1,340 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
Cooperative Downlink Multicell Preprocessing Relying on Reduced-Rate Back-Haul Data Exchange
Different-complexity multicell preprocessing (MCP) schemes employing distributed signal-to-interference leakageplus-noise ratio (SILNR) precoding techniques are proposed, which require reduced back-haul data exchange in comparison with the conventional MCP structure. Our results demonstrate that the proposed structures are capable of increasing the throughput achievable in the cell-edge area while offering different geographic rate profile distributions, as well as meeting different delay requirements
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
Optimality Properties, Distributed Strategies, and Measurement-Based Evaluation of Coordinated Multicell OFDMA Transmission
The throughput of multicell systems is inherently limited by interference and
the available communication resources. Coordinated resource allocation is the
key to efficient performance, but the demand on backhaul signaling and
computational resources grows rapidly with number of cells, terminals, and
subcarriers. To handle this, we propose a novel multicell framework with
dynamic cooperation clusters where each terminal is jointly served by a small
set of base stations. Each base station coordinates interference to neighboring
terminals only, thus limiting backhaul signalling and making the framework
scalable. This framework can describe anything from interference channels to
ideal joint multicell transmission.
The resource allocation (i.e., precoding and scheduling) is formulated as an
optimization problem (P1) with performance described by arbitrary monotonic
functions of the signal-to-interference-and-noise ratios (SINRs) and arbitrary
linear power constraints. Although (P1) is non-convex and difficult to solve
optimally, we are able to prove: 1) Optimality of single-stream beamforming; 2)
Conditions for full power usage; and 3) A precoding parametrization based on a
few parameters between zero and one. These optimality properties are used to
propose low-complexity strategies: both a centralized scheme and a distributed
version that only requires local channel knowledge and processing. We evaluate
the performance on measured multicell channels and observe that the proposed
strategies achieve close-to-optimal performance among centralized and
distributed solutions, respectively. In addition, we show that multicell
interference coordination can give substantial improvements in sum performance,
but that joint transmission is very sensitive to synchronization errors and
that some terminals can experience performance degradations.Comment: Published in IEEE Transactions on Signal Processing, 15 pages, 7
figures. This version corrects typos related to Eq. (4) and Eq. (28
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