329 research outputs found
Massive MIMO has Unlimited Capacity
The capacity of cellular networks can be improved by the unprecedented array
gain and spatial multiplexing offered by Massive MIMO. Since its inception, the
coherent interference caused by pilot contamination has been believed to create
a finite capacity limit, as the number of antennas goes to infinity. In this
paper, we prove that this is incorrect and an artifact from using simplistic
channel models and suboptimal precoding/combining schemes. We show that with
multicell MMSE precoding/combining and a tiny amount of spatial channel
correlation or large-scale fading variations over the array, the capacity
increases without bound as the number of antennas increases, even under pilot
contamination. More precisely, the result holds when the channel covariance
matrices of the contaminating users are asymptotically linearly independent,
which is generally the case. If also the diagonals of the covariance matrices
are linearly independent, it is sufficient to know these diagonals (and not the
full covariance matrices) to achieve an unlimited asymptotic capacity.Comment: To appear in IEEE Transactions on Wireless Communications, 17 pages,
7 figure
Fractional Pilot Reuse in Massive MIMO Systems
Pilot contamination is known to be one of the main impairments for massive
MIMO multi-cell communications. Inspired by the concept of fractional frequency
reuse and by recent contributions on pilot reutilization among non-adjacent
cells, we propose a new pilot allocation scheme to mitigate this effect. The
key idea is to allow users in neighboring cells that are closest to their base
stations to reuse the same pilot sequences. Focusing on the uplink, we obtain
expressions for the overall spectral efficiency per cell for different linear
combining techniques at the base station and use them to obtain both the
optimal pilot reuse parameters and the optimal number of scheduled users.
Numerical results show a remarkable improvement in terms of spectral efficiency
with respect to the existing techniques.Comment: Paper presented at the IEEE ICC 2015 Workshop on 5G & Beyond -
Enabling Technologies and Application
Soft Pilot Reuse and Multi-Cell Block Diagonalization Precoding for Massive MIMO Systems
The users at cell edge of a massive multiple-input multiple-output (MIMO)
system suffer from severe pilot contamination, which leads to poor quality of
service (QoS). In order to enhance the QoS for these edge users, soft pilot
reuse (SPR) combined with multi-cell block diagonalization (MBD) precoding are
proposed. Specifically, the users are divided into two groups according to
their large-scale fading coefficients, referred to as the center users, who
only suffer from modest pilot contamination and the edge users, who suffer from
severe pilot contamination. Based on this distinction, the SPR scheme is
proposed for improving the QoS for the edge users, whereby a cell-center pilot
group is reused for all cell-center users in all cells, while a cell-edge pilot
group is applied for the edge users in the adjacent cells. By extending the
classical block diagonalization precoding to a multi-cell scenario, the MBD
precoding scheme projects the downlink transmit signal onto the null space of
the subspace spanned by the inter-cell channels of the edge users in adjacent
cells. Thus, the inter-cell interference contaminating the edge users' signals
in the adjacent cells can be efficiently mitigated and hence the QoS of these
edge users can be further enhanced. Our theoretical analysis and simulation
results demonstrate that both the uplink and downlink rates of the edge users
are significantly improved, albeit at the cost of the slightly decreased rate
of center users.Comment: 13 pages, 12 figures, accepted for publication in IEEE Transactions
on Vehicular Technology, 201
Low-Complexity Channel Estimation in Large-Scale MIMO using Polynomial Expansion
This paper considers pilot-based channel estimation in large-scale
multiple-input multiple-output (MIMO) communication systems, also known as
"massive MIMO". Unlike previous works on this topic, which mainly considered
the impact of inter-cell disturbance due to pilot reuse (so-called pilot
contamination), we are concerned with the computational complexity. The
conventional minimum mean square error (MMSE) and minimum variance unbiased
(MVU) channel estimators rely on inverting covariance matrices, which has cubic
complexity in the multiplication of number of antennas at each side. Since this
is extremely expensive when there are hundreds of antennas, we propose to
approximate the inversion by an L-order matrix polynomial. A set of
low-complexity Bayesian channel estimators, coined Polynomial ExpAnsion CHannel
(PEACH) estimators, are introduced. The coefficients of the polynomials are
optimized to yield small mean square error (MSE). We show numerically that
near-optimal performance is achieved with low polynomial orders. In practice,
the order L can be selected to balance between complexity and MSE.
Interestingly, pilot contamination is beneficial to the PEACH estimators in the
sense that smaller L can be used to achieve near-optimal MSEs.Comment: Published at IEEE International Symposium on Personal, Indoor and
Mobile Radio Communications (PIMRC 2013), 8-11 September 2013, 6 pages, 4
figures, 1 tabl
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