422 research outputs found
A Rate-Splitting Approach To Robust Multiuser MISO Transmission
For multiuser MISO systems with bounded uncertainties in the Channel State
Information (CSI), we consider two classical robust design problems: maximizing
the minimum rate subject to a transmit power constraint, and power minimization
under a rate constraint. Contrary to conventional strategies, we propose a
Rate-Splitting (RS) strategy where each message is divided into two parts, a
common part and a private part. All common parts are packed into one super
common message encoded using a shared codebook and decoded by all users, while
private parts are independently encoded and retrieved by their corresponding
users. We prove that RS-based designs achieve higher max-min Degrees of Freedom
(DoF) compared to conventional designs (NoRS) for uncertainty regions that
scale with SNR. For the special case of non-scaling uncertainty regions, RS
contrasts with NoRS and achieves a non-saturating max-min rate. In the power
minimization problem, RS is shown to combat the feasibility problem arising
from multiuser interference in NoRS. A robust design of precoders for RS is
proposed, and performance gains over NoRS are demonstrated through simulations.Comment: To appear in ICASSP 201
Robust Monotonic Optimization Framework for Multicell MISO Systems
The performance of multiuser systems is both difficult to measure fairly and
to optimize. Most resource allocation problems are non-convex and NP-hard, even
under simplifying assumptions such as perfect channel knowledge, homogeneous
channel properties among users, and simple power constraints. We establish a
general optimization framework that systematically solves these problems to
global optimality. The proposed branch-reduce-and-bound (BRB) algorithm handles
general multicell downlink systems with single-antenna users, multiantenna
transmitters, arbitrary quadratic power constraints, and robustness to channel
uncertainty. A robust fairness-profile optimization (RFO) problem is solved at
each iteration, which is a quasi-convex problem and a novel generalization of
max-min fairness. The BRB algorithm is computationally costly, but it shows
better convergence than the previously proposed outer polyblock approximation
algorithm. Our framework is suitable for computing benchmarks in general
multicell systems with or without channel uncertainty. We illustrate this by
deriving and evaluating a zero-forcing solution to the general problem.Comment: Published in IEEE Transactions on Signal Processing, 16 pages, 9
figures, 2 table
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