5 research outputs found
Theoretical Performance Bound of Uplink Channel Estimation Accuracy in Massive MIMO
In this paper, we present a new performance bound for uplink channel
estimation (CE) accuracy in the Massive Multiple Input Multiple Output (MIMO)
system. The proposed approach is based on noise power prediction after the CE
unit. Our method outperforms the accuracy of a well-known Cramer-Rao lower
bound (CRLB) due to considering more statistics since performance strongly
depends on a number of channel taps and power ratio between them. Simulation
results are presented for the non-line of sight (NLOS) 3D-UMa model of 5G
QuaDRiGa 2.0 channel and compared with CRLB and state-of-the-art CE algorithms.Comment: accepted for presentation in a poster session at the ICASSP 2020
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Downlink Precoding for Massive MIMO Systems Exploiting Virtual Channel Model Sparsity
In this paper, the problem of designing a forward link linear precoder for
Massive Multiple-Input Multiple-Output (MIMO) systems in conjunction with
Quadrature Amplitude Modulation (QAM) is addressed. First, we employ a novel
and efficient methodology that allows for a sparse representation of multiple
users and groups in a fashion similar to Joint Spatial Division and
Multiplexing. Then, the method is generalized to include Orthogonal Frequency
Division Multiplexing (OFDM) for frequency selective channels, resulting in
Combined Frequency and Spatial Division and Multiplexing, a configuration that
offers high flexibility in Massive MIMO systems. A challenge in such system
design is to consider finite alphabet inputs, especially with larger
constellation sizes such as . The proposed methodology is next
applied jointly with the complexity-reducing Per-Group Processing (PGP)
technique, on a per user group basis, in conjunction with QAM modulation and in
simulations, for constellation size up to . We show by numerical results
that the precoders developed offer significantly better performance than the
configuration with no precoder or the plain beamformer and with