216 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
On the Throughput of Large-but-Finite MIMO Networks using Schedulers
This paper studies the sum throughput of the {multi-user}
multiple-input-single-output (MISO) networks in the cases with large but finite
number of transmit antennas and users. Considering continuous and bursty
communication scenarios with different users' data request probabilities, we
derive quasi-closed-form expressions for the maximum achievable throughput of
the networks using optimal schedulers. The results are obtained in various
cases with different levels of interference cancellation. Also, we develop an
efficient scheduling scheme using genetic algorithms (GAs), and evaluate the
effect of different parameters, such as channel/precoding models, number of
antennas/users, scheduling costs and power amplifiers' efficiency, on the
system performance. Finally, we use the recent results on the achievable rates
of finite block-length codes to analyze the system performance in the cases
with short packets. As demonstrated, the proposed GA-based scheduler reaches
(almost) the same throughput as in the exhaustive search-based optimal
scheduler, with substantially less implementation complexity. Moreover, the
power amplifiers' inefficiency and the scheduling delay affect the performance
of the scheduling-based systems significantly
A Light Signalling Approach to Node Grouping for Massive MIMO IoT Networks
Massive MIMO is a promising technology to connect very large numbers of
energy constrained nodes, as it offers both extensive spatial multiplexing and
large array gain. A challenge resides in partitioning the many nodes in groups
that can communicate simultaneously such that the mutual interference is
minimized. We here propose node partitioning strategies that do not require
full channel state information, but rather are based on nodes' respective
directional channel properties. In our considered scenarios, these typically
have a time constant that is far larger than the coherence time of the channel.
We developed both an optimal and an approximation algorithm to partition users
based on directional channel properties, and evaluated them numerically. Our
results show that both algorithms, despite using only these directional channel
properties, achieve similar performance in terms of the minimum
signal-to-interference-plus-noise ratio for any user, compared with a reference
method using full channel knowledge. In particular, we demonstrate that
grouping nodes with related directional properties is to be avoided. We hence
realise a simple partitioning method requiring minimal information to be
collected from the nodes, and where this information typically remains stable
over a long term, thus promoting their autonomy and energy efficiency
Coordinated Multi-cell Beamforming for Massive MIMO: A Random Matrix Approach
We consider the problem of coordinated multi- cell downlink beamforming in
massive multiple input multiple output (MIMO) systems consisting of N cells, Nt
antennas per base station (BS) and K user terminals (UTs) per cell.
Specifically, we formulate a multi-cell beamforming algorithm for massive MIMO
systems which requires limited amount of information exchange between the BSs.
The design objective is to minimize the aggregate transmit power across all the
BSs subject to satisfying the user signal to interference noise ratio (SINR)
constraints. The algorithm requires the BSs to exchange parameters which can be
computed solely based on the channel statistics rather than the instantaneous
CSI. We make use of tools from random matrix theory to formulate the
decentralized algorithm. We also characterize a lower bound on the set of
target SINR values for which the decentralized multi-cell beamforming algorithm
is feasible. We further show that the performance of our algorithm
asymptotically matches the performance of the centralized algorithm with full
CSI sharing. While the original result focuses on minimizing the aggregate
transmit power across all the BSs, we formulate a heuristic extension of this
algorithm to incorporate a practical constraint in multi-cell systems, namely
the individual BS transmit power constraints. Finally, we investigate the
impact of imperfect CSI and pilot contamination effect on the performance of
the decentralized algorithm, and propose a heuristic extension of the algorithm
to accommodate these issues. Simulation results illustrate that our algorithm
closely satisfies the target SINR constraints and achieves minimum power in the
regime of massive MIMO systems. In addition, it also provides substantial power
savings as compared to zero-forcing beamforming when the number of antennas per
BS is of the same orders of magnitude as the number of UTs per cell
- …