157 research outputs found
Outage Efficient Strategies for Network MIMO with Partial CSIT
We consider a multi-cell MIMO downlink (network MIMO) where base-stations
(BS) with antennas connected to a central station (CS) serve
single-antenna user terminals (UT). Although many works have shown the
potential benefits of network MIMO, the conclusion critically depends on the
underlying assumptions such as channel state information at transmitters (CSIT)
and backhaul links. In this paper, by focusing on the impact of partial CSIT,
we propose an outage-efficient strategy. Namely, with side information of all
UT's messages and local CSIT, each BS applies zero-forcing (ZF) beamforming in
a distributed manner. For a small number of UTs (), the ZF beamforming
creates parallel MISO channels. Based on the statistical knowledge of these
parallel channels, the CS performs a robust power allocation that
simultaneously minimizes the outage probability of all UTs and achieves a
diversity gain of per UT. With a large number of UTs (),
we propose a so-called distributed diversity scheduling (DDS) scheme to select
a subset of \Ks UTs with limited backhaul communication. It is proved that
DDS achieves a diversity gain of B\frac{K}{\Ks}(M-\Ks+1), which scales
optimally with the number of cooperative BSs as well as UTs. Numerical
results confirm that even under realistic assumptions such as partial CSIT and
limited backhaul communications, network MIMO can offer high data rates with a
sufficient reliability to individual UTs.Comment: 26 pages, 8 figures, submitted to IEEE Trans. on Signal Processin
Power Allocation Games in Wireless Networks of Multi-antenna Terminals
We consider wireless networks that can be modeled by multiple access channels
in which all the terminals are equipped with multiple antennas. The propagation
model used to account for the effects of transmit and receive antenna
correlations is the unitary-invariant-unitary model, which is one of the most
general models available in the literature. In this context, we introduce and
analyze two resource allocation games. In both games, the mobile stations
selfishly choose their power allocation policies in order to maximize their
individual uplink transmission rates; in particular they can ignore some
specified centralized policies. In the first game considered, the base station
implements successive interference cancellation (SIC) and each mobile station
chooses his best space-time power allocation scheme; here, a coordination
mechanism is used to indicate to the users the order in which the receiver
applies SIC. In the second framework, the base station is assumed to implement
single-user decoding. For these two games a thorough analysis of the Nash
equilibrium is provided: the existence and uniqueness issues are addressed; the
corresponding power allocation policies are determined by exploiting random
matrix theory; the sum-rate efficiency of the equilibrium is studied
analytically in the low and high signal-to-noise ratio regimes and by
simulations in more typical scenarios. Simulations show that, in particular,
the sum-rate efficiency is high for the type of systems investigated and the
performance loss due to the use of the proposed suboptimum coordination
mechanism is very small
Adaptive Power Allocation and Control in Time-Varying Multi-Carrier MIMO Networks
In this paper, we examine the fundamental trade-off between radiated power
and achieved throughput in wireless multi-carrier, multiple-input and
multiple-output (MIMO) systems that vary with time in an unpredictable fashion
(e.g. due to changes in the wireless medium or the users' QoS requirements).
Contrary to the static/stationary channel regime, there is no optimal power
allocation profile to target (either static or in the mean), so the system's
users must adapt to changes in the environment "on the fly", without being able
to predict the system's evolution ahead of time. In this dynamic context, we
formulate the users' power/throughput trade-off as an online optimization
problem and we provide a matrix exponential learning algorithm that leads to no
regret - i.e. the proposed transmit policy is asymptotically optimal in
hindsight, irrespective of how the system evolves over time. Furthermore, we
also examine the robustness of the proposed algorithm under imperfect channel
state information (CSI) and we show that it retains its regret minimization
properties under very mild conditions on the measurement noise statistics. As a
result, users are able to track the evolution of their individually optimum
transmit profiles remarkably well, even under rapidly changing network
conditions and high uncertainty. Our theoretical analysis is validated by
extensive numerical simulations corresponding to a realistic network deployment
and providing further insights in the practical implementation aspects of the
proposed algorithm.Comment: 25 pages, 4 figure
Degrees of Freedom of Time Correlated MISO Broadcast Channel with Delayed CSIT
We consider the time correlated multiple-input single-output (MISO) broadcast
channel where the transmitter has imperfect knowledge on the current channel
state, in addition to delayed channel state information. By representing the
quality of the current channel state information as P^-{\alpha} for the
signal-to-noise ratio P and some constant {\alpha} \geq 0, we characterize the
optimal degree of freedom region for this more general two-user MISO broadcast
correlated channel. The essential ingredients of the proposed scheme lie in the
quantization and multicasting of the overheard interferences, while
broadcasting new private messages. Our proposed scheme smoothly bridges between
the scheme recently proposed by Maddah-Ali and Tse with no current state
information and a simple zero-forcing beamforming with perfect current state
information.Comment: revised and final version, to appear in IEEE transactions on
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