185 research outputs found
Maximizing the Sum Rate in Cellular Networks Using Multi-Convex Optimization
In this paper, we propose a novel algorithm to maximize the sum rate in
interference-limited scenarios where each user decodes its own message with the
presence of unknown interferences and noise considering the
signal-to-interference-plus-noise-ratio. It is known that the problem of
adapting the transmit and receive filters of the users to maximize the sum rate
with a sum transmit power constraint is non-convex. Our novel approach is to
formulate the sum rate maximization problem as an equivalent multi-convex
optimization problem by adding two sets of auxiliary variables. An iterative
algorithm which alternatingly adjusts the system variables and the auxiliary
variables is proposed to solve the multi-convex optimization problem. The
proposed algorithm is applied to a downlink cellular scenario consisting of
several cells each of which contains a base station serving several mobile
stations. We examine the two cases, with or without several half-duplex
amplify-and-forward relays assisting the transmission. A sum power constraint
at the base stations and a sum power constraint at the relays are assumed.
Finally, we show that the proposed multi-convex formulation of the sum rate
maximization problem is applicable to many other wireless systems in which the
estimated data symbols are multi-affine functions of the system variables.Comment: 24 pages, 5 figure
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
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