Energy optimal control for time-varying wireless networks

• Published in: IEEE transactions on information theory Vol. 52; no. 7; pp. 2915 - 2934
• Main Author: Neely, M.J.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.07.2006; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ad hoc networks; Adaptive control; Adaptive systems; Algorithms; Applied sciences; Channels; Communication channels; Data transmission; Delay; distributed algorithms; Electric rates; Equipments and installations; Exact sciences and technology; Fading; Information theory; mobile networks; Mobile radiocommunication systems; Networks; Optimal control; Optimization; Programmable control; queueing analysis; Radiocommunications; Statistics; stochastic optimization; Stochastic processes; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Teleprocessing networks. Isdn; Teletraffic; Throughput; Transmission and modulation (techniques and equipments); Valuation and optimization of characteristics. Simulation; Wireless communication; Wireless networks; Wireless sensor networks; Mobile radiocommunication; Flow rate regulation; Wireless telecommunication; Information rate; Teletraffic; Ad hoc networks; mobile networks; Stochastic method; Information transmission; Time variation; stochastic optimization; Queueing system; distributed algorithms; Optimal control; Traffic management; Optimal solution; Distributed algorithm; Traffic control; Data transmission network; Delay time; queueing analysis; Ad hoc network; Peak power
• ISSN: 0018-9448; 1557-9654
• DOI: 10.1109/TIT.2006.876219

We develop a dynamic control strategy for minimizing energy expenditure in a time-varying wireless network with adaptive transmission rates. The algorithm operates without knowledge of traffic rates or channel statistics, and yields average power that is arbitrarily close to the minimum possible value achieved by an algorithm optimized with complete knowledge of future events. Proximity to this optimal solution is shown to be inversely proportional to network delay. We then present a similar algorithm that solves the related problem of maximizing network throughput subject to peak and average power constraints. The techniques used in this paper are novel and establish a foundation for stochastic network optimization