31,449 research outputs found
Worst-Case Robust Distributed Power Allocation in Shared Unlicensed Spectrum
This paper considers non-cooperative and fully-distributed power-allocation
for selfish transmitter-receiver pairs in shared unlicensed spectrum when
normalized-interference to each receiver is uncertain. We model each uncertain
parameter by the sum of its nominal (estimated) value and a bounded additive
error in a convex set, and show that the allocated power always converges to
its equilibrium, called robust Nash equilibrium (RNE). In the case of a bounded
and symmetric uncertainty region, we show that the power allocation problem for
each user is simplified, and can be solved in a distributed manner. We derive
the conditions for RNE's uniqueness and for convergence of the distributed
algorithm; and show that the total throughput (social utility) is less than
that at NE when RNE is unique. We also show that for multiple RNEs, the social
utility may be higher at a RNE as compared to that at the corresponding NE, and
demonstrate that this is caused by users' orthogonal utilization of bandwidth
at RNE. Simulations confirm our analysis
Robust Linear Precoder Design for Multi-cell Downlink Transmission
Coordinated information processing by the base stations of multi-cell
wireless networks enhances the overall quality of communication in the network.
Such coordinations for optimizing any desired network-wide quality of service
(QoS) necessitate the base stations to acquire and share some channel state
information (CSI). With perfect knowledge of channel states, the base stations
can adjust their transmissions for achieving a network-wise QoS optimality. In
practice, however, the CSI can be obtained only imperfectly. As a result, due
to the uncertainties involved, the network is not guaranteed to benefit from a
globally optimal QoS. Nevertheless, if the channel estimation perturbations are
confined within bounded regions, the QoS measure will also lie within a bounded
region. Therefore, by exploiting the notion of robustness in the worst-case
sense some worst-case QoS guarantees for the network can be asserted. We adopt
a popular model for noisy channel estimates that assumes that estimation noise
terms lie within known hyper-spheres. We aim to design linear transceivers that
optimize a worst-case QoS measure in downlink transmissions. In particular, we
focus on maximizing the worst-case weighted sum-rate of the network and the
minimum worst-case rate of the network. For obtaining such transceiver designs,
we offer several centralized (fully cooperative) and distributed (limited
cooperation) algorithms which entail different levels of complexity and
information exchange among the base stations.Comment: 38 Pages, 7 Figures, To appear in the IEEE Transactions on Signal
Processin
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