2,824 research outputs found
Large-Scale MIMO versus Network MIMO for Multicell Interference Mitigation
This paper compares two important downlink multicell interference mitigation
techniques, namely, large-scale (LS) multiple-input multiple-output (MIMO) and
network MIMO. We consider a cooperative wireless cellular system operating in
time-division duplex (TDD) mode, wherein each cooperating cluster includes
base-stations (BSs), each equipped with multiple antennas and scheduling
single-antenna users. In an LS-MIMO system, each BS employs antennas not
only to serve its scheduled users, but also to null out interference caused to
the other users within the cooperating cluster using zero-forcing (ZF)
beamforming. In a network MIMO system, each BS is equipped with only
antennas, but interference cancellation is realized by data and channel state
information exchange over the backhaul links and joint downlink transmission
using ZF beamforming. Both systems are able to completely eliminate
intra-cluster interference and to provide the same number of spatial degrees of
freedom per user. Assuming the uplink-downlink channel reciprocity provided by
TDD, both systems are subject to identical channel acquisition overhead during
the uplink pilot transmission stage. Further, the available sum power at each
cluster is fixed and assumed to be equally distributed across the downlink
beams in both systems. Building upon the channel distribution functions and
using tools from stochastic ordering, this paper shows, however, that from a
performance point of view, users experience better quality of service, averaged
over small-scale fading, under an LS-MIMO system than a network MIMO system.
Numerical simulations for a multicell network reveal that this conclusion also
holds true with regularized ZF beamforming scheme. Hence, given the likely
lower cost of adding excess number of antennas at each BS, LS-MIMO could be the
preferred route toward interference mitigation in cellular networks.Comment: 13 pages, 7 figures; IEEE Journal of Selected Topics in Signal
Processing, Special Issue on Signal Processing for Large-Scale MIMO
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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