111 research outputs found
Analysis of data-aided channel tracking for hybrid massive MIMO systems in millimeter wave communications
As the data traffic in future wireless communications will explosively grow up to 1000
folds by the deployment of 5G, several technologies are emerging to satisfy this demand, including
massive multiple-input multiple-output (MIMO), millimeter wave(mmWave) communications,
Non-Orthogonal Multiple Access (NOMA), etc. The combination of millimeter
wave communication and massive MIMO is a promising solution since it can provide tens
of GHz bandwidth by fundamentally exploring higher unoccupied spectrum resources. As
the wavelength of higher frequency shrinks, it is possible to design more compact antenna
array with a very large number of antennas. However, this will cause enormous hardware
cost, energy consumption and computation complexity of decent RF(Radio Frequency)
chains. To this end, spatial sparsity is widely explored to enable hybrid mmWave massive
MIMO systems with limited RF chains to achieve high spectral and energy efficiency.
On the other hand, channel estimation problem for systems with limited RF chains
is quite challenging due to the unaffordable overhead. To be specific, the conventional
pilot-based channel estimation requires to repeatedly transmit the same pilot because only
a limited number of antennas will be activated for each time slot. Therefore, it consumes
a huge amount of temporal and spectral resources. To overcome this problem, channel
estimation for mmWave massive MIMO systems is still an on-going research area. Among
plenty of candidates, channel tracking is the most promising one. To achieve the extremely
low cost and complexity, which is also the greatest motivation of this thesis, data-aided
channel tracking method is thoroughly investigated with closed-form CRLB(Cram´er-Rao
lower bound). In this thesis, data-aided channel tracking systems with different types of
antenna, including ULA(Uniform Linear Antenna array), DLA(Discrete Lens Antenna ar
ray) and UPA(Uniform Planar Antenna array), are comprehensively studied and proposed,
and the closed-form expressions of the corresponding CRLBs are carefully derived. The
numerical results of the simulations for each case are shown respectively, and they reveal
that the performance of the proposed data-aided channel tracking system approaches the
CRLB very well.
In addition, to further explore the data-aided channel tracking system, the multi-user
scenario is investigated in this thesis. This is motivated by the highway and high-speed
railway application, where overtaking operation happens frequently. In this case, the users
in the same beam suffer from high channel interference, thus degrading the channel estimation
performance or even causing outage. To deal with this issue, we proposed an
estimated SER(Symbol Error Rate) metric to indicate if a scheduling operation is necessary
to be taken place and restart of the whole channel tracking system is required. This
metric is included as the Update phase in the proposed channel tracking method for multiuser
scenario with DLA. The theoretical SER closed-form expression is also derived for
multi-user data detection. The numerical results of the simulations verified the theoretical
SER expression, and the scheduling metric based on the estimated SER performance is
also discussed
Downlink Extrapolation for FDD Multiple Antenna Systems Through Neural Network Using Extracted Uplink Path Gains
When base stations (BSs) are deployed with multiple antennas, they need to
have downlink (DL) channel state information (CSI) to optimize downlink
transmissions by beamforming. The DL CSI is usually measured at mobile stations
(MSs) through DL training and fed back to the BS in frequency division
duplexing (FDD). The DL training and uplink (UL) feedback might become
infeasible due to insufficient coherence time interval when the channel rapidly
changes due to high speed of MSs. Without the feedback from MSs, it may be
possible for the BS to directly obtain the DL CSI using the inherent relation
of UL and DL channels even in FDD, which is called DL extrapolation. Although
the exact relation would be highly nonlinear, previous studies have shown that
a neural network (NN) can be used to estimate the DL CSI from the UL CSI at the
BS. Most of previous works on this line of research trained the NN using full
dimensional UL and DL channels; however, the NN training complexity becomes
severe as the number of antennas at the BS increases. To reduce the training
complexity and improve DL CSI estimation quality, this paper proposes a novel
DL extrapolation technique using simplified input and output of the NN. It is
shown through many measurement campaigns that the UL and DL channels still
share common components like path delays and angles in FDD. The proposed
technique first extracts these common coefficients from the UL and DL channels
and trains the NN only using the path gains, which depend on frequency bands,
with reduced dimension compared to the full UL and DL channels. Extensive
simulation results show that the proposed technique outperforms the
conventional approach, which relies on the full UL and DL channels to train the
NN, regardless of the speed of MSs.Comment: accepted for IEEE Acces
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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