107 research outputs found
A Generalized Framework on Beamformer Design and CSI Acquisition for Single-Carrier Massive MIMO Systems in Millimeter Wave Channels
In this paper, we establish a general framework on the reduced dimensional
channel state information (CSI) estimation and pre-beamformer design for
frequency-selective massive multiple-input multiple-output MIMO systems
employing single-carrier (SC) modulation in time division duplex (TDD) mode by
exploiting the joint angle-delay domain channel sparsity in millimeter (mm)
wave frequencies. First, based on a generic subspace projection taking the
joint angle-delay power profile and user-grouping into account, the reduced
rank minimum mean square error (RR-MMSE) instantaneous CSI estimator is derived
for spatially correlated wideband MIMO channels. Second, the statistical
pre-beamformer design is considered for frequency-selective SC massive MIMO
channels. We examine the dimension reduction problem and subspace (beamspace)
construction on which the RR-MMSE estimation can be realized as accurately as
possible. Finally, a spatio-temporal domain correlator type reduced rank
channel estimator, as an approximation of the RR-MMSE estimate, is obtained by
carrying out least square (LS) estimation in a proper reduced dimensional
beamspace. It is observed that the proposed techniques show remarkable
robustness to the pilot interference (or contamination) with a significant
reduction in pilot overhead
Improving the Performance of R17 Type-II Codebook with Deep Learning
The Type-II codebook in Release 17 (R17) exploits the angular-delay-domain
partial reciprocity between uplink and downlink channels to select part of
angular-delay-domain ports for measuring and feeding back the downlink channel
state information (CSI), where the performance of existing deep learning
enhanced CSI feedback methods is limited due to the deficiency of sparse
structures. To address this issue, we propose two new perspectives of adopting
deep learning to improve the R17 Type-II codebook. Firstly, considering the low
signal-to-noise ratio of uplink channels, deep learning is utilized to
accurately select the dominant angular-delay-domain ports, where the focal loss
is harnessed to solve the class imbalance problem. Secondly, we propose to
adopt deep learning to reconstruct the downlink CSI based on the feedback of
the R17 Type-II codebook at the base station, where the information of sparse
structures can be effectively leveraged. Besides, a weighted shortcut module is
designed to facilitate the accurate reconstruction. Simulation results
demonstrate that our proposed methods could improve the sum rate performance
compared with its traditional R17 Type-II codebook and deep learning
benchmarks.Comment: Accepted by IEEE GLOBECOM 2023, conference version of
Arxiv:2305.0808
- …