80 research outputs found
AoD-Adaptive Channel Feedback for FDD Massive MIMO Systems With Multiple-Antenna Users
AoD-Adaptive Channel Feedback for FDD Massive MIMO Systems With Multiple-Antenna User
Channel feedback in FDD massive MIMO systems with multiple-antenna users
In this thesis, we consider the problem of Angle of Departure (AoD) based channel feedback in Frequency Division Duplex (FDD) massive Multiple- Input Multiple-Output (MIMO) systems with multiple antennas at the users. We consider the use of Zero-Forcing Block Diagonalization (BD) as the down- link precoding scheme. We consider two different cases; one in which the number of streams intended for a user equals the number of antennas at that user and the other case in which the number of streams is less than the number of user antennas. BD requires the feedback of the subspace spanned by the channel matrix at the user or a subspace of it in the case of having a smaller number of streams than the number of antennas at a specific user. Based on our channel model, we propose a channel feedback scheme that requires less feedback overhead compared to feeding back the whole channel matrix. Then, we quantify the rate gap between the rate of the system with perfect Channel State Information (CSI) at the massive MIMO Basestation (BS) and our proposed channel feedback scheme for a given number of feedback bits. Finally, we design feedback codebooks based on optimal subspace packing in the Grassmannian manifold. We show that our proposed codes achieve performance that is very close to the performance of the system with perfect CSI at the BS. We also propose a vector quantization scheme to quantize the channel matrix of the user when optimal power allocation across multiple streams is adopted. Sim- ulation results show that the vector quantization scheme combined with power optimization across the streams outperforms the subspace quantiza- tion scheme at the low SNR regime. However, the situation is reversed at high SNR levels and subspace quantization with uniform power allocation becomes better
Exploitation of Robust AoA Estimation and Low Overhead Beamforming in mmWave MIMO System
The limited spectral resource for wireless communications and dramatic proliferation of new applications and services directly necessitate the exploitation of millimeter wave (mmWave) communications. One critical enabling technology for mmWave communications is multi-input multi-output (MIMO), which enables other important physical layer techniques, specifically beamforming and antenna array based angle of arrival (AoA) estimation. Deployment of beamforming and AoA estimation has many challenges. Significant training and feedback overhead is required for beamforming, while conventional AoA estimation methods are not fast or robust. Thus, in this thesis, new algorithms are designed for low overhead beamforming, and robust AoA estimation with significantly reduced signal samples (snapshots).
The basic principle behind the proposed low overhead beamforming algorithm in time-division duplex (TDD) systems is to increase the beam serving period for the reduction of the feedback frequency. With the knowledge of location and speed of each candidate user equipment (UE), the codeword can be selected from the designed multi-pattern codebook, and the corresponding serving period can be estimated. The UEs with long serving period and low interference are selected and served simultaneously. This algorithm is proved to be effective in keeping the high data rate of conventional codebook-based beamforming, while the feedback required for codeword selection can be cut down.
A fast and robust AoA estimation algorithm is proposed as the basis of the low overhead beamforming for frequency-division duplex (FDD) systems. This algorithm utilizes uplink transmission signals to estimate the real-time AoA for angle-based beamforming in environments with different signal to noise ratios (SNR). Two-step neural network models are designed for AoA estimation. Within the angular group classified by the first model, the second model further estimates AoA with high accuracy. It is proved that these AoA estimation models work well with few signal snapshots, and are robust to applications in low SNR environments. The proposed AoA estimation algorithm based beamforming generates beams without using reference signals. Therefore, the low overhead beamforming can be achieved in FDD systems.
With the support of proposed algorithms, the mmWave resource can be leveraged to meet challenging requirements of new applications and services in wireless communication systems
MIMO Channel Information Feedback Using Deep Recurrent Network
In a multiple-input multiple-output (MIMO) system, the availability of
channel state information (CSI) at the transmitter is essential for performance
improvement. Recent convolutional neural network (NN) based techniques show
competitive ability in realizing CSI compression and feedback. By introducing a
new NN architecture, we enhance the accuracy of quantized CSI feedback in MIMO
communications. The proposed NN architecture invokes a module named long
short-term memory (LSTM) which admits the NN to benefit from exploiting
temporal and frequency correlations of wireless channels. Compromising
performance with complexity, we further modify the NN architecture with a
significantly reduced number of parameters to be trained. Finally, experiments
show that the proposed NN architectures achieve better performance in terms of
both CSI compression and recovery accuracy
Blind CSI acquisition for multi-antenna interference mitigation in 5G networks
Future wireless communication networks are required to satisfy the increasing demands
of traffic and capacity. The upcoming fifth generation (5G) of the cellular
technology is expected to meet 1000 times the capacity that of the current fourth
generation (4G). These tight specifications introduce a new set of research challenges.
However, interference has always been the bottleneck in cellular communications.
Thus, towards the vision of the 5G, massive multi-input multi-output (mMIMO) and
interference alignment (IA) are key transmission technologies to fulfil the future requirements,
by controlling the residual interference.
By equipping the base-station (BS) with a large number of transmit antennas, e.g,
tens of hundreds of antennas, a mMIMO system can theoretically achieve significant
capacity with limited interference, where many user equipment (UEs) can be served
simultaneously at the same time and frequency resources. A mMIMO offers great
spatial degrees of freedom (DoFs), which boost the total network capacity without
increasing transmission power or bandwidth. However, the majority of the recent
mMIMO investigations are based on theoretical channels with independent and identically
distributed (i.i.d) Gaussian distribution, which facilitates the computation of
closed-form rate expressions. Nonetheless, practical channels are not spatially uncorrelated,
where the BS receives different power ratios across different spatial directions
between the same transmitting and receiving antennas. Thus, it is important to understand the behavior of such new technology with practical channel modeling.
Alternatively, IA is known to break the bottleneck between the capacity of the
network and the overall spectral efficiency (SE), where a performance degradation
is observed at a certain level of connected user capacity, due to the overwhelming
inter-user interference. Theoretically, IA guarantees a linear relationship between
half of the overall network SE and the online capacity by aligning interference from
all transmitters inside one spatial signal subspace, leaving the other subspace for
desired transmission. However, IA has tight feasibility conditions in practice including
high precision channel state information at transmitter (CSIT), which leads to severe
feedback overhead.
In this thesis, high-precision blind CSIT algorithms are developed under different
transmission technologies. We first consider the CSIT acquisition problem in MIMO
IA systems. Proposed spatial channel estimation for MIMO-IA systems (SCEIA)
shows great offered spatial degrees of freedom which contributes to approaching the
performance of the perfect-CSIT case, without the requirements of channel quantization
or user feedback overhead. In massive MIMO setups, proposed CSIT strategy
offered scalable performance with the number of the transmit antennas. The effect
of the non-stationary channel characteristics, which appears with very large antenna
arrays, is minimized due to the effective scanning precision of the proposed strategy.
Finally, we extend the system model to the full dimensional space, where users are distributed
across the two dimensions of the cell space (azimuthal/elevation). Proposed
directional spatial channel estimation (D-SCE) scans the 3D cell space and effectively
attains additional CSIT and beamforming gains. In all cases, a list of comparisons
with state-of-the-art schemes from academia and industry is performed to show the
performance improvement of the proposed CSIT strategies
Performance Analysis of Channel Extrapolation in FDD Massive MIMO Systems
Channel estimation for the downlink of frequency division duplex (FDD)
massive MIMO systems is well known to generate a large overhead as the amount
of training generally scales with the number of transmit antennas in a MIMO
system. In this paper, we consider the solution of extrapolating the channel
frequency response from uplink pilot estimates to the downlink frequency band,
which completely removes the training overhead. We first show that conventional
estimators fail to achieve reasonable accuracy. We propose instead to use
high-resolution channel estimation. We derive theoretical lower bounds (LB) for
the mean squared error (MSE) of the extrapolated channel. Assuming that the
paths are well separated, the LB is simplified in an expression that gives
considerable physical insight. It is then shown that the MSE is inversely
proportional to the number of receive antennas while the extrapolation
performance penalty scales with the square of the ratio of the frequency offset
and the training bandwidth. The channel extrapolation performance is validated
through numeric simulations and experimental measurements taken in an anechoic
chamber. Our main conclusion is that channel extrapolation is a viable solution
for FDD massive MIMO systems if accurate system calibration is performed and
favorable propagation conditions are present.Comment: arXiv admin note: substantial text overlap with arXiv:1902.0684
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