3,037 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
Artificial Noise-Aided Biobjective Transmitter Optimization for Service Integration in Multi-User MIMO Gaussian Broadcast Channel
This paper considers an artificial noise (AN)-aided transmit design for
multi-user MIMO systems with integrated services. Specifically, two sorts of
service messages are combined and served simultaneously: one multicast message
intended for all receivers and one confidential message intended for only one
receiver and required to be perfectly secure from other unauthorized receivers.
Our interest lies in the joint design of input covariances of the multicast
message, confidential message and artificial noise (AN), such that the
achievable secrecy rate and multicast rate are simultaneously maximized. This
problem is identified as a secrecy rate region maximization (SRRM) problem in
the context of physical-layer service integration. Since this bi-objective
optimization problem is inherently complex to solve, we put forward two
different scalarization methods to convert it into a scalar optimization
problem. First, we propose to prefix the multicast rate as a constant, and
accordingly, the primal biobjective problem is converted into a secrecy rate
maximization (SRM) problem with quality of multicast service (QoMS) constraint.
By varying the constant, we can obtain different Pareto optimal points. The
resulting SRM problem can be iteratively solved via a provably convergent
difference-of-concave (DC) algorithm. In the second method, we aim to maximize
the weighted sum of the secrecy rate and the multicast rate. Through varying
the weighted vector, one can also obtain different Pareto optimal points. We
show that this weighted sum rate maximization (WSRM) problem can be recast into
a primal decomposable form, which is amenable to alternating optimization (AO).
Then we compare these two scalarization methods in terms of their overall
performance and computational complexity via theoretical analysis as well as
numerical simulation, based on which new insights can be drawn.Comment: 14 pages, 5 figure
Wireless MIMO Switching: Weighted Sum Mean Square Error and Sum Rate Optimization
This paper addresses joint transceiver and relay design for a wireless
multiple-input-multiple-output (MIMO) switching scheme that enables data
exchange among multiple users. Here, a multi-antenna relay linearly precodes
the received (uplink) signals from multiple users before forwarding the signal
in the downlink, where the purpose of precoding is to let each user receive its
desired signal with interference from other users suppressed. The problem of
optimizing the precoder based on various design criteria is typically
non-convex and difficult to solve. The main contribution of this paper is a
unified approach to solve the weighted sum mean square error (MSE) minimization
and weighted sum rate maximization problems in MIMO switching. Specifically, an
iterative algorithm is proposed for jointly optimizing the relay's precoder and
the users' receive filters to minimize the weighted sum MSE. It is also shown
that the weighted sum rate maximization problem can be reformulated as an
iterated weighted sum MSE minimization problem and can therefore be solved
similarly to the case of weighted sum MSE minimization. With properly chosen
initial values, the proposed iterative algorithms are asymptotically optimal in
both high and low signal-to-noise ratio (SNR) regimes for MIMO switching,
either with or without self-interference cancellation (a.k.a., physical-layer
network coding). Numerical results show that the optimized MIMO switching
scheme based on the proposed algorithms significantly outperforms existing
approaches in the literature.Comment: This manuscript is under 2nd review of IEEE Transactions on
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