MAPEL: Achieving global optimality for a non-convex wireless power control problem

• Published in: IEEE transactions on wireless communications 2009-03, Vol.8 (3), p.1553-1563
• Main Authors: Qian, Li Ping, Zhang, Ying Jun, Huang, Jianwei
• Format: Article
• Language: English
• Subjects: Algorithm design and analysis; Algorithms; Applied sciences; Approximation; Convex analysis; Exact sciences and technology; global optimization; Interference; Mathematical analysis; Maximization; multiplicative linear fractional programming; Mutual coupling; non-convex optimization; Optimization; Performance evaluation; Power control; Quality of service; Signal to noise ratio; Studies; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Throughput; Transmission and modulation (techniques and equipments); Tuning; Wireless ad hoc networks; Wireless communication; Wireless networks
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2009.080649

Achieving weighted throughput maximization (WTM) through power control has been a long standing open problem in interference-limited wireless networks. The complicated coupling between the mutual interferences of links gives rise to a non-convex optimization problem. Previous work has considered the WTM problem in the high signal to interference-and-noise ratio (SINR) regime, where the problem can be approximated and transformed into a convex optimization problem through proper change of variables. In the general SINR regime, however, the approximation and transformation approach does not work. This paper proposes an algorithm, MAPEL, which globally converges to a global optimal solution of the WTM problem in the general SINR regime. The MAPEL algorithm is designed based on three key observations of the WTM problem: (1) the objective function is monotonically increasing in SINR, (2) the objective function can be transformed into a product of exponentiated linear fraction functions, and (3) the feasible set of the equivalent transformed problem is always ldquonormalrdquo, although not necessarily convex. The MAPEL algorithm finds the desired optimal power control solution by constructing a series of polyblocks that approximate the feasible SINR region in an increasing precision. Furthermore, by tuning the approximation factor in MAPEL, we could engineer a desirable tradeoff between optimality and convergence time. MAPEL provides an important benchmark for performance evaluation of other heuristic algorithms targeting the same problem. With the help of MAPEL, we evaluate the performance of several existing algorithms through extensive simulations.