Distributed Wireless Utility Maximization via Fast Power Control

This thesis develops a new theoretical and algorithmic framework for practical distributed power control in wireless networks. It proposes and investigates fast optimal distributed power control algorithms applicable to LTE as well as cognitive radio. The proposed algorithms beat the well-known Qualcomm's load-spillage distributed power control algorithm in [HandeRanganChiangWu08] and the distributed weighted proportional SINR algorithm in [TanChiangSrikant11] in terms of both the optimality of the solution and the convergence speed.;Wireless network utility maximization via distributed power control is a classical and challenging issue that has attracted much research attention. The problem is often formulated as a system utility optimization problem under some transmit power constraints, where the system utility function is typically an increasing function of link signal-to-interference-plus-noise-ratio (SINR). This problem is complicated by the fact that these wireless devices may interfere with each other. In particular, the wireless devices are affected by each other's transmit power, and the transmit powers and interferences experienced by the devices are interwoven in a complex manner.;Despite that, there have been good centralized algorithms for solving the problem. "Decentralized" solutions, on the other hand, are a different story. In practice, decentralized algorithms in which the devices interact with each other in a loosely coupled manner to improve the network utility, are easier to deploy than centralized algorithms. However, the design of workable (and provably workable in the mathematical sense) solution is very challenging. Small neglects can lead to solutions that are invalid or non-convergent. For example, although both papers [HandeRanganChiangWu08] and [TanChiangSrikant11] claim their distributed algorithms to be optimal, we discover some experimental evidence suggesting that certain parts of these algorithms are not quite right. Oftentimes, the former fails to converge or converges extremely slowly, while the latter could diverge in the first few iterations.;To fix these glitches and to broaden the scope of the problem, we develop a new analytical and algorithmic framework with a more general formulation. With this framework, we can identify the sources of the defects and shortcomings of prior algorithms. We further construct an optimal distributed (sub)gradient projection algorithm with provably valid step size rules. Rigorous convergence proof and complexity analysis for our algorithm are given (note: convergence proof and complexity analysis were missing in [HandeRanganChiangWu08] and incorrect in [TanChiangSrikant11]). In some scenarios, our algorithm can be further accelerated to yield even better performance. Extensive simulation experiments confirm that our algorithms always outperform the prior algorithms, in terms of both optimality and efficiency. Specifically, simulation demonstrates at least 100 times faster convergence than the prior algorithms under certain scenarios.;In summary, this thesis solves the important SINR-based utility maximization problem and achieves significantly better results than existing work. It develops a new theoretical and algorithmic framework which completely addresses the difficult convergence and step-size issues. Going forward, we believe the foundation established in this work will open doors to other fast distributed wireless and mobile solutions to problems beyond the power control problem addressed here.