Convex Optimization
Analysis of iterative waterfilling algorithm for multiuser power control in digital subscriber lines
EURASIP Journal on Applied Signal Processing
The MIMO iterative waterfilling algorithm
IEEE Transactions on Signal Processing
Optimized signaling for MIMO interference systems with feedback
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Noncooperative Eigencoding for MIMO Ad hoc Networks
IEEE Transactions on Signal Processing
Cross-layer issues in MAC protocol design for MIMO ad hoc networks
IEEE Wireless Communications
Smart antenna techniques and their application to wireless ad hoc networks
IEEE Wireless Communications
IEEE Transactions on Wireless Communications
MIMO capacity with interference
IEEE Journal on Selected Areas in Communications
Convergence of Iterative Waterfilling Algorithm for Gaussian Interference Channels
IEEE Journal on Selected Areas in Communications
Competitive Design of Multiuser MIMO Systems Based on Game Theory: A Unified View
IEEE Journal on Selected Areas in Communications
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Precoding problem in multiple-input multiple-output (MIMO) ad hoc networks is addressed in this work. Firstly, we consider the problem of maximizing the system mutual information under a power constraint. In this context, we give a brief overview of the nonlinear optimization methods, and systematically we compare their performances. Then, we propose a fast and distributed algorithm based on the quasi-Newton methods to give a lower bound of the system capacity of MIMO ad hoc networks. Our proposed algorithm solves the maximization problem while diminishing the amount of information in the feedback links needed in the cooperative optimization. Secondly, we propose a different problem formulation, which consists in minimizing the total transmit power under a quality of signal constraint. This novel problem design is motivated since the packets are captured in ad hoc networks based on their signal-to-interference-plus-noise ratio (SINR) values. We convert the proposed formulation into semidefinite optimization problem, which can be solved numerically using interior point methods. Finally, an extensive set of simulations validates the proposed algorithms.