233 research outputs found
User Selection and Precoding Techniques for Rate Maximization in Broadcast MISO Systems
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Multi-user Linear Precoding for Multi-polarized Massive MIMO System under Imperfect CSIT
The space limitation and the channel acquisition prevent Massive MIMO from
being easily deployed in a practical setup. Motivated by current deployments of
LTE-Advanced, the use of multi-polarized antennas can be an efficient solution
to address the space constraint. Furthermore, the dual-structured precoding, in
which a preprocessing based on the spatial correlation and a subsequent linear
precoding based on the short-term channel state information at the transmitter
(CSIT) are concatenated, can reduce the feedback overhead efficiently. By
grouping and preprocessing spatially correlated mobile stations (MSs), the
dimension of the precoding signal space is reduced and the corresponding
short-term CSIT dimension is reduced. In this paper, to reduce the feedback
overhead further, we propose a dual-structured multi-user linear precoding, in
which the subgrouping method based on co-polarization is additionally applied
to the spatially grouped MSs in the preprocessing stage. Furthermore, under
imperfect CSIT, the proposed scheme is asymptotically analyzed based on random
matrix theory. By investigating the behavior of the asymptotic performance, we
also propose a new dual-structured precoding in which the precoding mode is
switched between two dual-structured precoding strategies with 1) the
preprocessing based only on the spatial correlation and 2) the preprocessing
based on both the spatial correlation and polarization. Finally, we extend it
to 3D dual-structured precoding.Comment: accepted to IEEE Transactions on Wireless Communication
On the Throughput of Large-but-Finite MIMO Networks using Schedulers
This paper studies the sum throughput of the {multi-user}
multiple-input-single-output (MISO) networks in the cases with large but finite
number of transmit antennas and users. Considering continuous and bursty
communication scenarios with different users' data request probabilities, we
derive quasi-closed-form expressions for the maximum achievable throughput of
the networks using optimal schedulers. The results are obtained in various
cases with different levels of interference cancellation. Also, we develop an
efficient scheduling scheme using genetic algorithms (GAs), and evaluate the
effect of different parameters, such as channel/precoding models, number of
antennas/users, scheduling costs and power amplifiers' efficiency, on the
system performance. Finally, we use the recent results on the achievable rates
of finite block-length codes to analyze the system performance in the cases
with short packets. As demonstrated, the proposed GA-based scheduler reaches
(almost) the same throughput as in the exhaustive search-based optimal
scheduler, with substantially less implementation complexity. Moreover, the
power amplifiers' inefficiency and the scheduling delay affect the performance
of the scheduling-based systems significantly
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