190 research outputs found
Local Partial Zero-Forcing Precoding for Cell-Free Massive MIMO
Cell-free Massive MIMO (multiple-input multiple-output) is a promising
distributed network architecture for 5G-and-beyond systems. It guarantees
ubiquitous coverage at high spectral efficiency (SE) by leveraging signal
co-processing at multiple access points (APs), aggressive spatial user
multiplexing and extraordinary macro-diversity gain.
In this study, we propose two distributed precoding schemes, referred to as
\textit{local partial zero-forcing} (PZF) and \textit{local protective partial
zero-forcing} (PPZF), that further improve the spectral efficiency by providing
an adaptable trade-off between interference cancelation and boosting of the
desired signal, with no additional front-hauling overhead, and implementable by
APs with very few antennas.
We derive closed-form expressions for the achievable SE under the assumption
of independent Rayleigh fading channel, channel estimation error and pilot
contamination. PZF and PPZF can substantially outperform maximum ratio
transmission and zero-forcing, and their performance is comparable to that
achieved by regularized zero-forcing (RZF), which is a benchmark in the
downlink. Importantly, these closed-form expressions can be employed to devise
optimal (long-term) power control strategies that are also suitable for RZF,
whose closed-form expression for the SE is not available.Comment: This paper was accepted for publication in IEEE Transactions on
Wireless Communications on March 31, 2020. {\copyright} 2020 IEEE. Personal
use of this material is permitted. Permission from IEEE must be obtained for
all other use
Large System Analysis of Linear Precoding in Correlated MISO Broadcast Channels under Limited Feedback
In this paper, we study the sum rate performance of zero-forcing (ZF) and
regularized ZF (RZF) precoding in large MISO broadcast systems under the
assumptions of imperfect channel state information at the transmitter and
per-user channel transmit correlation. Our analysis assumes that the number of
transmit antennas and the number of single-antenna users are large
while their ratio remains bounded. We derive deterministic approximations of
the empirical signal-to-interference plus noise ratio (SINR) at the receivers,
which are tight as . In the course of this derivation, the
per-user channel correlation model requires the development of a novel
deterministic equivalent of the empirical Stieltjes transform of large
dimensional random matrices with generalized variance profile. The
deterministic SINR approximations enable us to solve various practical
optimization problems. Under sum rate maximization, we derive (i) for RZF the
optimal regularization parameter, (ii) for ZF the optimal number of users,
(iii) for ZF and RZF the optimal power allocation scheme and (iv) the optimal
amount of feedback in large FDD/TDD multi-user systems. Numerical simulations
suggest that the deterministic approximations are accurate even for small
.Comment: submitted to IEEE Transactions on Information Theor
Comparison of Linear Precoding Schemes for the Massive MIMO Downlink
978-1-4577-2052-9International audienceWe consider the downlink of a time-division duplexing (TDD) multicell multiuser MIMO system where the base stations (BSs) are equipped with a very large number of antennas. Assuming channel estimation through uplink pilots, arbitrary antenna correlation and user distributions, we derive approximations of achievable rates with linear precoding techniques, namely eigenbeamforming (BF) and regularized zero-forcing (RZF). The approximations are tight in the large system limit with an infinitely large number of antennas and user terminals (UTs), but match our simulations for realistic system dimensions. We further show that a simple RZF precoding scheme can achieve the same performance as BF with one order of magnitude fewer antennas in both uncorrelated and correlated fading channels
AirSync: Enabling Distributed Multiuser MIMO with Full Spatial Multiplexing
The enormous success of advanced wireless devices is pushing the demand for
higher wireless data rates. Denser spectrum reuse through the deployment of
more access points per square mile has the potential to successfully meet the
increasing demand for more bandwidth. In theory, the best approach to density
increase is via distributed multiuser MIMO, where several access points are
connected to a central server and operate as a large distributed multi-antenna
access point, ensuring that all transmitted signal power serves the purpose of
data transmission, rather than creating "interference." In practice, while
enterprise networks offer a natural setup in which distributed MIMO might be
possible, there are serious implementation difficulties, the primary one being
the need to eliminate phase and timing offsets between the jointly coordinated
access points.
In this paper we propose AirSync, a novel scheme which provides not only time
but also phase synchronization, thus enabling distributed MIMO with full
spatial multiplexing gains. AirSync locks the phase of all access points using
a common reference broadcasted over the air in conjunction with a Kalman filter
which closely tracks the phase drift. We have implemented AirSync as a digital
circuit in the FPGA of the WARP radio platform. Our experimental testbed,
comprised of two access points and two clients, shows that AirSync is able to
achieve phase synchronization within a few degrees, and allows the system to
nearly achieve the theoretical optimal multiplexing gain. We also discuss MAC
and higher layer aspects of a practical deployment. To the best of our
knowledge, AirSync offers the first ever realization of the full multiuser MIMO
gain, namely the ability to increase the number of wireless clients linearly
with the number of jointly coordinated access points, without reducing the per
client rate.Comment: Submitted to Transactions on Networkin
Linear Precoding Based on Polynomial Expansion: Large-Scale Multi-Cell MIMO Systems
Large-scale MIMO systems can yield a substantial improvement in spectral
efficiency for future communication systems. Due to the finer spatial
resolution achieved by a huge number of antennas at the base stations, these
systems have shown to be robust to inter-user interference and the use of
linear precoding is asymptotically optimal. However, most precoding schemes
exhibit high computational complexity as the system dimensions increase. For
example, the near-optimal RZF requires the inversion of a large matrix. This
motivated our companion paper, where we proposed to solve the issue in
single-cell multi-user systems by approximating the matrix inverse by a
truncated polynomial expansion (TPE), where the polynomial coefficients are
optimized to maximize the system performance. We have shown that the proposed
TPE precoding with a small number of coefficients reaches almost the
performance of RZF but never exceeds it. In a realistic multi-cell scenario
involving large-scale multi-user MIMO systems, the optimization of RZF
precoding has thus far not been feasible. This is mainly attributed to the high
complexity of the scenario and the non-linear impact of the necessary
regularizing parameters. On the other hand, the scalar weights in TPE precoding
give hope for possible throughput optimization. Following the same methodology
as in the companion paper, we exploit random matrix theory to derive a
deterministic expression for the asymptotic SINR for each user. We also provide
an optimization algorithm to approximate the weights that maximize the
network-wide weighted max-min fairness. The optimization weights can be used to
mimic the user throughput distribution of RZF precoding. Using simulations, we
compare the network throughput of the TPE precoding with that of the suboptimal
RZF scheme and show that our scheme can achieve higher throughput using a TPE
order of only 3
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