180 research outputs found
Scaling up MIMO: Opportunities and Challenges with Very Large Arrays
This paper surveys recent advances in the area of very large MIMO systems.
With very large MIMO, we think of systems that use antenna arrays with an
order of magnitude more elements than in systems being built today, say a
hundred antennas or more. Very large MIMO entails an unprecedented number of
antennas simultaneously serving a much smaller number of terminals. The
disparity in number emerges as a desirable operating condition and a practical
one as well. The number of terminals that can be simultaneously served is
limited, not by the number of antennas, but rather by our inability to acquire
channel-state information for an unlimited number of terminals. Larger numbers
of terminals can always be accommodated by combining very large MIMO technology
with conventional time- and frequency-division multiplexing via OFDM. Very
large MIMO arrays is a new research field both in communication theory,
propagation, and electronics and represents a paradigm shift in the way of
thinking both with regards to theory, systems and implementation. The ultimate
vision of very large MIMO systems is that the antenna array would consist of
small active antenna units, plugged into an (optical) fieldbus.Comment: Accepted for publication in the IEEE Signal Processing Magazine,
October 201
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
Secrecy Sum-Rates with Regularized Channel Inversion Precoding under Imperfect CSI at the Transmitter
In this paper, we study the performance of regularized channel inversion
precoding in MISO broadcast channels with confidential messages under imperfect
channel state information at the transmitter (CSIT). We obtain an approximation
for the achievable secrecy sum-rate which is almost surely exact as the number
of transmit antennas and the number of users grow to infinity in a fixed ratio.
Simulations prove this anaylsis accurate even for finite-size systems. For FDD
systems, we determine how the CSIT error must scale with the SNR, and we derive
the number of feedback bits required to ensure a constant high-SNR rate gap to
the case with perfect CSIT. For TDD systems, we study the optimum amount of
channel training that maximizes the high-SNR secrecy sum-rate.Comment: IEEE International Conference on Acoustics, Speech, and Signal
Processing (ICASSP), May 2013. arXiv admin note: text overlap with
arXiv:1304.585
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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