2,272 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 Unified Successive Pseudo-Convex Approximation Framework
In this paper, we propose a successive pseudo-convex approximation algorithm
to efficiently compute stationary points for a large class of possibly
nonconvex optimization problems. The stationary points are obtained by solving
a sequence of successively refined approximate problems, each of which is much
easier to solve than the original problem. To achieve convergence, the
approximate problem only needs to exhibit a weak form of convexity, namely,
pseudo-convexity. We show that the proposed framework not only includes as
special cases a number of existing methods, for example, the gradient method
and the Jacobi algorithm, but also leads to new algorithms which enjoy easier
implementation and faster convergence speed. We also propose a novel line
search method for nondifferentiable optimization problems, which is carried out
over a properly constructed differentiable function with the benefit of a
simplified implementation as compared to state-of-the-art line search
techniques that directly operate on the original nondifferentiable objective
function. The advantages of the proposed algorithm are shown, both
theoretically and numerically, by several example applications, namely, MIMO
broadcast channel capacity computation, energy efficiency maximization in
massive MIMO systems and LASSO in sparse signal recovery.Comment: submitted to IEEE Transactions on Signal Processing; original title:
A Novel Iterative Convex Approximation Metho
Sum Throughput Maximization in Multi-Tag Backscattering to Multiantenna Reader
Backscatter communication (BSC) is being realized as the core technology for
pervasive sustainable Internet-of-Things applications. However, owing to the
resource-limitations of passive tags, the efficient usage of multiple antennas
at the reader is essential for both downlink excitation and uplink detection.
This work targets at maximizing the achievable sum-backscattered-throughput by
jointly optimizing the transceiver (TRX) design at the reader and
backscattering coefficients (BC) at the tags. Since, this joint problem is
nonconvex, we first present individually-optimal designs for the TRX and BC. We
show that with precoder and {combiner} designs at the reader respectively
targeting downlink energy beamforming and uplink Wiener filtering operations,
the BC optimization at tags can be reduced to a binary power control problem.
Next, the asymptotically-optimal joint-TRX-BC designs are proposed for both low
and high signal-to-noise-ratio regimes. Based on these developments, an
iterative low-complexity algorithm is proposed to yield an efficient
jointly-suboptimal design. Thereafter, we discuss the practical utility of the
proposed designs to other application settings like wireless powered
communication networks and BSC with imperfect channel state information.
Lastly, selected numerical results, validating the analysis and shedding novel
insights, demonstrate that the proposed designs can yield significant
enhancement in the sum-backscattered throughput over existing benchmarks.Comment: 17 pages, 5 figures, accepted for publication in IEEE Transactions on
Communication
A Practical Cooperative Multicell MIMO-OFDMA Network Based on Rank Coordination
An important challenge of wireless networks is to boost the cell edge
performance and enable multi-stream transmissions to cell edge users.
Interference mitigation techniques relying on multiple antennas and
coordination among cells are nowadays heavily studied in the literature.
Typical strategies in OFDMA networks include coordinated scheduling,
beamforming and power control. In this paper, we propose a novel and practical
type of coordination for OFDMA downlink networks relying on multiple antennas
at the transmitter and the receiver. The transmission ranks, i.e.\ the number
of transmitted streams, and the user scheduling in all cells are jointly
optimized in order to maximize a network utility function accounting for
fairness among users. A distributed coordinated scheduler motivated by an
interference pricing mechanism and relying on a master-slave architecture is
introduced. The proposed scheme is operated based on the user report of a
recommended rank for the interfering cells accounting for the receiver
interference suppression capability. It incurs a very low feedback and backhaul
overhead and enables efficient link adaptation. It is moreover robust to
channel measurement errors and applicable to both open-loop and closed-loop
MIMO operations. A 20% cell edge performance gain over uncoordinated LTE-A
system is shown through system level simulations.Comment: IEEE Transactions or Wireless Communications, Accepted for
Publicatio
Dispensing with channel estimation: differentially modulated cooperative wireless communications
As a benefit of bypassing the potentially excessive complexity and yet inaccurate channel estimation, differentially encoded modulation in conjunction with low-complexity noncoherent detection constitutes a viable candidate for user-cooperative systems, where estimating all the links by the relays is unrealistic. In order to stimulate further research on differentially modulated cooperative systems, a number of fundamental challenges encountered in their practical implementations are addressed, including the time-variant-channel-induced performance erosion, flexible cooperative protocol designs, resource allocation as well as its high-spectral-efficiency transceiver design. Our investigations demonstrate the quantitative benefits of cooperative wireless networks both from a pure capacity perspective as well as from a practical system design perspective
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