Downlink Channel Sounding For Multiuser Massive MIMO With Limited Feedback

Massive multiple-input multiple-output (MIMO) technology is pivotal for next-generation wireless networks, offering higher data rates and seamless connectivity. Despite its promise, channel estimation in feedback-based massive MIMO presents unique challenges that must be addressed to realize massive MIMO for frequency-division duplexing (FDD) and other communications systems without channel reciprocity. This paper proposes a novel base station (BS) centric closed-loop channel sounding method for multiuser massive MIMO systems, aiming to reduce the pilot overhead and improve the data rates. It also reduces the computational load on the user equipments (UEs) and facilitates accurate channel estimation at the BS. The proposed approach utilizes beamspace processing in the pilot optimization and the feedback scheme. Furthermore, we show that the proposed method can be employed with hybrid analog-digital architecture in BSs. An efficient algorithm for designing the pilots to maximize the approximate mutual information (MI) between the channel coefficients and received feedback signals is developed, along with an efficient hybrid zero-forcing (HZF) precoding algorithm. The simulation results show that this approach significantly improves channel estimation accuracy and sum rate performance in multiuser massive MIMO systems.