1,677 research outputs found
Robust Transmission in Downlink Multiuser MISO Systems: A Rate-Splitting Approach
We consider a downlink multiuser MISO system with bounded errors in the
Channel State Information at the Transmitter (CSIT). We first look at the
robust design problem of achieving max-min fairness amongst users (in the
worst-case sense). Contrary to the conventional approach adopted in literature,
we propose a rather unorthodox design based on a Rate-Splitting (RS) strategy.
Each user's message is split into two parts, a common part and a private part.
All common parts are packed into one super common message encoded using a
public codebook, while private parts are independently encoded. The resulting
symbol streams are linearly precoded and simultaneously transmitted, and each
receiver retrieves its intended message by decoding both the common stream and
its corresponding private stream. For CSIT uncertainty regions that scale with
SNR (e.g. by scaling the number of feedback bits), we prove that a RS-based
design achieves higher max-min (symmetric) Degrees of Freedom (DoF) compared to
conventional designs (NoRS). For the special case of non-scaling CSIT (e.g.
fixed number of feedback bits), and contrary to NoRS, RS can achieve a
non-saturating max-min rate. We propose a robust algorithm based on the
cutting-set method coupled with the Weighted Minimum Mean Square Error (WMMSE)
approach, and we demonstrate its performance gains over state-of-the art
designs. Finally, we extend the RS strategy to address the Quality of Service
(QoS) constrained power minimization problem, and we demonstrate significant
gains over NoRS-based designs.Comment: Accepted for publication in IEEE Transactions on Signal Processin
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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