920 research outputs found
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
MISO Capacity with Per-Antenna Power Constraint
We establish in closed-form the capacity and the optimal signaling scheme for
a MISO channel with per-antenna power constraint. Two cases of channel state
information are considered: constant channel known at both the transmitter and
receiver, and Rayleigh fading channel known only at the receiver. For the first
case, the optimal signaling scheme is beamforming with the phases of the beam
weights matched to the phases of the channel coefficients, but the amplitudes
independent of the channel coefficients and dependent only on the constrained
powers. For the second case, the optimal scheme is to send independent signals
from the antennas with the constrained powers. In both cases, the capacity with
per-antenna power constraint is usually less than that with sum power
constraint.Comment: 7 pages double-column, 3 figure
Rate-Splitting for Max-Min Fair Multigroup Multicast Beamforming in Overloaded Systems
In this paper, we consider the problem of achieving max-min fairness amongst
multiple co-channel multicast groups through transmit beamforming. We
explicitly focus on overloaded scenarios in which the number of transmitting
antennas is insufficient to neutralize all inter-group interference. Such
scenarios are becoming increasingly relevant in the light of growing
low-latency content delivery demands, and also commonly appear in multibeam
satellite systems. We derive performance limits of classical beamforming
strategies using DoF analysis unveiling their limitations; for example, rates
saturate in overloaded scenarios due to inter-group interference. To tackle
interference, we propose a strategy based on degraded beamforming and
successive interference cancellation. While the degraded strategy resolves the
rate-saturation issue, this comes at a price of sacrificing all spatial
multiplexing gains. This motivates the development of a unifying strategy that
combines the benefits of the two previous strategies. We propose a beamforming
strategy based on rate-splitting (RS) which divides the messages intended to
each group into a degraded part and a designated part, and transmits a
superposition of both degraded and designated beamformed streams. The
superiority of the proposed strategy is demonstrated through DoF analysis.
Finally, we solve the RS beamforming design problem and demonstrate significant
performance gains through simulations
Signal waveform estimation in the presence of uncertainties about the steering vector
We consider the problem of signal waveform estimation using an array of sensors where there exist uncertainties about the steering vector of interest. This problem occurs in many situations, including arrays undergoing deformations, uncalibrated arrays, scattering around the source, etc. In this paper, we assume that some statistical knowledge about the variations of the steering vector is available. Within this framework, two approaches are proposed, depending on whether the signal is assumed to be deterministic or random. In the former case, the maximum likelihood (ML) estimator is derived. It is shown that it amounts to a beamforming-like processing of the observations, and an iterative algorithm is presented to obtain the ML weight vector. For random signals, a Bayesian approach is advocated, and we successively derive an (approximate) minimum mean-square error estimator and maximum a posteriori estimators. Numerical examples are provided to illustrate the performances of the estimators
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