transformer Architecture with Higher Dimensional Feature Representation for Massive MIMO CSI Feedback

“…Leveraging the accurate CSI, the BS can design the appropriate precoding vector, thus improving the transmission quality. This is especially significant in FDD systems because, unlike in TDD systems where channel reciprocity is taken into account, the CSI of the downlink cannot be directly obtained from the channel estimation at the BS [5][6][7]. The CSI at the BS in FDD systems relies heavily on accurate feedback from the UE in real time, which poses a formidable challenge since the feedback overhead increases in a linear manner as the number of antennas grows [8].…”