314 research outputs found
Achieving "Massive MIMO" Spectral Efficiency with a Not-so-Large Number of Antennas
The main focus and contribution of this paper is a novel network-MIMO TDD
architecture that achieves spectral efficiencies comparable with "Massive
MIMO", with one order of magnitude fewer antennas per active user per cell. The
proposed architecture is based on a family of network-MIMO schemes defined by
small clusters of cooperating base stations, zero-forcing multiuser MIMO
precoding with suitable inter-cluster interference constraints, uplink pilot
signals reuse across cells, and frequency reuse. The key idea consists of
partitioning the users population into geographically determined "bins", such
that all users in the same bin are statistically equivalent, and use the
optimal network-MIMO architecture in the family for each bin. A scheduler takes
care of serving the different bins on the time-frequency slots, in order to
maximize a desired network utility function that captures some desired notion
of fairness. This results in a mixed-mode network-MIMO architecture, where
different schemes, each of which is optimized for the served user bin, are
multiplexed in time-frequency. In order to carry out the performance analysis
and the optimization of the proposed architecture in a clean and
computationally efficient way, we consider the large-system regime where the
number of users, the number of antennas, and the channel coherence block length
go to infinity with fixed ratios. The performance predicted by the large-system
asymptotic analysis matches very well the finite-dimensional simulations.
Overall, the system spectral efficiency obtained by the proposed architecture
is similar to that achieved by "Massive MIMO", with a 10-fold reduction in the
number of antennas at the base stations (roughly, from 500 to 50 antennas).Comment: Full version with appendice (proofs of theorems). A shortened version
without appendice was submitted to IEEE Trans. on Wireless Commun. Appendix B
was revised after submissio
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
Resource allocation for transmit hybrid beamforming in decoupled millimeter wave multiuser-MIMO downlink
This paper presents a study on joint radio resource allocation and hybrid precoding in multicarrier massive multiple-input multiple-output communications for 5G cellular networks. In this paper, we present the resource allocation algorithm to maximize the proportional fairness (PF) spectral efficiency under the per subchannel power and the beamforming rank constraints. Two heuristic algorithms are designed. The proportional fairness hybrid beamforming algorithm provides the transmit precoder with a proportional fair spectral efficiency among users for the desired number of radio-frequency (RF) chains. Then, we transform the number of RF chains or rank constrained optimization problem into convex semidefinite programming (SDP) problem, which can be solved by standard techniques. Inspired by the formulated convex SDP problem, a low-complexity, two-step, PF-relaxed optimization algorithm has been provided for the formulated convex optimization problem. Simulation results show that the proposed suboptimal solution to the relaxed optimization problem is near-optimal for the signal-to-noise ratio SNR <= 10 dB and has a performance gap not greater than 2.33 b/s/Hz within the SNR range 0-25 dB. It also outperforms the maximum throughput and PF-based hybrid beamforming schemes for sum spectral efficiency, individual spectral efficiency, and fairness index
- …