Pushing The Limit of Type I Codebook For Fdd Massive Mimo Beamforming: A Channel Covariance Reconstruction Approach

Abstract

There is a fundamental trade-off between the channel representation resolution of codebooks and the overheads of feedback communications in the fifth generation new radio (5G NR) frequency division duplex (FDD) massive multipleinput and multiple-output (MIMO) systems. In particular, two types of codebooks (namely Type I and Type II codebooks) are introduced with different resolution and overhead. Although the Type I codebook based scheme requires lower feedback overhead, its channel state information (CSI) reconstruction and beamforming performance are not as good as those from the Type II codebook based scheme. However, since the Type I codebook based scheme has been widely used in 4G systems for many years, replacing it by the Type II codebook based scheme overnight is too costly to be an option. Therefore, in this paper, using Type I codebook, we leverage advances in cutting plane method to optimize the CSI reconstruction at the base station (BS), in order to close the gap between these two codebook based beamforming schemes. Numerical results based on channel samples from QUAsi Deterministic RadIo channel GenerAtor (QuaDRiGa) are presented to show the excellent performance of the proposed algorithm in terms of beamforming vector acquisition.

Downlink CSI Sensing from Heterogeneous User Feedbacks: A Constrained Phase Retrieval Approach

“…It is worth pointing out that there exist several works [4][5][6] that share the same goal as our current work and target improving the CSI recovery at the BS with low-resolution feedback. The work [4] designed a binary CSI feedback scheme, where the receiver returns a binary indicator to inform whether the received signal-to-noise ratio (SNR) is more significant than a preset threshold.…”

“…While the CSI accuracy can be improved from multiple coarse feedbacks, the scheme in [4] cannot be applied to existing cellular systems with the PMI and CQI feedback mechanism. Comparatively, the designs in [5] and [6] also made use of the feedback PMI and CQI of the Type-I codebook. Still, the corresponding problem formulation and proposed schemes are different from our design.…”

“…Still, the corresponding problem formulation and proposed schemes are different from our design. For instance, a volume minimization problem was formulated in [5] and a cutting plane method like that in [4] was proposed to search the channel covariance matrix. The work [6] formulated a 2-norm regularized PR problem for CSI sensing, and an alternating minimization algorithm was devised.…”

“…The work [6] formulated a 2-norm regularized PR problem for CSI sensing, and an alternating minimization algorithm was devised. However, both methods in [5,6] still take a large number of feedbacks (e.g., more than 30 rounds of feedbacks in [5]) to achieve comparable performance as the Type-II codebook. Besides, they neither exploit the spatial consistency nor consider the precoding design.…”

“…In particular, we aim to utilize the PMI and channel quality indicator (CQI) of the Type-I codebook fed back from the target UE (TU) to recover its CSI with high accuracy. This CSI sensing task can be formulated as a phase retrieval (PR) problem via appropriate training signal design [4][5][6]. However, like the conventional PR, which requires many measurements for accurate recovery, these methods require many feedbacks from the TU to recover the CSI well.…”