206 research outputs found
Fundamental Limits in Correlated Fading MIMO Broadcast Channels: Benefits of Transmit Correlation Diversity
We investigate asymptotic capacity limits of the Gaussian MIMO broadcast
channel (BC) with spatially correlated fading to understand when and how much
transmit correlation helps the capacity. By imposing a structure on channel
covariances (equivalently, transmit correlations at the transmitter side) of
users, also referred to as \emph{transmit correlation diversity}, the impact of
transmit correlation on the power gain of MIMO BCs is characterized in several
regimes of system parameters, with a particular interest in the large-scale
array (or massive MIMO) regime. Taking the cost for downlink training into
account, we provide asymptotic capacity bounds of multiuser MIMO downlink
systems to see how transmit correlation diversity affects the system
multiplexing gain. We make use of the notion of joint spatial division and
multiplexing (JSDM) to derive the capacity bounds. It is advocated in this
paper that transmit correlation diversity may be of use to significantly
increase multiplexing gain as well as power gain in multiuser MIMO systems. In
particular, the new type of diversity in wireless communications is shown to
improve the system multiplexing gain up to by a factor of the number of degrees
of such diversity. Finally, performance limits of conventional large-scale MIMO
systems not exploiting transmit correlation are also characterized.Comment: 29 pages, 8 figure
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
A Two-Stage 2D Channel Extrapolation Scheme for TDD 5G NR Systems
Recently, channel extrapolation has been widely investigated in frequency
division duplex (FDD) massive MIMO systems. However, in time division duplex
(TDD) fifth generation (5G) new radio (NR) systems, the channel extrapolation
problem also arises due to the hopping uplink pilot pattern, which has not been
fully researched yet. This paper addresses this gap by formulating a channel
extrapolation problem in TDD massive MIMO-OFDM systems for 5G NR, incorporating
imperfection factors. A novel two-stage two-dimensional (2D) channel
extrapolation scheme in both frequency and time domain is proposed, designed to
mitigate the negative effects of imperfection factors and ensure high-accuracy
channel estimation. Specifically, in the channel estimation stage, we propose a
novel multi-band and multi-timeslot based high-resolution parameter estimation
algorithm to achieve 2D channel extrapolation in the presence of imperfection
factors. Then, to avoid repeated multi-timeslot based channel estimation, a
channel tracking stage is designed during the subsequent time instants, in
which a sparse Markov channel model is formulated to capture the dynamic
sparsity of massive MIMO-OFDM channels under the influence of imperfection
factors. Next, an expectation-maximization (EM) based compressive channel
tracking algorithm is designed to jointly estimate unknown imperfection and
channel parameters by exploiting the high-resolution prior information of the
delay/angle parameters from the previous timeslots. Simulation results
underscore the superior performance of our proposed channel extrapolation
scheme over baselines
Novel transmission and beamforming strategies for multiuser MIMO with various CSIT types
In multiuser multi-antenna wireless systems, the transmission and beamforming strategies that achieve the sum rate capacity depend critically on the acquisition of perfect Channel State Information at the Transmitter (CSIT).
Accordingly, a high-rate low-latency feedback link between the receiver and the transmitter is required to keep the latter accurately and instantaneously informed about the CSI.
In realistic wireless systems, however, only imperfect CSIT is achievable due to pilot contamination, estimation error, limited feedback and delay, etc.
As an intermediate solution, this thesis investigates novel transmission strategies suitable for various imperfect CSIT scenarios and the associated beamforming techniques to optimise the rate performance.
First, we consider a two-user Multiple-Input-Single-Output (MISO) Broadcast Channel (BC) under statistical and delayed CSIT.
We mainly focus on linear beamforming and power allocation designs for ergodic sum rate maximisation.
The proposed designs enable higher sum rate than the conventional designs.
Interestingly, we propose a novel transmission framework which makes better use of statistical and delayed CSIT and smoothly bridges between statistical CSIT-based strategies and delayed CSIT-based strategies.
Second, we consider a multiuser massive MIMO system under partial and statistical CSIT.
In order to tackle multiuser interference incurred by partial CSIT, a Rate-Splitting (RS) transmission strategy has been proposed recently.
We generalise the idea of RS into the large-scale array.
By further exploiting statistical CSIT, we propose a novel framework Hierarchical-Rate-Splitting that is particularly suited to massive MIMO systems.
Third, we consider a multiuser Millimetre Wave (mmWave) system with hybrid analog/digital precoding under statistical and quantised CSIT.
We leverage statistical CSIT to design digital precoder for interference mitigation while all feedback overhead is reserved for precise analog beamforming.
For very limited feedback and/or very sparse channels, the proposed precoding scheme yields higher sum rate than the conventional precoding schemes under a fixed total feedback constraint.
Moreover, a RS transmission strategy is introduced to further tackle the multiuser interference, enabling remarkable saving in feedback overhead compared with conventional transmission strategies.
Finally, we investigate the downlink hybrid precoding for physical layer multicasting with a limited number of RF chains.
We propose a low complexity algorithm to compute the analog precoder that achieves near-optimal max-min performance.
Moreover, we derive a simple condition under which the hybrid precoding driven by a limited number of RF chains incurs no loss of optimality with respect to the fully digital precoding case.Open Acces
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
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