11,385 research outputs found
Distributed Newton-type algorithms for network resource allocation
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 99-101).Most of today's communication networks are large-scale and comprise of agents with local information and heterogeneous preferences, making centralized control and coordination impractical. This motivated much interest in developing and studying distributed algorithms for network resource allocation problems, such as Internet routing, data collection and processing in sensor networks, and cross-layer communication network design. Existing works on network resource allocation problems rely on using dual decomposition and first-order (gradient or subgradient) methods, which involve simple computations and can be implemented in a distributed manner, yet suffer from slow rate of convergence. Second-order methods are faster, but their direct implementation requires computation intensive matrix inversion operations, which couple information across the network, hence cannot be implemented in a decentralized way. This thesis develops and analyzes Newton-type (second-order) distributed methods for network resource allocation problems. In particular, we focus on two general formulations: Network Utility Maximization (NUM), and network flow cost minimization problems. For NUM problems, we develop a distributed Newton-type fast converging algorithm using the properties of self-concordant utility functions. Our algorithm utilizes novel matrix splitting techniques, which enable both primal and dual Newton steps to be computed using iterative schemes in a decentralized manner with limited information exchange. Moreover, the step-size used in our method can be obtained via an iterative consensus-based averaging scheme. We show that even when the Newton direction and the step-size in our method are computed within some error (due to finite truncation of the iterative schemes), the resulting objective function value still converges superlinearly to an explicitly characterized error neighborhood. Simulation results demonstrate significant convergence rate improvement of our algorithm relative to the existing subgradient methods based on dual decomposition. The second part of the thesis presents a distributed approach based on a Newtontype method for solving network flow cost minimization problems. The key component of our method is to represent the dual Newton direction as the limit of an iterative procedure involving the graph Laplacian, which can be implemented based only on local information. Using standard Lipschitz conditions, we provide analysis for the convergence properties of our algorithm and show that the method converges superlinearly to an explicitly characterized error neighborhood, even when the iterative schemes used for computing the Newton direction and the stepsize are truncated. We also present some simulation results to illustrate the significant performance gains of this method over the subgradient methods currently used.by Ermin Wei.S.M
Resource Allocation for Secure Communication in Systems with Wireless Information and Power Transfer
This paper considers secure communication in a multiuser multiple-input
single-output (MISO) downlink system with simultaneous wireless information and
power transfer. We study the design of resource allocation algorithms
minimizing the total transmit power for the case when the receivers are able to
harvest energy from the radio frequency. In particular, the algorithm design is
formulated as a non-convex optimization problem which takes into account
artificial noise generation to combat potential eavesdroppers, a minimum
required signal-to-interference-plus-noise ratio (SINR) at the desired
receiver, maximum tolerable SINRs at the potential eavesdroppers, and a minimum
required power delivered to the receivers. We adopt a semidefinite programming
(SDP) relaxation approach to obtain an upper bound solution for the considered
problem. The tightness of the upper bound is revealed by examining a sufficient
condition for the global optimal solution. Inspired by the sufficient
condition, we propose two suboptimal resource allocation schemes enhancing
secure communication and facilitating efficient energy harvesting. Simulation
results demonstrate a close-to-optimal performance achieved by the proposed
suboptimal schemes and significant transmit power savings by optimization of
the artificial noise generation.Comment: 7 pages, 5 figures, and 1 table. Submitted for possible conference
publicatio
Energy Harvesting for Secure OFDMA Systems
Energy harvesting and physical-layer security in wireless networks are of
great significance. In this paper, we study the simultaneous wireless
information and power transfer (SWIPT) in downlink orthogonal
frequency-division multiple access (OFDMA) systems, where each user applies
power splitting to coordinate the energy harvesting and information decoding
processes while secrecy information requirement is guaranteed. The problem is
formulated to maximize the aggregate harvested power at the users while
satisfying secrecy rate requirements of all users by subcarrier allocation and
the optimal power splitting ratio selection. Due to the NP-hardness of the
problem, we propose an efficient iterative algorithm. The numerical results
show that the proposed method outperforms conventional methods.Comment: Accepted by WCSP 201
Power Efficient and Secure Multiuser Communication Systems with Wireless Information and Power Transfer
In this paper, we study resource allocation algorithm design for power
efficient secure communication with simultaneous wireless information and power
transfer (WIPT) in multiuser communication systems. In particular, we focus on
power splitting receivers which are able to harvest energy and decode
information from the received signals. The considered problem is modeled as an
optimization problem which takes into account a minimum required
signal-to-interference-plus-noise ratio (SINR) at multiple desired receivers, a
maximum tolerable data rate at multiple multi-antenna potential eavesdroppers,
and a minimum required power delivered to the receivers. The proposed problem
formulation facilitates the dual use of artificial noise in providing efficient
energy transfer and guaranteeing secure communication. We aim at minimizing the
total transmit power by jointly optimizing transmit beamforming vectors, power
splitting ratios at the desired receivers, and the covariance of the artificial
noise. The resulting non-convex optimization problem is transformed into a
semidefinite programming (SDP) and solved by SDP relaxation. We show that the
adopted SDP relaxation is tight and achieves the global optimum of the original
problem. Simulation results illustrate the significant power saving obtained by
the proposed optimal algorithm compared to suboptimal baseline schemes.Comment: Accepted for presentation at the IEEE International Conference on
Communications (ICC), Sydney, Australia, 201
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