655 research outputs found
Large System Analysis of Power Normalization Techniques in Massive MIMO
Linear precoding has been widely studied in the context of Massive
multiple-input-multiple-output (MIMO) together with two common power
normalization techniques, namely, matrix normalization (MN) and vector
normalization (VN). Despite this, their effect on the performance of Massive
MIMO systems has not been thoroughly studied yet. The aim of this paper is to
fulfill this gap by using large system analysis. Considering a system model
that accounts for channel estimation, pilot contamination, arbitrary pathloss,
and per-user channel correlation, we compute tight approximations for the
signal-to-interference-plus-noise ratio and the rate of each user equipment in
the system while employing maximum ratio transmission (MRT), zero forcing (ZF),
and regularized ZF precoding under both MN and VN techniques. Such
approximations are used to analytically reveal how the choice of power
normalization affects the performance of MRT and ZF under uncorrelated fading
channels. It turns out that ZF with VN resembles a sum rate maximizer while it
provides a notion of fairness under MN. Numerical results are used to validate
the accuracy of the asymptotic analysis and to show that in Massive MIMO,
non-coherent interference and noise, rather than pilot contamination, are often
the major limiting factors of the considered precoding schemes.Comment: 12 pages, 3 figures, Accepted for publication in the IEEE
Transactions on Vehicular Technolog
Massive MU-MIMO Downlink TDD Systems with Linear Precoding and Downlink Pilots
We consider a massive MU-MIMO downlink time-division duplex system where a
base station (BS) equipped with many antennas serves several single-antenna
users in the same time-frequency resource. We assume that the BS uses linear
precoding for the transmission. To reliably decode the signals transmitted from
the BS, each user should have an estimate of its channel. In this work, we
consider an efficient channel estimation scheme to acquire CSI at each user,
called beamforming training scheme. With the beamforming training scheme, the
BS precodes the pilot sequences and forwards to all users. Then, based on the
received pilots, each user uses minimum mean-square error channel estimation to
estimate the effective channel gains. The channel estimation overhead of this
scheme does not depend on the number of BS antennas, and is only proportional
to the number of users. We then derive a lower bound on the capacity for
maximum-ratio transmission and zero-forcing precoding techniques which enables
us to evaluate the spectral efficiency taking into account the spectral
efficiency loss associated with the transmission of the downlink pilots.
Comparing with previous work where each user uses only the statistical channel
properties to decode the transmitted signals, we see that the proposed
beamforming training scheme is preferable for moderate and low-mobility
environments.Comment: Allerton Conference on Communication, Control, and Computing,
Urbana-Champaign, Illinois, Oct. 201
Joint Power Allocation and User Association Optimization for Massive MIMO Systems
This paper investigates the joint power allocation and user association
problem in multi-cell Massive MIMO (multiple-input multiple-output) downlink
(DL) systems. The target is to minimize the total transmit power consumption
when each user is served by an optimized subset of the base stations (BSs),
using non-coherent joint transmission. We first derive a lower bound on the
ergodic spectral efficiency (SE), which is applicable for any channel
distribution and precoding scheme. Closed-form expressions are obtained for
Rayleigh fading channels with either maximum ratio transmission (MRT) or zero
forcing (ZF) precoding. From these bounds, we further formulate the DL power
minimization problems with fixed SE constraints for the users. These problems
are proved to be solvable as linear programs, giving the optimal power
allocation and BS-user association with low complexity. Furthermore, we
formulate a max-min fairness problem which maximizes the worst SE among the
users, and we show that it can be solved as a quasi-linear program. Simulations
manifest that the proposed methods provide good SE for the users using less
transmit power than in small-scale systems and the optimal user association can
effectively balance the load between BSs when needed. Even though our framework
allows the joint transmission from multiple BSs, there is an overwhelming
probability that only one BS is associated with each user at the optimal
solution.Comment: 16 pages, 12 figures, Accepted by IEEE Trans. Wireless Commu
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