57 research outputs found
On convergence of the auxiliary-vector beamformer with rank-deficient covariance matrices
The auxiliary-vector beamformer is an algorithm that generates iteratively a sequence of beamformers which,
under the assumption of a positive definite covariance matrix R, converges to the minimum variance distortionless response beamformer, without resorting to any matrix inversion. In the case where R is rank-deficient, e.g., when R is substituted for the sample covariance matrix and the number of snapshots is less than the number of array elements, the behavior of the AV beamformer is not known theoretically. In this letter, we derive a new convergence result and show that the AV beamformer weights converge when R is rank-deficient, and that the limit belongs to the class of reduced-rank beamformers
Adaptive beamforming for large arrays in satellite communications systems with dispersed coverage
Conventional multibeam satellite communications systems ensure coverage of wide areas through multiple fixed beams where all users inside a beam share the same bandwidth. We consider a new and more flexible system where each user is assigned his own beam, and the users can be very geographically dispersed. This is achieved through the use of a large direct radiating array (DRA) coupled with adaptive beamforming so as to reject interferences and to provide a maximal gain to the user of interest. New fast-converging adaptive beamforming algorithms are presented, which allow to obtain good signal to interference and noise ratio (SINR) with a number of snapshots much lower than the number of antennas in the array. These beamformers are evaluated on reference scenarios
Robust Adaptive Beamforming for General-Rank Signal Model with Positive Semi-Definite Constraint via POTDC
The robust adaptive beamforming (RAB) problem for general-rank signal model
with an additional positive semi-definite constraint is considered. Using the
principle of the worst-case performance optimization, such RAB problem leads to
a difference-of-convex functions (DC) optimization problem. The existing
approaches for solving the resulted non-convex DC problem are based on
approximations and find only suboptimal solutions. Here we solve the non-convex
DC problem rigorously and give arguments suggesting that the solution is
globally optimal. Particularly, we rewrite the problem as the minimization of a
one-dimensional optimal value function whose corresponding optimization problem
is non-convex. Then, the optimal value function is replaced with another
equivalent one, for which the corresponding optimization problem is convex. The
new one-dimensional optimal value function is minimized iteratively via
polynomial time DC (POTDC) algorithm.We show that our solution satisfies the
Karush-Kuhn-Tucker (KKT) optimality conditions and there is a strong evidence
that such solution is also globally optimal. Towards this conclusion, we
conjecture that the new optimal value function is a convex function. The new
RAB method shows superior performance compared to the other state-of-the-art
general-rank RAB methods.Comment: 29 pages, 7 figures, 2 tables, Submitted to IEEE Trans. Signal
Processing on August 201
IRS-Aided SWIPT: Joint Waveform, Active and Passive Beamforming Design Under Nonlinear Harvester Model
The performance of Simultaneous Wireless Information and Power Transfer
(SWIPT) is mainly constrained by the received Radio-Frequency (RF) signal
strength. To tackle this problem, we introduce an Intelligent Reflecting
Surface (IRS) to compensate the propagation loss and boost the transmission
efficiency. This paper proposes a novel IRS-aided SWIPT system where a
multi-carrier multi-antenna Access Point (AP) transmits information and power
simultaneously, with the assist of an IRS, to a single-antenna User Equipment
(UE) employing practical receiving schemes. Considering harvester nonlinearity,
we characterize the achievable Rate-Energy (R-E) region through a joint
optimization of waveform, active and passive beamforming based on the Channel
State Information at the Transmitter (CSIT). This problem is solved by the
Block Coordinate Descent (BCD) method, where we obtain the active precoder in
closed form, the passive beamforming by the Successive Convex Approximation
(SCA) approach, and the waveform amplitude by the Geometric Programming (GP)
technique. To facilitate practical implementation, we also propose a
low-complexity design based on closed-form adaptive waveform schemes.
Simulation results demonstrate the proposed algorithms bring considerable R-E
gains with robustness to CSIT inaccuracy and finite IRS states, and emphasize
the importance of modeling harvester nonlinearity in the IRS-aided SWIPT
design.Comment: Source code available at
https://github.com/SnowzTail/irs-aided-swipt-joint-waveform-active-and-passive-beamforming-design-under-nonlinear-harvester-mode
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