298 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
Advanced Quantizer Designs for FDD-Based FD-MIMO Systems Using Uniform Planar Arrays
Massive multiple-input multiple-output (MIMO) systems, which utilize a large
number of antennas at the base station, are expected to enhance network
throughput by enabling improved multiuser MIMO techniques. To deploy many
antennas in reasonable form factors, base stations are expected to employ
antenna arrays in both horizontal and vertical dimensions, which is known as
full-dimension (FD) MIMO. The most popular two-dimensional array is the uniform
planar array (UPA), where antennas are placed in a grid pattern. To exploit the
full benefit of massive MIMO in frequency division duplexing (FDD), the
downlink channel state information (CSI) should be estimated, quantized, and
fed back from the receiver to the transmitter. However, it is difficult to
accurately quantize the channel in a computationally efficient manner due to
the high dimensionality of the massive MIMO channel. In this paper, we develop
both narrowband and wideband CSI quantizers for FD-MIMO taking the properties
of realistic channels and the UPA into consideration. To improve quantization
quality, we focus on not only quantizing dominant radio paths in the channel,
but also combining the quantized beams. We also develop a hierarchical beam
search approach, which scans both vertical and horizontal domains jointly with
moderate computational complexity. Numerical simulations verify that the
performance of the proposed quantizers is better than that of previous CSI
quantization techniques.Comment: 15 pages, 6 figure
Feedback Mechanisms for Centralized and Distributed Mobile Systems
The wireless communication market is expected to witness considerable growth in the immediate future due to increasing smart device usage to access real-time data. Mobile devices become the predominant method of Internet access via cellular networks (4G/5G) and the onset of virtual reality (VR), ushering in the wide deployment of multiple bands, ranging from TVWhite Spaces to cellular/WiFi bands and on to mmWave. Multi-antenna techniques have been considered to be promising approaches in telecommunication to optimize the utilization of radio spectrum and minimize the cost of system construction. The performance of multiple antenna technology depends on the utilization of radio propagation properties and feedback of such information in a timely manner. However, when a signal is transmitted, it is usually dispersed over time coming over different paths of different lengths due to reflections from obstacles or affected by Doppler shift in mobile environments. This motivates the design of novel feedback mechanisms that improve the performance of multi-antenna systems. Accurate channel state information (CSI) is essential to increasing throughput in multiinput, multi-output (MIMO) systems with digital beamforming. Channel-state information for the operation of MIMO schemes (such as transmit diversity or spatial multiplexing) can be acquired by feedback of CSI reports in the downlink direction, or inferred from uplink measurements assuming perfect channel reciprocity (CR). However, most works make the assumption that channels are perfectly reciprocal. This assumption is often incorrect in practice due to poor channel estimation and imperfect channel feedback. Instead, experiments have demonstrated that channel reciprocity can be easily broken by multiple factors. Specifically, channel reciprocity error (CRE) introduced by transmitter-receiver imbalance have been widely studied by both simulations and experiments, and the impact of mobility and estimation error have been fully investigated in this thesis. In particular, unmanned aerial vehicles (UAVs) have asymmetric behavior when communicating with one another and to the ground, due to differences in altitude that frequently occur. Feedback mechanisms are also affected by channel differences caused by the user’s body. While there has been work to specifically quantify the losses in signal reception, there has been little work on how these channel differences affect feedback mechanisms.
In this dissertation, we perform system-level simulations, implement design with a software defined radio platform, conduct in-field experiments for various wireless communication systems to analyze different channel feedback mechanisms. To explore the feedback mechanism, we then explore two specific real world scenarios, including UAV-based beamforming communications, and user-induced feedback systems
A Tractable Approach to Coverage and Rate in Cellular Networks
Cellular networks are usually modeled by placing the base stations on a grid,
with mobile users either randomly scattered or placed deterministically. These
models have been used extensively but suffer from being both highly idealized
and not very tractable, so complex system-level simulations are used to
evaluate coverage/outage probability and rate. More tractable models have long
been desirable. We develop new general models for the multi-cell
signal-to-interference-plus-noise ratio (SINR) using stochastic geometry. Under
very general assumptions, the resulting expressions for the downlink SINR CCDF
(equivalent to the coverage probability) involve quickly computable integrals,
and in some practical special cases can be simplified to common integrals
(e.g., the Q-function) or even to simple closed-form expressions. We also
derive the mean rate, and then the coverage gain (and mean rate loss) from
static frequency reuse. We compare our coverage predictions to the grid model
and an actual base station deployment, and observe that the proposed model is
pessimistic (a lower bound on coverage) whereas the grid model is optimistic,
and that both are about equally accurate. In addition to being more tractable,
the proposed model may better capture the increasingly opportunistic and dense
placement of base stations in future networks.Comment: Submitted to IEEE Transactions on Communication
Scaling up MIMO: Opportunities and Challenges with Very Large Arrays
This paper surveys recent advances in the area of very large MIMO systems.
With very large MIMO, we think of systems that use antenna arrays with an
order of magnitude more elements than in systems being built today, say a
hundred antennas or more. Very large MIMO entails an unprecedented number of
antennas simultaneously serving a much smaller number of terminals. The
disparity in number emerges as a desirable operating condition and a practical
one as well. The number of terminals that can be simultaneously served is
limited, not by the number of antennas, but rather by our inability to acquire
channel-state information for an unlimited number of terminals. Larger numbers
of terminals can always be accommodated by combining very large MIMO technology
with conventional time- and frequency-division multiplexing via OFDM. Very
large MIMO arrays is a new research field both in communication theory,
propagation, and electronics and represents a paradigm shift in the way of
thinking both with regards to theory, systems and implementation. The ultimate
vision of very large MIMO systems is that the antenna array would consist of
small active antenna units, plugged into an (optical) fieldbus.Comment: Accepted for publication in the IEEE Signal Processing Magazine,
October 201
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
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Analysis of millimeter wave and massive MIMO cellular networks
Millimeter wave (mmWave) communication and massive multiple-input multiple-output (MIMO) are promising techniques to increase system capacity in 5G cellular networks. The prior frameworks for conventional cellular systems do not directly apply to analyze mmWave or massive MIMO networks, as (i) mmWave cellular networks differ in the different propagation conditions and hardware constraints; and (ii) with a order of magnitude more antennas than conventional multi-user MIMO systems, massive MIMO systems will be operated in time-division duplex (TDD) mode, which renders pilot contamination a primary limiting factor. In this dissertation, I develop stochastic geometry frameworks to analyze the system-level performance of mmWave, sub-6 GHz massive MIMO, and mmWave massive MIMO cellular networks. The proposed models capture the key features of each technique, and allow for tractable signal-to-interference-plus-noise ratio (SINR) and rate analyses. In the first contribution, I develop an mmWave cellular network model that incorporates the blockage effect and directional beamforming, and analyze the SINR and rate distributions as functions of the base station density, blockage parameters, and antenna geometry. The analytical results demonstrate that with a sufficiently dense base station deployment, mmWave cellular networks are capable to achieve comparable SINR coverage and much higher rates than conventional networks. In my second contribution, I analyze the uplink SINR and rate in sub-6 GHz massive MIMO networks with the incorporation of pilot contamination and fractional power control. Based on the analysis, I show scaling laws between the number of antennas and scheduled users per cell that maintain the uplink signal-to-interference ratio (SIR) distributions are different for maximum ratio combining (MRC) and zero-forcing (ZF) receivers. In my third contribution, I extend the sub-6 GHz massive MIMO model to mmWave frequencies, by incorporating key mmWave features. I leverage the proposed model to investigate the asymptotic SINR performance, when the number of antennas goes to infinity. Numerical results show that mmWave massive MIMO outperforms its sub-6 GHz counterpart in cell throughput with a dense base station deployment, while the reverse can be true with a low base station density.Electrical and Computer Engineerin
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