DMCA
Power control by geometric programming (2005)
Cached
Download Links
- [www.princeton.edu]
- [www.princeton.edu]
- [www.cs.cityu.edu.hk]
- DBLP
Other Repositories/Bibliography
| Venue: | IEEE Trans. on Wireless Commun |
| Citations: | 130 - 16 self |
BibTeX
@ARTICLE{Chiang05powercontrol,
author = {Mung Chiang and Chee Wei Tan and Daniel P. Palomar and David Julian},
title = {Power control by geometric programming},
journal = {IEEE Trans. on Wireless Commun},
year = {2005},
volume = {5},
pages = {289--313}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Abstract — In wireless cellular or ad hoc networks where Quality of Service (QoS) is interference-limited, a variety of power control problems can be formulated as nonlinear optimization with a system-wide objective, e.g., maximizing the total system throughput or the worst user throughput, subject to QoS constraints from individual users, e.g., on data rate, delay, and outage probability. We show that in the high Signal-to-Interference Ratios (SIR) regime, these nonlinear and apparently difficult, nonconvex optimization problems can be transformed into convex optimization problems in the form of geometric programming; hence they can be very efficiently solved for global optimality even with a large number of users. In the medium to low SIR regime, some of these constrained nonlinear optimization of power control cannot be turned into tractable convex formulations, but a heuristic can be used to compute in most cases the optimal solution by solving a series of geometric programs through the approach of successive convex approximation. While efficient and robust algorithms have been extensively studied for centralized solutions of geometric programs, distributed algorithms have not been explored before. We present a systematic method of distributed algorithms for power control that is geometric-programming-based. These techniques for power control, together with their implications to admission control and pricing in wireless networks, are illustrated through several numerical examples. Index Terms — Convex optimization, CDMA power control, Distributed algorithms. I.
Keyphrases
power control geometric programming distributed algorithm geometric program systematic method low sir regime total system nonlinear optimization global optimality cdma power control successive convex approximation qos constraint index term convex optimization ad hoc network optimal solution robust algorithm outage probability system-wide objective wireless network individual user several numerical example power control cannot centralized solution tractable convex formulation nonconvex optimization problem power control problem convex optimization problem data rate large number user throughput high signal-to-interference ratio
